system

Abstract

The system according to the embodiment aims to accurately estimate indoor radio wave strength and to perform comprehensive coverage improvement. [Solution] A system according to an embodiment includes a collection unit, a generation unit, an estimation unit, and an improvement unit. The collection unit collects real estate information such as rental properties. The generation unit creates an indoor layout based on the information collected by the collection unit. The estimation unit performs radio wave propagation estimation based on the indoor layout created by the generation unit and outdoor map information. The improvement unit performs area improvement based on the distribution of radio wave intensity obtained by the estimation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to accurately estimate indoor radio wave strength and comprehensively improve coverage.

[0005] The system according to the embodiment aims to accurately estimate indoor radio wave strength and to perform comprehensive coverage improvement. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, an estimation unit, and an improvement unit. The collection unit collects real estate information such as rental properties. The generation unit creates an indoor layout based on the information collected by the collection unit. The estimation unit performs radio wave propagation estimation based on the indoor layout created by the generation unit and outdoor map information. The improvement unit performs area improvement based on the distribution of radio wave intensity obtained by the estimation unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately estimate indoor radio wave strength and provide comprehensive coverage improvement. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention accurately estimates radio wave strength in indoor areas and performs comprehensive area improvements. This system collects real estate information, such as rental properties, and a generation AI constructs an indoor layout. Based on outdoor map information and the obtained indoor layout, radio wave propagation estimation is performed to improve the area. For example, information such as floor plans of rental properties and indoor videos taken during viewings is collected and input into the generation AI. The generation AI identifies the location and wall thickness of each room from the floor plan and the furniture layout and materials from the indoor video. Next, based on the indoor layout and outdoor map information, radio wave propagation estimation is performed for all indoor and outdoor areas. For example, the attenuation of radio waves as they penetrate from outdoors to indoors and reflection and absorption by walls and furniture inside the building are taken into account. Finally, based on the distribution of radio wave strength obtained, repeaters are installed and antennas are adjusted in areas with low radio wave strength. This optimizes radio wave strength in all indoor and outdoor areas, improving the user's communication environment. This enables the system to take proactive, comprehensive area improvements, rather than individually responding to customer requests as in the past. Furthermore, since it is not necessary to enter the customer's home each time to create an indoor layout, this is an efficient and practical method.

[0029] A radio wave propagation estimation system according to an embodiment includes a collection unit, a generation unit, an estimation unit, and an improvement unit. The collection unit collects real estate information, such as rental properties. For example, the collection unit can collect information such as floor plans of rental properties and indoor videos taken during viewings. The collection unit can also automatically collect information from real estate information sites on the Internet. The generation unit uses a generation AI to construct an indoor layout based on the information collected by the collection unit. For example, the generation unit identifies the location of each room and the thickness of walls from the floor plan and identifies the furniture arrangement and materials from the indoor video. The generation AI can generate a detailed indoor layout using a text generation AI (e.g., LLM) or a multimodal generation AI. The estimation unit performs radio wave propagation estimation based on the indoor layout constructed by the generation unit and outdoor map information. For example, the estimation unit simulates radio wave propagation paths and attenuation for all indoor and outdoor areas. The estimation unit can use algorithms such as cosine similarity and Jaccard coefficient to simulate radio wave propagation paths. The improvement unit performs area improvement based on the distribution of radio wave strength obtained by the estimation unit. For example, the improvement unit can install repeaters or adjust antennas in areas with low radio wave strength. This allows the radio wave propagation estimation system according to the embodiment to accurately estimate radio wave strength in indoor areas and perform comprehensive area improvement.

[0030] The collection unit can collect information on floor plans or indoor videos. For example, the collection unit can collect floor plans of rental properties. The floor plans include information on the layout of rooms, the location of walls, the locations of windows and doors, and the like. The collection unit can also collect indoor videos taken during viewings. The indoor videos include information on the layout and materials of furniture, the size of rooms, and the like. Furthermore, the collection unit can automatically collect floor plans and indoor videos from real estate information sites on the Internet. In this way, detailed indoor layouts can be created by collecting information such as floor plans and indoor videos.

[0031] The generation unit can identify the location of each room and the thickness of walls from the floor plan, and can identify the layout and materials of furniture from indoor video. The generation unit can, for example, analyze the floor plan to identify the location of each room. The floor plan includes information such as the layout of rooms, the location of walls, and the locations of windows and doors, so the generation unit can identify the location of each room based on this information. The generation unit can also analyze the floor plan to identify the thickness of walls. Since wall thickness affects radio wave propagation, it is important to accurately identify the thickness. Furthermore, the generation unit can analyze indoor video to identify the layout and materials of furniture. For example, the generation unit can use a video analysis algorithm to identify the location and materials of furniture. This allows a detailed indoor layout to be constructed by analyzing floor plans and indoor video.

