System

Abstract

An object of a system according to an embodiment is to make it possible to efficiently search for a property that matches a desired condition at the time of moving and check the details.SOLUTION: A system includes a reception part, a condition analysis part, a property retrieval part, a property provision part, and a video provision part. The reception unit receives a condition input. The condition analysis unit analyzes the condition received by the reception unit. The property search unit searches for a property based on the condition analyzed by the condition analysis unit. The property provision unit provides the user with the property retrieved by the property retrieval unit. The video image providing unit provides a video image for the user to check details of the property provided by the property providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to efficiently search for properties that meet desired conditions and check the details when moving.

[0005] The system according to the embodiment aims to enable efficient searching for properties that meet desired conditions when moving and to enable confirmation of details. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a condition analysis unit, a property search unit, a property provision unit, and a video provision unit. The reception unit receives condition input. The condition analysis unit analyzes the conditions received by the reception unit. The property search unit searches for properties based on the conditions analyzed by the condition analysis unit. The property provision unit provides the user with the properties searched for by the property search unit. The video provision unit provides video for the user to check details of the property provided by the property provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently search for properties that meet their desired conditions when moving and check the details. [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) In an embodiment of the present invention, a property matching system allows users to input their desired conditions via text or chat, and AI analyzes the conditions to match them with optimal properties and provide a list of candidate properties. Furthermore, drones are installed in available rooms, allowing users to check the room's status online at any time. For example, if a user inputs conditions such as "within a 10-minute walk from the station, 2LDK, pet-friendly," the AI ​​will list properties that meet those conditions and provide them to the user. Users can also select a room they are interested in from the list and check the room's status online. The drone flies within the room and provides real-time video, allowing users to check the room's details and make a decision about whether to move in. This allows the property matching system to efficiently find properties that meet users' needs. Furthermore, by checking the room's status online, users can avoid the hassle of visiting the property in person.

[0029] A property matching system according to an embodiment includes a reception unit, a condition analysis unit, a property search unit, a property provision unit, and a video provision unit. The reception unit provides an interface for a user to input desired conditions. For example, the user can input the conditions in text or chat format. The condition analysis unit uses AI to analyze the conditions received by the reception unit. For example, it analyzes the user's desired conditions using natural language processing technology. The property search unit searches for optimal properties from a database based on the analyzed conditions. For example, it searches for a property that best matches the user's desired conditions. The property provision unit provides the searched properties to the user as a list. For example, it displays property information in list format so that the user can easily check it. The video provision unit provides video for checking details of a property selected by the user. For example, it uses a drone to fly around a room and provide video in real time. This allows the user to check the status of the room online. This allows the property matching system according to an embodiment to efficiently search for and provide properties based on the user's desired conditions.

[0030] The video providing unit can fly a drone around the room and provide video in real time. The video providing unit can, for example, fly a drone around the room and provide video in real time. For example, a drone can automatically fly around the room and capture images of the room using a camera. The video providing unit can also stream the video captured by the drone to a user's device in real time. For example, a user can check the room's status in real time using a smartphone or a PC. This allows the user to check the room's status online in real time. Some or all of the above-described processing in the video providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the video providing unit can efficiently capture the room's status using an AI algorithm for optimizing the drone's flight path.

[0031] The condition analysis unit can analyze the user's desired conditions using natural language processing. The condition analysis unit analyzes the user's desired conditions using, for example, natural language processing technology. For example, it uses morphological analysis to divide the text entered by the user into words and analyze the meaning. The condition analysis unit can also analyze the grammatical structure of the user's desired conditions using grammatical analysis. For example, it analyzes the structure of a sentence to clarify the relationship between the subject, predicate, object, etc. The condition analysis unit can also analyze the meaning of the user's desired conditions using semantic analysis. For example, it understands the meaning of the conditions entered by the user and extracts information for searching for an appropriate property. This allows the condition analysis unit to accurately analyze the user's desired conditions. Some or all of the above-mentioned processing in the condition analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the condition analysis unit can analyze the user's desired conditions using an AI model of natural language processing.

[0032] The property search unit can search for optimal properties from the database based on the analyzed conditions. The property search unit, for example, searches for optimal properties from the database based on the analyzed conditions. For example, it searches for properties that best match the user's desired conditions. The property search unit can also narrow down search results using filtering conditions. For example, it narrows down search results based on conditions such as price, location, and floor plan. The property search unit can also rank search results using a ranking algorithm. For example, it displays properties that best match the user's desired conditions at the top. This allows the property search unit to efficiently search for properties that meet the user's desired conditions. Some or all of the above-mentioned processing in the property search unit may be performed using, for example, AI, or may be performed without using AI. For example, the property search unit can use an AI algorithm to search for optimal properties based on the user's desired conditions.

[0033] The property providing unit can provide the searched properties to the user as a list. The property providing unit, for example, provides the searched properties to the user as a list. For example, it displays property information in list format so that the user can easily check it. The property providing unit can also provide detailed information about the property. For example, it displays detailed information such as photos, floor plans, locations, and prices of the property. The property providing unit can also provide a function that allows the user to compare properties. For example, it can display multiple properties side by side so that the user can easily compare them. This allows the property providing unit to check the search results in list format. Some or all of the above-mentioned processing in the property providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the property providing unit can use an AI algorithm to provide the user with an optimal property list.

[0034] The video providing unit can provide video to be streamed to a user's device. The video providing unit, for example, provides video to be streamed to a user's device. For example, video captured by a drone can be streamed to a user's smartphone or PC in real time. The video providing unit can also adjust the bit rate and delay time of the video. For example, the video providing unit can provide video at an optimal bit rate depending on the user's network environment. The video providing unit can also use technology to maintain video quality. For example, the video providing unit can provide high-quality video using video compression technology. This allows the user to check the status of the room through the device. Some or all of the above-mentioned processing in the video providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the video providing unit can optimize the bit rate and delay time of the video using an AI algorithm.

[0035] The video providing unit may include a flight path optimization unit for optimizing the flight path of the drone. The video providing unit may include, for example, a flight path optimization unit for optimizing the flight path of the drone. For example, the video providing unit may enable the drone to fly efficiently within a room and capture the overall situation. The flight path optimization unit may also calculate an optimal flight path using an AI algorithm. For example, the optimal flight path may be calculated taking into account the layout of the room and the location of obstacles. The flight path optimization unit may also calculate a path for minimizing the drone's battery consumption. For example, the video providing unit may fly the drone through the shortest distance to reduce battery consumption. This allows the video providing unit to efficiently check the situation in the room by optimizing the drone's flight path. Some or all of the above-described processing in the flight path optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the flight path optimization unit may calculate the optimal flight path using an AI algorithm.

[0036] The property providing unit may include an interface unit that allows users to easily operate the property on a smartphone or PC. The property providing unit may include, for example, an interface unit that allows users to easily operate the property on a smartphone or PC. For example, the interface unit may provide a graphical user interface (GUI) that allows users to operate intuitively. The interface unit may also improve operability through usability testing. For example, the interface design may be improved based on user feedback. The interface unit may also support multiple input methods, such as voice input and gesture operation. For example, the user may input conditions by voice or operate with gestures. This allows the property providing unit to easily operate property information on a smartphone or PC. Some or all of the above-described processing in the interface unit may be performed using, for example, AI, or may be performed without AI. For example, the interface unit may use an AI algorithm to analyze the user's operation history and provide an optimal interface.