[0032] The estimation unit can simulate the propagation path and attenuation of radio waves for indoor and outdoor areas. The estimation unit can simulate the propagation path and attenuation of radio waves for, for example, all indoor and outdoor areas. The estimation unit can use algorithms such as cosine similarity and Jaccard coefficient to simulate the propagation path of radio waves. For example, the estimation unit can simulate the attenuation of radio waves as they penetrate from outdoors to indoors. Because radio waves are attenuated by obstacles such as walls and furniture, the simulation takes these factors into account. The estimation unit can also simulate reflection and absorption by walls and furniture indoors. This makes it possible to obtain an accurate distribution of radio wave strength by targeting all indoor and outdoor areas.

[0033] The estimation unit can take into account attenuation of radio waves when they penetrate indoors from outdoors, and reflection and absorption by walls and furniture indoors. The estimation unit can, for example, simulate attenuation of radio waves when they penetrate indoors from outdoors. Because radio waves are attenuated when they pass through obstacles such as walls and windows, the simulation takes these factors into account. The estimation unit can also simulate reflection and absorption by walls and furniture indoors. For example, the estimation unit simulates reflection and absorption of radio waves taking into account the material and thickness of walls, and the arrangement and material of furniture. This allows for accurate simulation of the radio wave propagation path and attenuation, thereby accurately estimating the distribution of radio wave strength.

[0034] The improvement unit can install repeaters and adjust antennas in areas with weak radio wave strength. For example, the improvement unit can install repeaters in areas with low radio wave strength. Repeaters are devices that amplify radio waves and propagate them over a wide area, and by installing them in areas with low radio wave strength, the radio wave coverage area can be expanded. The improvement unit can also adjust antennas. For example, by adjusting the direction and angle of the antenna, the propagation direction of radio waves can be optimized and radio wave strength can be improved. This makes it possible to optimize the communication environment by making appropriate improvements to areas with low radio wave strength.

[0035] The collection unit can analyze past historical information about rental properties and select the optimal collection method. The collection unit can, for example, analyze past historical information about rental properties and select the optimal collection method. For example, the collection unit can select the most efficient collection method based on previously collected rental property information. The collection unit can also identify the most suitable time period for collection from the past historical information about rental properties. Furthermore, the collection unit can analyze past historical information about rental properties and select the most suitable device or means for collection. In this way, information can be collected efficiently by selecting the optimal collection method based on the past historical information.

[0036] The collection unit can filter the real estate information based on the type of property and the region when collecting the real estate information. For example, the collection unit can filter the information to be collected based on the type of property (rental, sale, etc.). The collection unit can also filter the information to be collected based on the region of the property (urban, suburban, etc.). Furthermore, the collection unit can filter the information to be collected based on the price range of the property. In this way, by filtering the information based on the type of property and the region, highly relevant information can be collected.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting real estate information. For example, when collecting real estate information, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method.

[0038] When collecting real estate information, the collection unit can prioritize collecting highly relevant information by taking into consideration the geographical location information of the property. For example, when collecting real estate information, the collection unit can prioritize collecting highly relevant information by taking into consideration the geographical location information of the property. For example, the collection unit can prioritize collecting information on nearby facilities and transportation based on the geographical location information of the property. The collection unit can also prioritize collecting local security information based on the geographical location information of the property. Furthermore, the collection unit can prioritize collecting local environmental information based on the geographical location information of the property. In this way, highly relevant information can be prioritized by taking into consideration the geographical location information of the property.

[0039] The collection unit can analyze the social media activity of the property when collecting real estate information and collect related information. For example, the collection unit can analyze the social media activity of the property when collecting real estate information and collect related information. For example, the collection unit can collect ratings and reviews of the property on social media. The collection unit can also collect photos and videos of the property on social media. Furthermore, the collection unit can collect event information on the property on social media. In this way, by analyzing the social media activity of the property, related information can be collected efficiently.

[0040] The collection unit can customize the collection method by reflecting past feedback on the property when collecting real estate information. For example, the collection unit can customize the collection method by reflecting past feedback on the property when collecting real estate information. For example, the collection unit can customize the type of information to be collected based on past feedback on the property. The collection unit can also customize the priority of the information to be collected based on past feedback on the property. Furthermore, the collection unit can customize the means of collecting information based on past feedback on the property. In this way, the collection method can be optimized by reflecting past feedback on the property.

[0041] The generation unit can adjust the level of detail of generation based on the importance of the property when generating an indoor layout. For example, the generation unit can adjust the level of detail of generation based on the importance of the property when generating an indoor layout. For example, the generation unit can generate a detailed indoor layout for a property with high importance. The generation unit can also generate a simplified indoor layout for a property with low importance. Furthermore, the generation unit can adjust the accuracy of the layout to be generated according to the importance of the property. In this way, by adjusting the level of detail of generation based on the importance of the property, an indoor layout can be generated efficiently.

[0042] The generation unit can apply different generation algorithms depending on the property category when generating an indoor layout. For example, the generation unit can apply different generation algorithms depending on the property category when generating an indoor layout. For example, the generation unit can apply a dedicated generation algorithm to rental properties. The generation unit can also apply a dedicated generation algorithm to commercial properties. Furthermore, the generation unit can also apply a dedicated generation algorithm to residential properties. In this way, an optimal indoor layout can be generated by applying a generation algorithm depending on the property category.

[0043] The generation unit can improve the accuracy of generation when generating an indoor layout by referring to past layout information of the property. For example, the generation unit can improve the accuracy of generation when generating an indoor layout by referring to past layout information of the property. For example, the generation unit can improve the accuracy of the generated layout based on the past layout information of the property. The generation unit can also analyze the past layout information of the property and adjust the level of detail of the generated layout. Furthermore, the generation unit can optimize the placement of the generated layout by referring to the past layout information of the property. In this way, the accuracy of generation can be improved by referring to the past layout information of the property.

[0044] The generation unit can determine the generation priority based on the time of submission of the property when generating an indoor layout. The generation unit can determine the generation priority based on the time of submission of the property when generating an indoor layout. For example, the generation unit can generate an indoor layout preferentially for a property that was submitted earlier. The generation unit can also generate an indoor layout for a property that was submitted later at a later date. Furthermore, the generation unit can adjust the level of detail of the layout to be generated depending on the time of submission. In this way, by determining the generation priority based on the time of submission of the property, layouts can be generated efficiently.

[0045] The generation unit can adjust the order of generation based on the relevance of properties when generating an indoor layout. For example, the generation unit can adjust the order of generation based on the relevance of properties when generating an indoor layout. For example, if the relevance of properties is high, the generation unit can generate the indoor layout with priority. Also, if the relevance of properties is low, the generation unit can generate the indoor layout at a later date. Furthermore, the generation unit can adjust the order of layouts to be generated according to the relevance of properties. As a result, by adjusting the order of generation based on the relevance of properties, layouts can be generated efficiently.

[0046] The generation unit can adjust the use of technical terminology in generating an indoor layout according to the level of expertise of the property when generating the indoor layout. For example, the generation unit can adjust the use of technical terminology in generating an indoor layout according to the level of expertise of the property when generating the indoor layout. For example, if the level of expertise is high, the generation unit can generate an indoor layout that uses a lot of technical terminology. Also, if the level of expertise is low, the generation unit can generate an indoor layout that is explained in simple terms. Furthermore, the generation unit can adjust the terminology of the layout to be generated according to the level of expertise. In this way, an appropriate layout can be provided by adjusting the use of technical terminology according to the level of expertise of the property.

[0047] The estimation unit can improve the accuracy of the estimation by taking into account the interrelationships between properties when estimating radio wave propagation. For example, the estimation unit can improve the accuracy of the estimation by taking into account the interrelationships between properties when estimating radio wave propagation. For example, the estimation unit can improve the accuracy of the radio wave propagation estimation based on the interrelationships between properties. The estimation unit can also analyze the interrelationships between properties and optimize the path of the estimated radio wave propagation. Furthermore, the estimation unit can adjust the attenuation of the estimated radio wave propagation by taking into account the interrelationships between properties. In this way, the accuracy of the radio wave propagation estimation can be improved by taking into account the interrelationships between properties.

[0048] The estimation unit can perform estimation by taking into account the attribute information of the property when estimating radio wave propagation. For example, the estimation unit can perform estimation by taking into account the attribute information of the property when estimating radio wave propagation. For example, the estimation unit can perform radio wave propagation estimation based on the attribute information of the property (material, structure, etc.). The estimation unit can also analyze the attribute information of the property and optimize the path of the radio wave propagation to be estimated. Furthermore, the estimation unit can adjust the attenuation of the radio wave propagation to be estimated by taking into account the attribute information of the property. In this way, by taking into account the attribute information of the property, the accuracy of the radio wave propagation estimation can be improved.

[0049] The estimation unit can weight the estimation based on the submission frequency of the property when estimating radio wave propagation. For example, the estimation unit can weight the estimation based on the submission frequency of the property when estimating radio wave propagation. For example, the estimation unit can perform radio wave propagation estimation by assigning a higher weight to a property with a high submission frequency. The estimation unit can also perform radio wave propagation estimation by assigning a lower weight to a property with a low submission frequency. Furthermore, the estimation unit can adjust the accuracy of the estimated radio wave propagation according to the submission frequency. As a result, by weighting the estimation based on the submission frequency of the property, estimation can be performed efficiently.