[0037] The reception unit can analyze the user's past condition input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past condition input history and suggests the optimal input method. For example, the reception unit can automatically display conditions that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. The reception unit can also predict and suggest conditions to be used in a specific time period based on the user's past input history. For example, if the user tends to input specific conditions in a specific time period, the reception unit can suggest conditions appropriate for that time period. This allows the reception unit to suggest the optimal input method based on the user's past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to a generation AI and have the generation AI suggest the optimal input method.

[0038] The reception unit can customize the input fields based on the user's current living situation and areas of interest when entering conditions. For example, when entering conditions, the reception unit customizes the input fields based on the user's current living situation and areas of interest. For example, if the user has a pet, properties that allow pets can be displayed preferentially. Furthermore, if the user has children, the reception unit can also display properties near school districts or parks preferentially. For example, if the user owns a car, properties with parking spaces can be displayed preferentially. This allows the reception unit to provide input fields according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's living situation data into a generation AI and cause the generation AI to customize the input fields.

[0039] The reception unit can select the optimal input means according to the user's input method when inputting conditions. For example, the reception unit selects the optimal input means according to the user's input method when inputting conditions. For example, when the user inputs conditions by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs conditions by text, the reception unit can also support the input using text analysis technology. For example, when the user inputs conditions using an image, the reception unit supports the input using image recognition technology. This allows the reception unit to provide the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0040] The reception unit can prioritize input of highly relevant conditions in consideration of the user's geographical location information when inputting conditions. For example, the reception unit prioritizes input of highly relevant conditions in consideration of the user's geographical location information when inputting conditions. For example, the reception unit can prioritize displaying properties close to the user's current location if the user wants to live in a specific area. Furthermore, the reception unit can also prioritize displaying properties in that area if the user wants to shorten their commute time. For example, if the user wants to shorten their commute time, the reception unit can prioritize displaying properties close to their workplace. This allows the reception unit to prioritize input of highly relevant conditions based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize input of highly relevant conditions.

[0041] The reception unit can analyze the user's social media activity when conditions are input and suggest related conditions. For example, the reception unit can analyze the user's social media activity when conditions are input and suggest related conditions. For example, the reception unit can suggest related properties based on the location where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related conditions. For example, the reception unit can suggest related conditions based on the activity of the user's friends on social media. This allows the reception unit to suggest related conditions based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related conditions.

[0042] The reception unit can customize the input method by reflecting the user's past feedback when entering conditions. The reception unit, for example, customizes the input method by reflecting the user's past feedback when entering conditions. For example, the reception unit improves the input interface based on feedback provided by the user in the past. The reception unit can also avoid input methods that the user has been dissatisfied with in the past and suggest an input method that provides high satisfaction. For example, the reception unit analyzes the user's past feedback and provides an optimal input method. This allows the reception unit to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the input method.

[0043] The condition analysis unit can improve the accuracy of the analysis by referring to the user's past condition analysis results during condition analysis. For example, the condition analysis unit can improve the accuracy of the analysis by referring to the user's past condition analysis results during condition analysis. For example, the accuracy of the analysis can be improved based on condition analysis results previously provided by the user. The condition analysis unit can also analyze the user's past condition analysis results and propose an optimal analysis method. For example, the accuracy of the analysis can be improved by referring to the user's past condition analysis results. This allows the condition analysis unit to improve the accuracy of the analysis based on the user's past condition analysis results. Some or all of the above-described processing in the condition analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the condition analysis unit can input the user's past condition analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The condition analysis unit can apply different analysis algorithms depending on the user's input during condition analysis. For example, the condition analysis unit applies different analysis algorithms depending on the user's input during condition analysis. For example, if the user inputs detailed conditions, a detailed analysis algorithm is applied. Furthermore, the condition analysis unit can also apply a simple analysis algorithm when the user inputs simple conditions. For example, if the user inputs specific conditions, an analysis algorithm optimal for those conditions is applied. This allows the condition analysis unit to apply the optimal analysis algorithm depending on the user's input. Some or all of the above-described processing in the condition analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the condition analysis unit can input the user's input data to a generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0045] The condition analysis unit can adjust the level of detail of the analysis according to the user's level of expertise during condition analysis. The condition analysis unit, for example, adjusts the level of detail of the analysis according to the user's level of expertise during condition analysis. For example, if the user has expertise, it provides a detailed analysis result. The condition analysis unit can also provide a concise analysis result if the user does not have expertise. For example, it adjusts the level of detail of the analysis according to the user's level of expertise. This allows the condition analysis unit to provide an analysis result according to the user's level of expertise. Some or all of the above-mentioned processing in the condition analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the condition analysis unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0046] The condition analysis unit can determine the analysis priority based on the user's submission time during condition analysis. The condition analysis unit, for example, determines the analysis priority based on the user's submission time during condition analysis. For example, if the user is in a hurry, the analysis priority can be increased. The condition analysis unit can also perform analysis at normal priority when the user is relaxed. For example, the analysis priority can be adjusted based on the user's submission time. This allows the condition analysis unit to adjust the analysis priority based on the user's submission time. Some or all of the above-mentioned processing in the condition analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the condition analysis unit can input the user's submission time data into the generation AI and have the generation AI determine the analysis priority.

[0047] The condition analysis unit can adjust the order of analysis based on the user's relevance during condition analysis. The condition analysis unit, for example, adjusts the order of analysis based on the user's relevance during condition analysis. For example, if the user inputs an important condition, the condition is analyzed with priority. Furthermore, if the user inputs a detailed condition, the condition analysis unit can postpone analysis of the condition. For example, the analysis order is adjusted based on the user's relevance. This allows the condition analysis unit to adjust the order of analysis based on the user's relevance. Some or all of the above-described processing in the condition analysis unit may be performed using, or without, AI, for example. For example, the condition analysis unit can input user relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0048] The condition analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during condition analysis. For example, the condition analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during condition analysis. For example, if the user has technical expertise, the analysis result can be provided using technical terms. Furthermore, if the user does not have technical expertise, the condition analysis unit can also provide the analysis result in simple language. For example, the use of technical terms in the analysis can be adjusted according to the user's level of expertise. This allows the condition analysis unit to provide analysis results according to the user's level of expertise. Some or all of the above-described processing in the condition analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the condition analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0049] The property search unit can improve the accuracy of a property search by referring to the user's past search results. For example, the property search unit can improve the accuracy of a property search by referring to the user's past search results. For example, the search accuracy can be improved based on search results provided by the user in the past. The property search unit can also analyze the user's past search results and suggest an optimal search method. For example, the search accuracy can be improved by referring to the user's past search results. This allows the property search unit to improve the accuracy of a search based on the user's past search results. Some or all of the above-described processing in the property search unit may be performed using, for example, AI, or may be performed without using AI. For example, the property search unit can input the user's past search result data into the generation AI and cause the generation AI to improve the search accuracy.

[0050] The property search unit can apply different search algorithms depending on the user's input when searching for properties. For example, when searching for properties, the property search unit applies different search algorithms depending on the user's input. For example, if the user inputs detailed conditions, a detailed search algorithm is applied. Furthermore, if the user inputs simple conditions, the property search unit can also apply a simple search algorithm. For example, if the user inputs specific conditions, a search algorithm optimal for those conditions is applied. This allows the property search unit to apply the optimal search algorithm depending on the user's input. Some or all of the above-mentioned processing in the property search unit may be performed using AI, for example, or may be performed without using AI. For example, the property search unit can input the user's input data into a generation AI and have the generation AI apply the optimal search algorithm.

[0051] The property search unit can adjust the level of search detail according to the user's level of expertise when searching for properties. For example, the property search unit adjusts the level of search detail according to the user's level of expertise when searching for properties. For example, if the user has expertise, it provides detailed search results. The property search unit can also provide concise search results if the user does not have expertise. For example, it adjusts the level of search detail according to the user's level of expertise. This allows the property search unit to provide search results according to the user's level of expertise. Some or all of the above-mentioned processing in the property search unit may be performed using AI, for example, or may be performed without using AI. For example, the property search unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the level of search detail.

[0052] The property search unit can determine search priorities based on the time of user submission when searching for properties. For example, the property search unit determines search priorities based on the time of user submission when searching for properties. For example, if the user is in a hurry, the search priority can be increased. The property search unit can also perform searches at normal priority when the user is relaxed. For example, the search priority can be adjusted based on the time of user submission. This allows the property search unit to adjust search priorities based on the time of user submission. Some or all of the above-mentioned processing in the property search unit may be performed using AI, for example, or may be performed without using AI. For example, the property search unit can input user submission time data into a generation AI and have the generation AI determine the search priorities.

[0053] The property search unit can adjust the search order based on the user's relevance when searching for properties. The property search unit, for example, adjusts the search order based on the user's relevance when searching for properties. For example, if the user inputs important conditions, the property search unit prioritizes the search for those conditions. Furthermore, if the user inputs detailed conditions, the property search unit can also postpone the search for those conditions. For example, the search order is adjusted based on the user's relevance. This allows the property search unit to adjust the search order based on the user's relevance. Some or all of the above-described processing in the property search unit may be performed, for example, using AI, or may be performed without using AI. For example, the property search unit can input the user's relevance data into a generation AI and have the generation AI adjust the search order.

[0054] The property search unit can adjust the use of search terminology according to the user's level of expertise when searching for properties. For example, when searching for properties, the property search unit adjusts the use of search terminology according to the user's level of expertise. For example, if the user has expertise, it provides search results using specialized terminology. Furthermore, if the user does not have expertise, the property search unit can also provide search results in concise language. For example, it adjusts the use of search terminology according to the user's level of expertise. This allows the property search unit to provide search results according to the user's level of expertise. Some or all of the above-described processing in the property search unit may be performed using AI, for example, or may be performed without using AI. For example, the property search unit can input the user's expertise level data into the generation AI and have the generation AI adjust the use of terminology.

[0055] The property providing unit can improve the accuracy of the property provision by referring to the user's past property provision results when providing a property. For example, the property providing unit can improve the accuracy of the property provision by referring to the user's past property provision results when providing a property. For example, the property providing unit can improve the accuracy of the property provision based on the user's past property provision results. The property providing unit can also analyze the user's past property provision results and propose an optimal provision method. For example, the property providing unit can improve the accuracy of the property provision by referring to the user's past property provision results. This allows the property providing unit to improve the accuracy of the property provision based on the user's past property provision results. Some or all of the above-mentioned processing in the property providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the property providing unit can input the user's past property provision result data into the generation AI and cause the generation AI to improve the accuracy of the property provision.

[0056] The property providing unit can apply different provision algorithms depending on the user's input when providing a property. For example, the property providing unit applies different provision algorithms depending on the user's input when providing a property. For example, if the user inputs detailed conditions, it applies a detailed provision algorithm. Furthermore, if the user inputs simple conditions, the property providing unit can also apply a simple provision algorithm. For example, if the user inputs specific conditions, it applies a provision algorithm that is optimal for those conditions. This allows the property providing unit to apply the optimal provision algorithm depending on the user's input. Some or all of the above-mentioned processing in the property providing unit may be performed using AI, for example, or may be performed without using AI. For example, the property providing unit can input the user's input data to a generation AI and have the generation AI apply the optimal provision algorithm.

[0057] The property providing unit can adjust the level of detail of the property provided according to the user's level of expertise when providing the property. For example, the property providing unit adjusts the level of detail of the property provided according to the user's level of expertise when providing the property. For example, if the user has expertise, detailed property information is provided. The property providing unit can also provide concise property information if the user does not have expertise. For example, the level of detail of the property provided is adjusted according to the user's level of expertise. This allows the property providing unit to provide property information according to the user's level of expertise. Some or all of the above-mentioned processing in the property providing unit may be performed using AI, for example, or may be performed without using AI. For example, the property providing unit can input the user's expertise level data into the generation AI and have the generation AI adjust the level of detail of the property provided.

[0058] The property providing unit can determine the priority of provision based on the time of user submission when providing a property. For example, the property providing unit determines the priority of provision based on the time of user submission when providing a property. For example, if the user is in a hurry, the property providing unit can increase the priority of provision. Furthermore, if the user is relaxed, the property providing unit can provide the property at normal priority. For example, the property providing unit adjusts the priority of provision based on the time of user submission. This allows the property providing unit to adjust the priority of provision based on the time of user submission. Some or all of the above-mentioned processing in the property providing unit may be performed using AI, for example, or may be performed without using AI. For example, the property providing unit can input the user's submission time data into the generation AI and have the generation AI determine the priority of provision.

[0059] The property providing unit can adjust the order of properties provided based on the user's relevance when providing properties. For example, the property providing unit adjusts the order of properties provided based on the user's relevance when providing properties. For example, if the user inputs important conditions, those conditions are provided as a priority. Furthermore, if the user inputs detailed conditions, the property providing unit can also provide those conditions later. For example, the order of properties provided is adjusted based on the user's relevance. This allows the property providing unit to adjust the order of properties provided based on the user's relevance. Some or all of the above-described processing in the property providing unit may be performed using AI, for example, or may be performed without using AI. For example, the property providing unit can input user relevance data to a generation AI and cause the generation AI to adjust the order of properties provided.

[0060] The video providing unit can improve the accuracy of video provision by referring to the user's past video provision results when providing video. For example, the video providing unit improves the accuracy of video provision by referring to the user's past video provision results when providing video. For example, the accuracy of video provision is improved based on the user's past video provision results. The video providing unit can also analyze the user's past video provision results and propose an optimal video provision method. For example, the accuracy of video provision is improved by referring to the user's past video provision results. This allows the video providing unit to improve the accuracy of video provision based on the user's past video provision results. Some or all of the above-described processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit can input the user's past video provision result data into the generation AI and cause the generation AI to improve the accuracy of video provision.

[0061] The video providing unit can apply different provision algorithms depending on the user's input when providing video. For example, the video providing unit applies different provision algorithms depending on the user's input when providing video. For example, if the user inputs detailed conditions, the video providing unit applies a detailed provision algorithm. Furthermore, if the user inputs simple conditions, the video providing unit can also apply a simple provision algorithm. For example, if the user inputs specific conditions, the video providing unit applies the optimal provision algorithm for those conditions. This allows the video providing unit to apply the optimal provision algorithm depending on the user's input. Some or all of the above-mentioned processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit can input the user's input data to a generation AI and cause the generation AI to apply the optimal provision algorithm.

[0062] The video providing unit can adjust the level of detail of the video provided in accordance with the user's level of expertise when providing the video. For example, the video providing unit adjusts the level of detail of the video provided in accordance with the user's level of expertise when providing the video. For example, if the user has expertise, detailed video information is provided. Furthermore, the video providing unit can also provide concise video information if the user does not have expertise. For example, the level of detail of the video provided is adjusted in accordance with the user's level of expertise. This allows the video providing unit to provide video information in accordance with the user's level of expertise. Some or all of the above-described processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit can input the user's expertise level data to the generation AI and cause the generation AI to adjust the level of detail of the video provided.

[0063] The video providing unit can determine the priority of provision based on the time of user submission when providing a video. For example, the video providing unit determines the priority of provision based on the time of user submission when providing a video. For example, if the user is in a hurry, the video providing unit may increase the priority of provision. Furthermore, if the user is relaxed, the video providing unit may provide the video at a normal priority. For example, the priority of provision is adjusted based on the time of user submission. This allows the video providing unit to adjust the priority of provision based on the time of user submission. Some or all of the above-described processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit may input the user's submission time data into the generation AI and cause the generation AI to determine the priority of provision.