[0050] The estimation unit can perform estimation taking into account the geographical distribution of properties when estimating radio wave propagation. For example, the estimation unit can perform estimation taking into account the geographical distribution of properties when estimating radio wave propagation. For example, the estimation unit can perform radio wave propagation estimation based on the geographical distribution of properties. The estimation unit can also analyze the geographical distribution of properties and optimize the path of the estimated radio wave propagation. Furthermore, the estimation unit can adjust the attenuation of the estimated radio wave propagation taking into account the geographical distribution of properties. In this way, the accuracy of the radio wave propagation estimation can be improved by taking into account the geographical distribution of properties.

[0051] The estimation unit can improve the accuracy of the estimation by referring to literature related to the property when estimating radio wave propagation. For example, the estimation unit can improve the accuracy of the estimation by referring to literature related to the property when estimating radio wave propagation. For example, the estimation unit can improve the accuracy of the radio wave propagation estimation based on literature related to the property. The estimation unit can also analyze literature related to the property and optimize the path of the radio wave propagation to be estimated. Furthermore, the estimation unit can adjust the attenuation of the radio wave propagation to be estimated by referring to literature related to the property. In this way, the accuracy of the radio wave propagation estimation can be improved by referring to literature related to the property.

[0052] The estimation unit can perform estimation taking into account the market value of a property when estimating radio wave propagation. For example, the estimation unit can perform estimation taking into account the market value of a property when estimating radio wave propagation. For example, the estimation unit can perform radio wave propagation estimation based on the market value of a property. The estimation unit can also analyze the market value of a property and optimize the path of the radio wave propagation to be estimated. Furthermore, the estimation unit can adjust the attenuation of the radio wave propagation to be estimated taking into account the market value of the property. In this way, by taking into account the market value of the property, the accuracy of the radio wave propagation estimation can be improved.

[0053] When improving an area, the improvement department can analyze the past improvement history of the property to select the optimal improvement method. When improving an area, for example, the improvement department can analyze the past improvement history of the property to select the optimal improvement method. For example, the improvement department can select the optimal improvement method based on the past improvement history of the property. The improvement department can also analyze the past improvement history of the property and determine the priority of the areas to be improved. Furthermore, the improvement department can optimize the improvement measures by referring to the past improvement history of the property. In this way, the optimal improvement method can be selected by analyzing the past improvement history of the property.

[0054] The improvement department can customize improvement measures based on the current status of the property when improving an area. For example, the improvement department can customize improvement measures based on the current status of the property when improving an area. For example, the improvement department can select the optimal improvement measures based on the current status of the property. The improvement department can also analyze the current status of the property and determine the priority of the areas to be improved. Furthermore, the improvement department can optimize the improvement measures with reference to the current status of the property. In this way, by customizing improvement measures based on the current status of the property, more appropriate improvements can be made.

[0055] The improvement department can improve the improvement method by reflecting property feedback when improving an area. For example, the improvement department can improve the improvement method by reflecting property feedback when improving an area. For example, the improvement department can determine the priority of areas to be improved based on property feedback. The improvement department can also analyze property feedback and optimize improvement measures. Furthermore, the improvement department can customize the improvement method by referring to property feedback. In this way, the improvement method can be optimized by reflecting property feedback.

[0056] When improving an area, the improvement department can select the optimal improvement method by taking into consideration the geographical location information of the property. For example, when improving an area, the improvement department can select the optimal improvement method by taking into consideration the geographical location information of the property. For example, the improvement department can select the optimal improvement method based on the geographical location information of the property. The improvement department can also analyze the geographical location information of the property and determine the priority of the areas to be improved. Furthermore, the improvement department can optimize the improvement measures by referring to the geographical location information of the property. In this way, the optimal improvement method can be selected by taking into consideration the geographical location information of the property.

[0057] When making area improvements, the improvement department can analyze the social media activity of the property and propose improvement measures. When making area improvements, the improvement department can analyze the social media activity of the property and propose improvement measures. For example, when making area improvements, the improvement department can propose improvement measures based on the ratings and reviews of the property on social media. The improvement department can also propose improvement measures based on photos and videos of the property on social media. Furthermore, the improvement department can also propose improvement measures based on event information on social media for the property. In this way, by analyzing the social media activity of the property, appropriate improvement measures can be proposed.

[0058] When improving an area, the improvement department can customize the improvement method by reflecting past feedback on the property. For example, when improving an area, the improvement department can customize the improvement method by reflecting past feedback on the property. For example, the improvement department can determine the priority of areas to be improved based on past feedback on the property. The improvement department can also analyze past feedback on the property and optimize the means of improvement. Furthermore, the improvement department can customize the improvement method by referring to past feedback on the property. In this way, the improvement method can be optimized by reflecting past feedback on the property.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can analyze past historical information about rental properties and select the optimal collection method. For example, the collection unit can select the most efficient collection method based on previously collected rental property information. The collection unit can also identify the most suitable time of day for collection from the past historical information about rental properties. Furthermore, the collection unit can analyze the past historical information about rental properties and select the most suitable device or means for collection. This allows information to be collected efficiently by selecting the optimal collection method based on the past historical information.