[0064] The video providing unit can adjust the order of provision of videos based on the user's relevance when providing videos. For example, the video providing unit adjusts the order of provision of videos based on the user's relevance when providing videos. For example, if the user inputs important conditions, those conditions are provided preferentially. Furthermore, if the user inputs detailed conditions, the video providing unit can also provide those conditions later. For example, the order of provision is adjusted based on the user's relevance. This allows the video providing unit to adjust the order of provision based on the user's relevance. Some or all of the above-described processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit can input user relevance data to a generation AI and cause the generation AI to adjust the order of provision.

[0065] The video providing unit can adjust the use of provided terminology according to the user's level of expertise when providing the video. For example, the video providing unit adjusts the use of provided terminology according to the user's level of expertise when providing the video. For example, if the user has specialized knowledge, the video information is provided using specialized terminology. Furthermore, if the user does not have specialized knowledge, the video providing unit can also provide the video information in simple language. For example, the use of provided terminology is adjusted according to the user's level of expertise. This allows the video providing unit to provide video information according to the user's level of expertise. Some or all of the above-described processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of terminology.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The property matching system can further include a hobby and preference analysis unit that suggests properties based on the user's hobbies and preferences. For example, if the user likes the outdoors, properties with lots of parks and nature nearby can be displayed preferentially. Also, if the user likes cooking, properties with well-equipped kitchens can be suggested. This makes it possible to suggest properties that are optimal for the user's hobbies and preferences.

[0068] The property matching system can further include a history analysis unit that analyzes the user's past search history and suggests the most suitable property. For example, properties with similar conditions can be preferentially displayed based on the conditions of properties the user has searched for in the past. The history analysis unit can also analyze the characteristics of properties in which the user has shown interest in the past and suggest properties that the user prefers. This makes it possible to suggest the most suitable property based on the user's past search history.

[0069] The property matching system can further include a lifestyle pattern learning unit that learns the user's lifestyle patterns and suggests optimal properties. For example, if the user goes to work early in the morning, properties with short commute times can be displayed preferentially. Also, if the user works late at night, properties in quiet environments can be suggested. This makes it possible to suggest optimal properties according to the user's lifestyle patterns.

[0070] The property matching system can further include a family structure analysis unit that suggests properties based on the user's family structure. For example, if the user has children, properties near school districts or parks can be displayed preferentially. Also, if the user has pets, properties that allow pets can be suggested. This allows the system to suggest properties that are optimal for the user's family structure.

[0071] The property matching system can further include a weather information analysis unit that takes into account the user's current weather information when proposing properties. For example, on a rainy day, properties close to the station can be displayed preferentially. On a sunny day, properties with balconies or gardens can be suggested. This allows the system to suggest optimal properties based on the user's current weather information.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit provides an interface for the user to input desired conditions. For example, the user can input the conditions in text or chat format. Step 2: The condition analysis unit uses AI to analyze the conditions received by the reception unit. For example, it uses natural language processing technology to analyze the user's desired conditions. Step 3: The property search unit searches the database for the most suitable property based on the analyzed conditions, for example, the property that best matches the user's desired conditions. Step 4: The property providing unit provides the searched properties to the user as a list. For example, the property information is displayed in a list format so that the user can easily check it. Step 5: The video provider provides video for the user to check the details of the property they selected. For example, a drone may be used to fly around the room and provide video in real time. This allows the user to check the status of the room online.

[0074] (Example 2) In an embodiment of the present invention, a property matching system allows users to input their desired conditions via text or chat, and AI analyzes the conditions to match them with optimal properties and provide a list of candidate properties. Furthermore, drones are installed in available rooms, allowing users to check the room's status online at any time. For example, if a user inputs conditions such as "within a 10-minute walk from the station, 2LDK, pet-friendly," the AI ​​will list properties that meet those conditions and provide them to the user. Users can also select a room they are interested in from the list and check the room's status online. The drone flies within the room and provides real-time video, allowing users to check the room's details and make a decision about whether to move in. This allows the property matching system to efficiently find properties that meet users' needs. Furthermore, by checking the room's status online, users can avoid the hassle of visiting the property in person.

[0075] A property matching system according to an embodiment includes a reception unit, a condition analysis unit, a property search unit, a property provision unit, and a video provision unit. The reception unit provides an interface for a user to input desired conditions. For example, the user can input the conditions in text or chat format. The condition analysis unit uses AI to analyze the conditions received by the reception unit. For example, it analyzes the user's desired conditions using natural language processing technology. The property search unit searches for optimal properties from a database based on the analyzed conditions. For example, it searches for a property that best matches the user's desired conditions. The property provision unit provides the searched properties to the user as a list. For example, it displays property information in list format so that the user can easily check it. The video provision unit provides video for checking details of a property selected by the user. For example, it uses a drone to fly around a room and provide video in real time. This allows the user to check the status of the room online. This allows the property matching system according to an embodiment to efficiently search for and provide properties based on the user's desired conditions.

[0076] The video providing unit can fly a drone around the room and provide video in real time. The video providing unit can, for example, fly a drone around the room and provide video in real time. For example, a drone can automatically fly around the room and capture images of the room using a camera. The video providing unit can also stream the video captured by the drone to a user's device in real time. For example, a user can check the room's status in real time using a smartphone or a PC. This allows the user to check the room's status online in real time. Some or all of the above-described processing in the video providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the video providing unit can efficiently capture the room's status using an AI algorithm for optimizing the drone's flight path.

[0077] The condition analysis unit can analyze the user's desired conditions using natural language processing. The condition analysis unit analyzes the user's desired conditions using, for example, natural language processing technology. For example, it uses morphological analysis to divide the text entered by the user into words and analyze the meaning. The condition analysis unit can also analyze the grammatical structure of the user's desired conditions using grammatical analysis. For example, it analyzes the structure of a sentence to clarify the relationship between the subject, predicate, object, etc. The condition analysis unit can also analyze the meaning of the user's desired conditions using semantic analysis. For example, it understands the meaning of the conditions entered by the user and extracts information for searching for an appropriate property. This allows the condition analysis unit to accurately analyze the user's desired conditions. Some or all of the above-mentioned processing in the condition analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the condition analysis unit can analyze the user's desired conditions using an AI model of natural language processing.

[0078] The property search unit can search for optimal properties from the database based on the analyzed conditions. The property search unit, for example, searches for optimal properties from the database based on the analyzed conditions. For example, it searches for properties that best match the user's desired conditions. The property search unit can also narrow down search results using filtering conditions. For example, it narrows down search results based on conditions such as price, location, and floor plan. The property search unit can also rank search results using a ranking algorithm. For example, it displays properties that best match the user's desired conditions at the top. This allows the property search unit to efficiently search for properties that meet the user's desired conditions. Some or all of the above-mentioned processing in the property search unit may be performed using, for example, AI, or may be performed without using AI. For example, the property search unit can use an AI algorithm to search for optimal properties based on the user's desired conditions.

[0079] The property providing unit can provide the searched properties to the user as a list. The property providing unit, for example, provides the searched properties to the user as a list. For example, it displays property information in list format so that the user can easily check it. The property providing unit can also provide detailed information about the property. For example, it displays detailed information such as photos, floor plans, locations, and prices of the property. The property providing unit can also provide a function that allows the user to compare properties. For example, it can display multiple properties side by side so that the user can easily compare them. This allows the property providing unit to check the search results in list format. Some or all of the above-mentioned processing in the property providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the property providing unit can use an AI algorithm to provide the user with an optimal property list.

[0080] The video providing unit can provide video to be streamed to a user's device. The video providing unit, for example, provides video to be streamed to a user's device. For example, video captured by a drone can be streamed to a user's smartphone or PC in real time. The video providing unit can also adjust the bit rate and delay time of the video. For example, the video providing unit can provide video at an optimal bit rate depending on the user's network environment. The video providing unit can also use technology to maintain video quality. For example, the video providing unit can provide high-quality video using video compression technology. This allows the user to check the status of the room through the device. Some or all of the above-mentioned processing in the video providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the video providing unit can optimize the bit rate and delay time of the video using an AI algorithm.

[0081] The video providing unit may include a flight path optimization unit for optimizing the flight path of the drone. The video providing unit may include, for example, a flight path optimization unit for optimizing the flight path of the drone. For example, the video providing unit may enable the drone to fly efficiently within a room and capture the overall situation. The flight path optimization unit may also calculate an optimal flight path using an AI algorithm. For example, the optimal flight path may be calculated taking into account the layout of the room and the location of obstacles. The flight path optimization unit may also calculate a path for minimizing the drone's battery consumption. For example, the video providing unit may fly the drone through the shortest distance to reduce battery consumption. This allows the video providing unit to efficiently check the situation in the room by optimizing the drone's flight path. Some or all of the above-described processing in the flight path optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the flight path optimization unit may calculate the optimal flight path using an AI algorithm.

[0082] The property providing unit may include an interface unit that allows users to easily operate the property on a smartphone or PC. The property providing unit may include, for example, an interface unit that allows users to easily operate the property on a smartphone or PC. For example, the interface unit may provide a graphical user interface (GUI) that allows users to operate intuitively. The interface unit may also improve operability through usability testing. For example, the interface design may be improved based on user feedback. The interface unit may also support multiple input methods, such as voice input and gesture operation. For example, the user may input conditions by voice or operate with gestures. This allows the property providing unit to easily operate property information on a smartphone or PC. Some or all of the above-described processing in the interface unit may be performed using, for example, AI, or may be performed without AI. For example, the interface unit may use an AI algorithm to analyze the user's operation history and provide an optimal interface.

[0083] The reception unit can estimate the user's emotion and adjust the condition input interface based on the estimated user emotion. For example, the reception unit can estimate the user's emotion and adjust the condition input interface based on the estimated user emotion. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to quickly enter conditions. This allows the reception unit to provide an optimal condition input interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0084] The reception unit can analyze the user's past condition input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past condition input history and suggests the optimal input method. For example, the reception unit can automatically display conditions that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. The reception unit can also predict and suggest conditions to be used in a specific time period based on the user's past input history. For example, if the user tends to input specific conditions in a specific time period, the reception unit can suggest conditions appropriate for that time period. This allows the reception unit to suggest the optimal input method based on the user's past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to a generation AI and have the generation AI suggest the optimal input method.

[0085] The reception unit can customize the input fields based on the user's current living situation and areas of interest when entering conditions. For example, when entering conditions, the reception unit customizes the input fields based on the user's current living situation and areas of interest. For example, if the user has a pet, properties that allow pets can be displayed preferentially. Furthermore, if the user has children, the reception unit can also display properties near school districts or parks preferentially. For example, if the user owns a car, properties with parking spaces can be displayed preferentially. This allows the reception unit to provide input fields according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's living situation data into a generation AI and cause the generation AI to customize the input fields.

[0086] The reception unit can select the optimal input means according to the user's input method when inputting conditions. For example, the reception unit selects the optimal input means according to the user's input method when inputting conditions. For example, when the user inputs conditions by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs conditions by text, the reception unit can also support the input using text analysis technology. For example, when the user inputs conditions using an image, the reception unit supports the input using image recognition technology. This allows the reception unit to provide the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0087] The reception unit can estimate the user's emotions and determine the priority of conditions to be input based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of conditions to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can cause the user to input important conditions first. The reception unit can also cause the user to input detailed conditions if the user is relaxed. For example, if the user is in a hurry, the reception unit can cause the user to input the minimum necessary conditions first. This allows the reception unit to determine the priority of conditions to be input based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0088] The reception unit can prioritize input of highly relevant conditions in consideration of the user's geographical location information when inputting conditions. For example, the reception unit prioritizes input of highly relevant conditions in consideration of the user's geographical location information when inputting conditions. For example, the reception unit can prioritize displaying properties close to the user's current location if the user wants to live in a specific area. Furthermore, the reception unit can also prioritize displaying properties in that area if the user wants to shorten their commute time. For example, if the user wants to shorten their commute time, the reception unit can prioritize displaying properties close to their workplace. This allows the reception unit to prioritize input of highly relevant conditions based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize input of highly relevant conditions.

[0089] The reception unit can analyze the user's social media activity when conditions are input and suggest related conditions. For example, the reception unit can analyze the user's social media activity when conditions are input and suggest related conditions. For example, the reception unit can suggest related properties based on the location where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related conditions. For example, the reception unit can suggest related conditions based on the activity of the user's friends on social media. This allows the reception unit to suggest related conditions based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related conditions.

[0090] The reception unit can customize the input method by reflecting the user's past feedback when entering conditions. The reception unit, for example, customizes the input method by reflecting the user's past feedback when entering conditions. For example, the reception unit improves the input interface based on feedback provided by the user in the past. The reception unit can also avoid input methods that the user has been dissatisfied with in the past and suggest an input method that provides high satisfaction. For example, the reception unit analyzes the user's past feedback and provides an optimal input method. This allows the reception unit to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the input method.

[0091] The condition analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, the condition analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is stressed, the accuracy of the analysis can be increased to provide results quickly. Furthermore, if the user is relaxed, the condition analysis unit can perform a detailed analysis to increase the accuracy. For example, if the user is in a hurry, the analysis can be minimized to provide results quickly. This allows the condition analysis unit to adjust the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the condition analysis unit can be performed using AI, or without AI. For example, the condition analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] The condition analysis unit can improve the accuracy of the analysis by referring to the user's past condition analysis results during condition analysis. For example, the condition analysis unit can improve the accuracy of the analysis by referring to the user's past condition analysis results during condition analysis. For example, the accuracy of the analysis can be improved based on condition analysis results previously provided by the user. The condition analysis unit can also analyze the user's past condition analysis results and propose an optimal analysis method. For example, the accuracy of the analysis can be improved by referring to the user's past condition analysis results. This allows the condition analysis unit to improve the accuracy of the analysis based on the user's past condition analysis results. Some or all of the above-described processing in the condition analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the condition analysis unit can input the user's past condition analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0093] The condition analysis unit can apply different analysis algorithms depending on the user's input during condition analysis. For example, the condition analysis unit applies different analysis algorithms depending on the user's input during condition analysis. For example, if the user inputs detailed conditions, a detailed analysis algorithm is applied. Furthermore, the condition analysis unit can also apply a simple analysis algorithm when the user inputs simple conditions. For example, if the user inputs specific conditions, an analysis algorithm optimal for those conditions is applied. This allows the condition analysis unit to apply the optimal analysis algorithm depending on the user's input. Some or all of the above-described processing in the condition analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the condition analysis unit can input the user's input data to a generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0094] The condition analysis unit can adjust the level of detail of the analysis according to the user's level of expertise during condition analysis. The condition analysis unit, for example, adjusts the level of detail of the analysis according to the user's level of expertise during condition analysis. For example, if the user has expertise, it provides a detailed analysis result. The condition analysis unit can also provide a concise analysis result if the user does not have expertise. For example, it adjusts the level of detail of the analysis according to the user's level of expertise. This allows the condition analysis unit to provide an analysis result according to the user's level of expertise. Some or all of the above-mentioned processing in the condition analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the condition analysis unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0095] The condition analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the condition analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the condition analysis unit can provide a display method including detailed information. For example, if the user is in a hurry, it can provide a display method that focuses on the main points. This allows the condition analysis unit to provide an optimal display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the condition analysis unit can be performed using, for example, an AI, or without an AI. For example, the condition analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0096] The condition analysis unit can determine the analysis priority based on the user's submission time during condition analysis. The condition analysis unit, for example, determines the analysis priority based on the user's submission time during condition analysis. For example, if the user is in a hurry, the analysis priority can be increased. The condition analysis unit can also perform analysis at normal priority when the user is relaxed. For example, the analysis priority can be adjusted based on the user's submission time. This allows the condition analysis unit to adjust the analysis priority based on the user's submission time. Some or all of the above-mentioned processing in the condition analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the condition analysis unit can input the user's submission time data into the generation AI and have the generation AI determine the analysis priority.