[0061] When collecting real estate information, the collection unit can filter the information based on the type of property or the region. For example, the collection unit can filter the information to be collected based on the type of property (rental, sale, etc.). The collection unit can also filter the information to be collected based on the region of the property (urban, suburban, etc.). Furthermore, the collection unit can filter the information to be collected based on the price range of the property. In this way, by filtering the information based on the type of property or the region, highly relevant information can be collected.

[0062] When collecting real estate information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Also, if the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method.

[0063] When generating an indoor layout, the generation unit can adjust the level of detail of the generation based on the importance of the property. For example, the generation unit can generate a detailed indoor layout for a property with high importance. The generation unit can also generate a simplified indoor layout for a property with low importance. Furthermore, the generation unit can adjust the accuracy of the generated layout depending on the importance of the property. In this way, by adjusting the level of detail of the generation based on the importance of the property, an indoor layout can be generated efficiently.

[0064] The estimation unit can improve the accuracy of the estimation by taking into account the interrelationships between properties when estimating radio wave propagation. For example, the estimation unit can improve the accuracy of the radio wave propagation estimation based on the interrelationships between properties. The estimation unit can also analyze the interrelationships between properties and optimize the path of the estimated radio wave propagation. Furthermore, the estimation unit can adjust the attenuation of the estimated radio wave propagation by taking into account the interrelationships between properties. In this way, the accuracy of the radio wave propagation estimation can be improved by taking into account the interrelationships between properties.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects real estate information such as rental properties. For example, the collection unit can collect information such as floor plans of rental properties and indoor videos taken during viewings. The collection unit can also automatically collect information from real estate information sites on the Internet. Step 2: The generation unit uses a generation AI to construct an indoor layout based on the information collected by the collection unit. For example, the generation unit identifies the location of each room and the thickness of walls from a floor plan, and identifies the placement and materials of furniture from indoor video. The generation AI can generate a detailed indoor layout using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The estimation unit performs radio wave propagation estimation based on the indoor layout and outdoor map information constructed by the generation unit. For example, the estimation unit simulates the radio wave propagation path and attenuation for all indoor and outdoor areas. The estimation unit can use algorithms such as cosine similarity and Jaccard coefficient to simulate the radio wave propagation path. Step 4: The improvement unit performs area improvement based on the distribution of radio wave strength obtained by the estimation unit. For example, the improvement unit can install repeaters or adjust antennas in areas with low radio wave strength. This allows the radio wave propagation estimation system according to the embodiment to accurately estimate radio wave strength in indoor areas and perform comprehensive area improvement.

[0067] (Example 2) A system according to an embodiment of the present invention accurately estimates radio wave strength in indoor areas and performs comprehensive area improvements. This system collects real estate information, such as rental properties, and a generation AI constructs an indoor layout. Based on outdoor map information and the obtained indoor layout, radio wave propagation estimation is performed to improve the area. For example, information such as floor plans of rental properties and indoor videos taken during viewings is collected and input into the generation AI. The generation AI identifies the location and wall thickness of each room from the floor plan and the furniture layout and materials from the indoor video. Next, based on the indoor layout and outdoor map information, radio wave propagation estimation is performed for all indoor and outdoor areas. For example, the attenuation of radio waves as they penetrate from outdoors to indoors and reflection and absorption by walls and furniture inside the building are taken into account. Finally, based on the distribution of radio wave strength obtained, repeaters are installed and antennas are adjusted in areas with low radio wave strength. This optimizes radio wave strength in all indoor and outdoor areas, improving the user's communication environment. This enables the system to take proactive, comprehensive area improvements, rather than individually responding to customer requests as in the past. Furthermore, since it is not necessary to enter the customer's home each time to create an indoor layout, this is an efficient and practical method.

[0068] A radio wave propagation estimation system according to an embodiment includes a collection unit, a generation unit, an estimation unit, and an improvement unit. The collection unit collects real estate information, such as rental properties. For example, the collection unit can collect information such as floor plans of rental properties and indoor videos taken during viewings. The collection unit can also automatically collect information from real estate information sites on the Internet. The generation unit uses a generation AI to construct an indoor layout based on the information collected by the collection unit. For example, the generation unit identifies the location of each room and the thickness of walls from the floor plan and identifies the furniture arrangement and materials from the indoor video. The generation AI can generate a detailed indoor layout using a text generation AI (e.g., LLM) or a multimodal generation AI. The estimation unit performs radio wave propagation estimation based on the indoor layout constructed by the generation unit and outdoor map information. For example, the estimation unit simulates radio wave propagation paths and attenuation for all indoor and outdoor areas. The estimation unit can use algorithms such as cosine similarity and Jaccard coefficient to simulate radio wave propagation paths. The improvement unit performs area improvement based on the distribution of radio wave strength obtained by the estimation unit. For example, the improvement unit can install repeaters or adjust antennas in areas with low radio wave strength. This allows the radio wave propagation estimation system according to the embodiment to accurately estimate radio wave strength in indoor areas and perform comprehensive area improvement.

[0069] The collection unit can collect information on floor plans or indoor videos. For example, the collection unit can collect floor plans of rental properties. The floor plans include information on the layout of rooms, the location of walls, the locations of windows and doors, and the like. The collection unit can also collect indoor videos taken during viewings. The indoor videos include information on the layout and materials of furniture, the size of rooms, and the like. Furthermore, the collection unit can automatically collect floor plans and indoor videos from real estate information sites on the Internet. In this way, detailed indoor layouts can be created by collecting information such as floor plans and indoor videos.

[0070] The generation unit can identify the location of each room and the thickness of walls from the floor plan, and can identify the layout and materials of furniture from indoor video. The generation unit can, for example, analyze the floor plan to identify the location of each room. The floor plan includes information such as the layout of rooms, the location of walls, and the locations of windows and doors, so the generation unit can identify the location of each room based on this information. The generation unit can also analyze the floor plan to identify the thickness of walls. Since wall thickness affects radio wave propagation, it is important to accurately identify the thickness. Furthermore, the generation unit can analyze indoor video to identify the layout and materials of furniture. For example, the generation unit can use a video analysis algorithm to identify the location and materials of furniture. This allows a detailed indoor layout to be constructed by analyzing floor plans and indoor video.

[0071] The estimation unit can simulate the propagation path and attenuation of radio waves for indoor and outdoor areas. The estimation unit can simulate the propagation path and attenuation of radio waves for, for example, all indoor and outdoor areas. The estimation unit can use algorithms such as cosine similarity and Jaccard coefficient to simulate the propagation path of radio waves. For example, the estimation unit can simulate the attenuation of radio waves as they penetrate from outdoors to indoors. Because radio waves are attenuated by obstacles such as walls and furniture, the simulation takes these factors into account. The estimation unit can also simulate reflection and absorption by walls and furniture indoors. This makes it possible to obtain an accurate distribution of radio wave strength by targeting all indoor and outdoor areas.

[0072] The estimation unit can take into account attenuation of radio waves when they penetrate indoors from outdoors, and reflection and absorption by walls and furniture indoors. The estimation unit can, for example, simulate attenuation of radio waves when they penetrate indoors from outdoors. Because radio waves are attenuated when they pass through obstacles such as walls and windows, the simulation takes these factors into account. The estimation unit can also simulate reflection and absorption by walls and furniture indoors. For example, the estimation unit simulates reflection and absorption of radio waves taking into account the material and thickness of walls, and the arrangement and material of furniture. This allows for accurate simulation of the radio wave propagation path and attenuation, thereby accurately estimating the distribution of radio wave strength.

[0073] The improvement unit can install repeaters and adjust antennas in areas with weak radio wave strength. For example, the improvement unit can install repeaters in areas with low radio wave strength. Repeaters are devices that amplify radio waves and propagate them over a wide area, and by installing them in areas with low radio wave strength, the radio wave coverage area can be expanded. The improvement unit can also adjust antennas. For example, by adjusting the direction and angle of the antenna, the propagation direction of radio waves can be optimized and radio wave strength can be improved. This makes it possible to optimize the communication environment by making appropriate improvements to areas with low radio wave strength.

[0074] The collection unit can estimate the user's emotions and adjust the timing of collecting real estate information based on the estimated user emotions. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting real estate information based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect real estate information when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately start collecting real estate information. Furthermore, if the user is in a hurry, the collection unit can quickly collect real estate information. In this way, by adjusting the collection timing according to the user's emotions, information can be collected at a more appropriate time.

[0075] The collection unit can analyze past historical information about rental properties and select the optimal collection method. The collection unit can, for example, analyze past historical information about rental properties and select the optimal collection method. For example, the collection unit can select the most efficient collection method based on previously collected rental property information. The collection unit can also identify the most suitable time period for collection from the past historical information about rental properties. Furthermore, the collection unit can analyze past historical information about rental properties and select the most suitable device or means for collection. In this way, information can be collected efficiently by selecting the optimal collection method based on the past historical information.