[0097] The condition analysis unit can adjust the order of analysis based on the user's relevance during condition analysis. The condition analysis unit, for example, adjusts the order of analysis based on the user's relevance during condition analysis. For example, if the user inputs an important condition, the condition is analyzed with priority. Furthermore, if the user inputs a detailed condition, the condition analysis unit can postpone analysis of the condition. For example, the analysis order is adjusted based on the user's relevance. This allows the condition analysis unit to adjust the order of analysis based on the user's relevance. Some or all of the above-described processing in the condition analysis unit may be performed using, or without, AI, for example. For example, the condition analysis unit can input user relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0098] The condition analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during condition analysis. For example, the condition analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during condition analysis. For example, if the user has technical expertise, the analysis result can be provided using technical terms. Furthermore, if the user does not have technical expertise, the condition analysis unit can also provide the analysis result in simple language. For example, the use of technical terms in the analysis can be adjusted according to the user's level of expertise. This allows the condition analysis unit to provide analysis results according to the user's level of expertise. Some or all of the above-described processing in the condition analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the condition analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0099] The property search unit can estimate the user's emotions and adjust the search accuracy based on the estimated user emotions. For example, the property search unit can estimate the user's emotions and adjust the search accuracy based on the estimated user emotions. For example, if the user is stressed, the search accuracy can be increased to quickly provide results. Furthermore, if the user is relaxed, the property search unit can perform a detailed search to increase accuracy. For example, if the user is in a hurry, the property search unit can perform a minimal search to quickly provide results. This allows the property search unit to provide optimal search accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the property search unit can be performed using AI, for example, or without AI. For example, the property search unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0100] The property search unit can improve the accuracy of a property search by referring to the user's past search results. For example, the property search unit can improve the accuracy of a property search by referring to the user's past search results. For example, the search accuracy can be improved based on search results provided by the user in the past. The property search unit can also analyze the user's past search results and suggest an optimal search method. For example, the search accuracy can be improved by referring to the user's past search results. This allows the property search unit to improve the accuracy of a search based on the user's past search results. Some or all of the above-described processing in the property search unit may be performed using, for example, AI, or may be performed without using AI. For example, the property search unit can input the user's past search result data into the generation AI and cause the generation AI to improve the search accuracy.

[0101] The property search unit can apply different search algorithms depending on the user's input when searching for properties. For example, when searching for properties, the property search unit applies different search algorithms depending on the user's input. For example, if the user inputs detailed conditions, a detailed search algorithm is applied. Furthermore, if the user inputs simple conditions, the property search unit can also apply a simple search algorithm. For example, if the user inputs specific conditions, a search algorithm optimal for those conditions is applied. This allows the property search unit to apply the optimal search algorithm depending on the user's input. Some or all of the above-mentioned processing in the property search unit may be performed using AI, for example, or may be performed without using AI. For example, the property search unit can input the user's input data into a generation AI and have the generation AI apply the optimal search algorithm.

[0102] The property search unit can adjust the level of search detail according to the user's level of expertise when searching for properties. For example, the property search unit adjusts the level of search detail according to the user's level of expertise when searching for properties. For example, if the user has expertise, it provides detailed search results. The property search unit can also provide concise search results if the user does not have expertise. For example, it adjusts the level of search detail according to the user's level of expertise. This allows the property search unit to provide search results according to the user's level of expertise. Some or all of the above-mentioned processing in the property search unit may be performed using AI, for example, or may be performed without using AI. For example, the property search unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the level of search detail.

[0103] The property search unit can estimate the user's emotions and adjust the display method of search results based on the estimated user emotions. For example, the property search unit can estimate the user's emotions and adjust the display method of search results based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the property search unit can provide a display method including detailed information. For example, if the user is in a hurry, it can provide a display method that focuses on the main points. This allows the property search unit to provide an optimal display method of search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the property search unit can be performed using AI, for example, or without AI. For example, the property search unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0104] The property search unit can determine search priorities based on the time of user submission when searching for properties. For example, the property search unit determines search priorities based on the time of user submission when searching for properties. For example, if the user is in a hurry, the search priority can be increased. The property search unit can also perform searches at normal priority when the user is relaxed. For example, the search priority can be adjusted based on the time of user submission. This allows the property search unit to adjust search priorities based on the time of user submission. Some or all of the above-mentioned processing in the property search unit may be performed using AI, for example, or may be performed without using AI. For example, the property search unit can input user submission time data into a generation AI and have the generation AI determine the search priorities.

[0105] The property search unit can adjust the search order based on the user's relevance when searching for properties. The property search unit, for example, adjusts the search order based on the user's relevance when searching for properties. For example, if the user inputs important conditions, the property search unit prioritizes the search for those conditions. Furthermore, if the user inputs detailed conditions, the property search unit can also postpone the search for those conditions. For example, the search order is adjusted based on the user's relevance. This allows the property search unit to adjust the search order based on the user's relevance. Some or all of the above-described processing in the property search unit may be performed, for example, using AI, or may be performed without using AI. For example, the property search unit can input the user's relevance data into a generation AI and have the generation AI adjust the search order.

[0106] The property search unit can adjust the use of search terminology according to the user's level of expertise when searching for properties. For example, when searching for properties, the property search unit adjusts the use of search terminology according to the user's level of expertise. For example, if the user has expertise, it provides search results using specialized terminology. Furthermore, if the user does not have expertise, the property search unit can also provide search results in concise language. For example, it adjusts the use of search terminology according to the user's level of expertise. This allows the property search unit to provide search results according to the user's level of expertise. Some or all of the above-described processing in the property search unit may be performed using AI, for example, or may be performed without using AI. For example, the property search unit can input the user's expertise level data into the generation AI and have the generation AI adjust the use of terminology.

[0107] The property providing unit can estimate the user's emotions and determine the priority of properties to provide based on the estimated user emotions. For example, the property providing unit can estimate the user's emotions and determine the priority of properties to provide based on the estimated user emotions. For example, if the user is feeling stressed, important properties can be provided preferentially. The property providing unit can also provide detailed property information when the user is relaxed. For example, if the user is in a hurry, the property providing unit can provide the minimum amount of property information preferentially. This allows the property providing unit to provide optimal property provision priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the property providing unit may be performed using AI, or may be performed without AI. For example, the property providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0108] The property providing unit can improve the accuracy of the property provision by referring to the user's past property provision results when providing a property. For example, the property providing unit can improve the accuracy of the property provision by referring to the user's past property provision results when providing a property. For example, the property providing unit can improve the accuracy of the property provision based on the user's past property provision results. The property providing unit can also analyze the user's past property provision results and propose an optimal provision method. For example, the property providing unit can improve the accuracy of the property provision by referring to the user's past property provision results. This allows the property providing unit to improve the accuracy of the property provision based on the user's past property provision results. Some or all of the above-mentioned processing in the property providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the property providing unit can input the user's past property provision result data into the generation AI and cause the generation AI to improve the accuracy of the property provision.