[0076] The collection unit can filter the real estate information based on the type of property and the region when collecting the real estate information. For example, the collection unit can filter the information to be collected based on the type of property (rental, sale, etc.). The collection unit can also filter the information to be collected based on the region of the property (urban, suburban, etc.). Furthermore, the collection unit can filter the information to be collected based on the price range of the property. In this way, by filtering the information based on the type of property and the region, highly relevant information can be collected.

[0077] The collection unit can select the optimal collection means depending on the user's input method when collecting real estate information. For example, when collecting real estate information, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method.

[0078] The collection unit can estimate the user's emotions and determine the priority of real estate information to be collected based on the estimated user's emotions. The collection unit can, for example, estimate the user's emotions and determine the priority of real estate information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can postpone collecting information of low importance. Furthermore, when the user is relaxed, the collection unit can prioritize collecting information of high importance. Furthermore, when the user is in a hurry, the collection unit can prioritize information that needs to be collected quickly. In this way, by determining the priority of information according to the user's emotions, important information can be collected preferentially.

[0079] When collecting real estate information, the collection unit can prioritize collecting highly relevant information by taking into consideration the geographical location information of the property. For example, when collecting real estate information, the collection unit can prioritize collecting highly relevant information by taking into consideration the geographical location information of the property. For example, the collection unit can prioritize collecting information on nearby facilities and transportation based on the geographical location information of the property. The collection unit can also prioritize collecting local security information based on the geographical location information of the property. Furthermore, the collection unit can prioritize collecting local environmental information based on the geographical location information of the property. In this way, highly relevant information can be prioritized by taking into consideration the geographical location information of the property.

[0080] The collection unit can analyze the social media activity of the property when collecting real estate information and collect related information. For example, the collection unit can analyze the social media activity of the property when collecting real estate information and collect related information. For example, the collection unit can collect ratings and reviews of the property on social media. The collection unit can also collect photos and videos of the property on social media. Furthermore, the collection unit can collect event information on the property on social media. In this way, by analyzing the social media activity of the property, related information can be collected efficiently.

[0081] The collection unit can customize the collection method by reflecting past feedback on the property when collecting real estate information. For example, the collection unit can customize the collection method by reflecting past feedback on the property when collecting real estate information. For example, the collection unit can customize the type of information to be collected based on past feedback on the property. The collection unit can also customize the priority of the information to be collected based on past feedback on the property. Furthermore, the collection unit can customize the means of collecting information based on past feedback on the property. In this way, the collection method can be optimized by reflecting past feedback on the property.

[0082] The generation unit can estimate the user's emotion and adjust the indoor layout generation method based on the estimated user's emotion. The generation unit can, for example, estimate the user's emotion and adjust the indoor layout generation method based on the estimated user's emotion. For example, the generation unit can generate a detailed indoor layout when the user is relaxed. Furthermore, the generation unit can generate a simplified indoor layout when the user is in a hurry. Furthermore, the generation unit can generate a visually appealing indoor layout when the user is excited. In this way, a more appropriate indoor layout can be generated by adjusting the generation method according to the user's emotion.

[0083] The generation unit can adjust the level of detail of generation based on the importance of the property when generating an indoor layout. For example, the generation unit can adjust the level of detail of generation based on the importance of the property when generating an indoor layout. For example, the generation unit can generate a detailed indoor layout for a property with high importance. The generation unit can also generate a simplified indoor layout for a property with low importance. Furthermore, the generation unit can adjust the accuracy of the layout to be generated according to the importance of the property. In this way, by adjusting the level of detail of generation based on the importance of the property, an indoor layout can be generated efficiently.

[0084] The generation unit can apply different generation algorithms depending on the property category when generating an indoor layout. For example, the generation unit can apply different generation algorithms depending on the property category when generating an indoor layout. For example, the generation unit can apply a dedicated generation algorithm to rental properties. The generation unit can also apply a dedicated generation algorithm to commercial properties. Furthermore, the generation unit can also apply a dedicated generation algorithm to residential properties. In this way, an optimal indoor layout can be generated by applying a generation algorithm depending on the property category.

[0085] The generation unit can improve the accuracy of generation when generating an indoor layout by referring to past layout information of the property. For example, the generation unit can improve the accuracy of generation when generating an indoor layout by referring to past layout information of the property. For example, the generation unit can improve the accuracy of the generated layout based on the past layout information of the property. The generation unit can also analyze the past layout information of the property and adjust the level of detail of the generated layout. Furthermore, the generation unit can optimize the placement of the generated layout by referring to the past layout information of the property. In this way, the accuracy of generation can be improved by referring to the past layout information of the property.

[0086] The generation unit can estimate the user's emotion and adjust the length of the indoor layout based on the estimated user's emotion. The generation unit can, for example, estimate the user's emotion and adjust the length of the indoor layout based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point indoor layout. If the user is relaxed, the generation unit can also generate a longer indoor layout including detailed explanations. Furthermore, if the user is excited, the generation unit can also generate an indoor layout with visually stimulating effects. In this way, by adjusting the length of the indoor layout according to the user's emotion, a more appropriate layout can be provided.