[0109] The property providing unit can apply different provision algorithms depending on the user's input when providing a property. For example, the property providing unit applies different provision algorithms depending on the user's input when providing a property. For example, if the user inputs detailed conditions, it applies a detailed provision algorithm. Furthermore, if the user inputs simple conditions, the property providing unit can also apply a simple provision algorithm. For example, if the user inputs specific conditions, it applies a provision algorithm that is optimal for those conditions. This allows the property providing unit to apply the optimal provision algorithm depending on the user's input. Some or all of the above-mentioned processing in the property providing unit may be performed using AI, for example, or may be performed without using AI. For example, the property providing unit can input the user's input data to a generation AI and have the generation AI apply the optimal provision algorithm.

[0110] The property providing unit can adjust the level of detail of the property provided according to the user's level of expertise when providing the property. For example, the property providing unit adjusts the level of detail of the property provided according to the user's level of expertise when providing the property. For example, if the user has expertise, detailed property information is provided. The property providing unit can also provide concise property information if the user does not have expertise. For example, the level of detail of the property provided is adjusted according to the user's level of expertise. This allows the property providing unit to provide property information according to the user's level of expertise. Some or all of the above-mentioned processing in the property providing unit may be performed using AI, for example, or may be performed without using AI. For example, the property providing unit can input the user's expertise level data into the generation AI and have the generation AI adjust the level of detail of the property provided.

[0111] The property providing unit can estimate the user's emotions and adjust the display method of the property to be provided based on the estimated user's emotions. For example, the property providing unit can estimate the user's emotions and adjust the display method of the property to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the property providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the property providing unit can provide a display method including detailed information. For example, if the user is in a hurry, the property providing unit can provide a display method that focuses on the main points. This allows the property providing unit to provide an optimal property display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the property providing unit may be performed using AI, or may be performed without AI. For example, the property providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0112] The property providing unit can determine the priority of provision based on the time of user submission when providing a property. For example, the property providing unit determines the priority of provision based on the time of user submission when providing a property. For example, if the user is in a hurry, the property providing unit can increase the priority of provision. Furthermore, if the user is relaxed, the property providing unit can provide the property at normal priority. For example, the property providing unit adjusts the priority of provision based on the time of user submission. This allows the property providing unit to adjust the priority of provision based on the time of user submission. Some or all of the above-mentioned processing in the property providing unit may be performed using AI, for example, or may be performed without using AI. For example, the property providing unit can input the user's submission time data into the generation AI and have the generation AI determine the priority of provision.

[0113] The property providing unit can adjust the order of properties provided based on the user's relevance when providing properties. For example, the property providing unit adjusts the order of properties provided based on the user's relevance when providing properties. For example, if the user inputs important conditions, those conditions are provided as a priority. Furthermore, if the user inputs detailed conditions, the property providing unit can also provide those conditions later. For example, the order of properties provided is adjusted based on the user's relevance. This allows the property providing unit to adjust the order of properties provided based on the user's relevance. Some or all of the above-described processing in the property providing unit may be performed using AI, for example, or may be performed without using AI. For example, the property providing unit can input user relevance data to a generation AI and cause the generation AI to adjust the order of properties provided.

[0114] The video providing unit can estimate the user's emotions and adjust the video display method based on the estimated user emotions. For example, the video providing unit can estimate the user's emotions and adjust the video display method based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a simple, highly visible video display method. Furthermore, if the user is relaxed, the video providing unit can provide a video display method including detailed information. For example, if the user is in a hurry, it can provide a video display method that focuses on the main points. This allows the video providing unit to provide an optimal video display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the video providing unit can be performed using AI, for example, or without AI. For example, the video providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0115] The video providing unit can improve the accuracy of video provision by referring to the user's past video provision results when providing video. For example, the video providing unit improves the accuracy of video provision by referring to the user's past video provision results when providing video. For example, the accuracy of video provision is improved based on the user's past video provision results. The video providing unit can also analyze the user's past video provision results and propose an optimal video provision method. For example, the accuracy of video provision is improved by referring to the user's past video provision results. This allows the video providing unit to improve the accuracy of video provision based on the user's past video provision results. Some or all of the above-described processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit can input the user's past video provision result data into the generation AI and cause the generation AI to improve the accuracy of video provision.

[0116] The video providing unit can apply different provision algorithms depending on the user's input when providing video. For example, the video providing unit applies different provision algorithms depending on the user's input when providing video. For example, if the user inputs detailed conditions, the video providing unit applies a detailed provision algorithm. Furthermore, if the user inputs simple conditions, the video providing unit can also apply a simple provision algorithm. For example, if the user inputs specific conditions, the video providing unit applies the optimal provision algorithm for those conditions. This allows the video providing unit to apply the optimal provision algorithm depending on the user's input. Some or all of the above-mentioned processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit can input the user's input data to a generation AI and cause the generation AI to apply the optimal provision algorithm.

[0117] The video providing unit can adjust the level of detail of the video provided in accordance with the user's level of expertise when providing the video. For example, the video providing unit adjusts the level of detail of the video provided in accordance with the user's level of expertise when providing the video. For example, if the user has expertise, detailed video information is provided. Furthermore, the video providing unit can also provide concise video information if the user does not have expertise. For example, the level of detail of the video provided is adjusted in accordance with the user's level of expertise. This allows the video providing unit to provide video information in accordance with the user's level of expertise. Some or all of the above-described processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit can input the user's expertise level data to the generation AI and cause the generation AI to adjust the level of detail of the video provided.

[0118] The video providing unit can estimate the user's emotions and adjust the video display method based on the estimated user emotions. For example, the video providing unit can estimate the user's emotions and adjust the video display method based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a simple, highly visible video display method. Furthermore, if the user is relaxed, the video providing unit can provide a video display method including detailed information. For example, if the user is in a hurry, it can provide a video display method that focuses on the main points. This allows the video providing unit to provide an optimal video display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the video providing unit can be performed using AI, for example, or without AI. For example, the video providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0119] The video providing unit can determine the priority of provision based on the time of user submission when providing a video. For example, the video providing unit determines the priority of provision based on the time of user submission when providing a video. For example, if the user is in a hurry, the video providing unit may increase the priority of provision. Furthermore, if the user is relaxed, the video providing unit may provide the video at a normal priority. For example, the priority of provision is adjusted based on the time of user submission. This allows the video providing unit to adjust the priority of provision based on the time of user submission. Some or all of the above-described processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit may input the user's submission time data into the generation AI and cause the generation AI to determine the priority of provision.

[0120] The video providing unit can adjust the order of provision of videos based on the user's relevance when providing videos. For example, the video providing unit adjusts the order of provision of videos based on the user's relevance when providing videos. For example, if the user inputs important conditions, those conditions are provided preferentially. Furthermore, if the user inputs detailed conditions, the video providing unit can also provide those conditions later. For example, the order of provision is adjusted based on the user's relevance. This allows the video providing unit to adjust the order of provision based on the user's relevance. Some or all of the above-described processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit can input user relevance data to a generation AI and cause the generation AI to adjust the order of provision.

[0121] The video providing unit can adjust the use of provided terminology according to the user's level of expertise when providing the video. For example, the video providing unit adjusts the use of provided terminology according to the user's level of expertise when providing the video. For example, if the user has specialized knowledge, the video information is provided using specialized terminology. Furthermore, if the user does not have specialized knowledge, the video providing unit can also provide the video information in simple language. For example, the use of provided terminology is adjusted according to the user's level of expertise. This allows the video providing unit to provide video information according to the user's level of expertise. Some or all of the above-described processing in the video providing unit may be performed using AI, for example, or may be performed without using AI. For example, the video providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of terminology. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, condition analysis unit, property search unit, property provision unit, and video provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for a user to input conditions in text or chat format. For example, the condition analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the conditions accepted by the reception unit using AI. For example, the property search unit is implemented by the specific processing unit 290 of the data processing device 12 and searches the database 24 for optimal properties based on the analyzed conditions. For example, the property provision unit is implemented by the output device 40 of the smart device 14 and provides the user with a list of searched properties. For example, the video provision unit is implemented by the camera 42 and control unit 46A of the smart device 14 and flies a drone around a room to provide video in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, condition analysis unit, property search unit, property provision unit, and video provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for a user to input conditions in text or chat format. For example, the condition analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the conditions received by the reception unit using AI. For example, the property search unit is realized by the specific processing unit 290 of the data processing device 12 and searches the database 24 for optimal properties based on the analyzed conditions. For example, the property provision unit is realized by the speaker 240 of the smart glasses 214 and provides the user with a list of searched properties. For example, the video provision unit is realized by the camera 42 and control unit 46A of the smart glasses 214 and flies a drone around a room to provide video in real time. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, condition analysis unit, property search unit, property provision unit, and video provision unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for a user to input conditions in text or chat format. For example, the condition analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the conditions accepted by the reception unit using AI. For example, the property search unit is implemented by the specific processing unit 290 of the data processing device 12 and searches the database 24 for optimal properties based on the analyzed conditions. For example, the property provision unit is implemented by the display 343 of the headset terminal 314 and provides the user with a list of searched properties. For example, the video provision unit is implemented by the camera 42 and control unit 46A of the headset terminal 314 and flies a drone around a room to provide video in real time. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, condition analysis unit, property search unit, property provision unit, and video provision unit, described above, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for a user to input conditions in text or chat format. For example, the condition analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the conditions accepted by the reception unit using AI. For example, the property search unit is implemented by the specific processing unit 290 of the data processing device 12 and searches the database 24 for optimal properties based on the analyzed conditions. For example, the property provision unit is implemented by the speaker 240 of the robot 414 and provides the user with a list of searched properties. For example, the video provision unit is implemented by the camera 42 and control unit 46A of the robot 414 and flies a drone around a room to provide video in real time.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The property matching system can further include a health monitoring unit that monitors the user's health condition. For example, while the user is searching for a property, the health monitoring unit can measure the user's heart rate and stress level in real time, and if the user is feeling stressed, it can prioritize the display of properties that will help the user relax. In addition, the health monitoring unit can provide detailed property information when the user is relaxed. This makes it possible to provide the user with optimal property information based on their health condition.

[0124] The property matching system can further include a hobby and preference analysis unit that suggests properties based on the user's hobbies and preferences. For example, if the user likes the outdoors, properties with lots of parks and nature nearby can be displayed preferentially. Also, if the user likes cooking, properties with well-equipped kitchens can be suggested. This makes it possible to suggest properties that are optimal for the user's hobbies and preferences.

[0125] The property matching system can further include a history analysis unit that analyzes the user's past search history and suggests the most suitable property. For example, properties with similar conditions can be preferentially displayed based on the conditions of properties the user has searched for in the past. The history analysis unit can also analyze the characteristics of properties in which the user has shown interest in the past and suggest properties that the user prefers. This makes it possible to suggest the most suitable property based on the user's past search history.

[0126] The property matching system can further include a lifestyle pattern learning unit that learns the user's lifestyle patterns and suggests optimal properties. For example, if the user goes to work early in the morning, properties with short commute times can be displayed preferentially. Also, if the user works late at night, properties in quiet environments can be suggested. This makes it possible to suggest optimal properties according to the user's lifestyle patterns.

[0127] The property matching system can further include a family structure analysis unit that suggests properties based on the user's family structure. For example, if the user has children, properties near school districts or parks can be displayed preferentially. Also, if the user has pets, properties that allow pets can be suggested. This allows the system to suggest properties that are optimal for the user's family structure.

[0128] The property matching system can further estimate the user's emotions and adjust the property suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, simple and highly visible property information can be provided. On the other hand, if the user is relaxed, detailed property information can be provided. This makes it possible to provide the optimal property suggestion method according to the user's emotions.

[0129] The property matching system can further include a weather information analysis unit that takes into account the user's current weather information when proposing properties. For example, on a rainy day, properties close to the station can be displayed preferentially. On a sunny day, properties with balconies or gardens can be suggested. This allows the system to suggest optimal properties based on the user's current weather information.

[0130] The property matching system can further estimate the user's emotions and provide detailed property information based on the estimated user's emotions. For example, if the user is feeling stressed, only basic property information can be provided. Alternatively, if the user is feeling relaxed, detailed property information can be provided. This makes it possible to provide optimal property information according to the user's emotions.

[0131] The property matching system can further estimate the user's emotions and filter property search results based on the estimated user emotions. For example, if the user is feeling stressed, the system can narrow down the search results and display them. Alternatively, if the user is feeling relaxed, the system can display a wider range of search results. This makes it possible to provide optimal search results according to the user's emotions.

[0132] The property matching system can further estimate the user's emotions and adjust the order of property suggestions based on the estimated user emotions. For example, if the user is feeling stressed, important properties can be displayed preferentially. Also, if the user is feeling relaxed, detailed property information can be provided. This makes it possible to provide the optimal property suggestion order according to the user's emotions.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The reception unit provides an interface for the user to input desired conditions. For example, the user can input the conditions in text or chat format. Step 2: The condition analysis unit uses AI to analyze the conditions received by the reception unit. For example, it uses natural language processing technology to analyze the user's desired conditions. Step 3: The property search unit searches the database for the most suitable property based on the analyzed conditions, for example, the property that best matches the user's desired conditions. Step 4: The property providing unit provides the searched properties to the user as a list. For example, the property information is displayed in a list format so that the user can easily check it. Step 5: The video provider provides video for the user to check the details of the property they selected. For example, a drone may be used to fly around the room and provide video in real time. This allows the user to check the status of the room online.

[0135] 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.

[0136] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives condition input; a condition analysis unit that analyzes the condition accepted by the acceptance unit; a property search unit that searches for properties based on the conditions analyzed by the condition analysis unit; a property providing unit that provides the property searched by the property search unit to a user; and a video providing unit for allowing a user to check details of the property provided by the property providing unit. A system characterized by:

2. The video providing unit Flying a drone around the room and providing real-time footage 2. The system of claim 1.

3. The condition analysis unit Analyze user requirements using natural language processing 2. The system of claim 1.

4. The property search unit Search for the best property from the database based on the analyzed conditions 2. The system of claim 1.

5. The property providing unit Provide the searched properties to the user as a list 2. The system of claim 1.

6. The video providing unit Providing video that is streamed to the user's device 2. The system of claim 1.

7. The video providing unit Equipped with a flight path optimization unit to optimize the drone's flight path 2. The system of claim 1.

8. The property providing unit Equipped with an interface that users can easily operate using a smartphone or PC 2. The system of claim 1.

9. The reception unit Estimating a user's emotion and adjusting a condition input interface based on the estimated user's emotion 2. The system of claim 1.

Images