[0087] The generation unit can determine the generation priority based on the time of submission of the property when generating an indoor layout. The generation unit can determine the generation priority based on the time of submission of the property when generating an indoor layout. For example, the generation unit can generate an indoor layout preferentially for a property that was submitted earlier. The generation unit can also generate an indoor layout for a property that was submitted later at a later date. Furthermore, the generation unit can adjust the level of detail of the layout to be generated depending on the time of submission. In this way, by determining the generation priority based on the time of submission of the property, layouts can be generated efficiently.

[0088] The generation unit can adjust the order of generation based on the relevance of properties when generating an indoor layout. For example, the generation unit can adjust the order of generation based on the relevance of properties when generating an indoor layout. For example, if the relevance of properties is high, the generation unit can generate the indoor layout with priority. Also, if the relevance of properties is low, the generation unit can generate the indoor layout at a later date. Furthermore, the generation unit can adjust the order of layouts to be generated according to the relevance of properties. As a result, by adjusting the order of generation based on the relevance of properties, layouts can be generated efficiently.

[0089] The generation unit can adjust the use of technical terminology in generating an indoor layout according to the level of expertise of the property when generating the indoor layout. For example, the generation unit can adjust the use of technical terminology in generating an indoor layout according to the level of expertise of the property when generating the indoor layout. For example, if the level of expertise is high, the generation unit can generate an indoor layout that uses a lot of technical terminology. Also, if the level of expertise is low, the generation unit can generate an indoor layout that is explained in simple terms. Furthermore, the generation unit can adjust the terminology of the layout to be generated according to the level of expertise. In this way, an appropriate layout can be provided by adjusting the use of technical terminology according to the level of expertise of the property.

[0090] The estimation unit can estimate the user's emotion and adjust the criteria for radio wave propagation estimation based on the estimated user's emotion. The estimation unit can, for example, estimate the user's emotion and adjust the criteria for radio wave propagation estimation based on the estimated user's emotion. For example, the estimation unit can perform detailed radio wave propagation estimation when the user is relaxed. Furthermore, the estimation unit can perform simplified radio wave propagation estimation when the user is in a hurry. Furthermore, the estimation unit can perform visually appealing radio wave propagation estimation when the user is excited. In this way, by adjusting the criteria for radio wave propagation estimation according to the user's emotion, more appropriate estimation results can be obtained.

[0091] The estimation unit can improve the accuracy of the estimation by taking into account the interrelationships between properties when estimating radio wave propagation. For example, the estimation unit can improve the accuracy of the estimation by taking into account the interrelationships between properties when estimating radio wave propagation. For example, the estimation unit can improve the accuracy of the radio wave propagation estimation based on the interrelationships between properties. The estimation unit can also analyze the interrelationships between properties and optimize the path of the estimated radio wave propagation. Furthermore, the estimation unit can adjust the attenuation of the estimated radio wave propagation by taking into account the interrelationships between properties. In this way, the accuracy of the radio wave propagation estimation can be improved by taking into account the interrelationships between properties.

[0092] The estimation unit can perform estimation by taking into account the attribute information of the property when estimating radio wave propagation. For example, the estimation unit can perform estimation by taking into account the attribute information of the property when estimating radio wave propagation. For example, the estimation unit can perform radio wave propagation estimation based on the attribute information of the property (material, structure, etc.). The estimation unit can also analyze the attribute information of the property and optimize the path of the radio wave propagation to be estimated. Furthermore, the estimation unit can adjust the attenuation of the radio wave propagation to be estimated by taking into account the attribute information of the property. In this way, by taking into account the attribute information of the property, the accuracy of the radio wave propagation estimation can be improved.

[0093] The estimation unit can weight the estimation based on the submission frequency of the property when estimating radio wave propagation. For example, the estimation unit can weight the estimation based on the submission frequency of the property when estimating radio wave propagation. For example, the estimation unit can perform radio wave propagation estimation by assigning a higher weight to a property with a high submission frequency. The estimation unit can also perform radio wave propagation estimation by assigning a lower weight to a property with a low submission frequency. Furthermore, the estimation unit can adjust the accuracy of the estimated radio wave propagation according to the submission frequency. As a result, by weighting the estimation based on the submission frequency of the property, estimation can be performed efficiently.

[0094] The estimation unit can estimate the user's emotion and adjust the order in which the results of radio wave propagation estimation are displayed based on the estimated user's emotion. The estimation unit can, for example, estimate the user's emotion and adjust the order in which the results of radio wave propagation estimation are displayed based on the estimated user's emotion. For example, the estimation unit can prioritize displaying detailed results when the user is relaxed. Furthermore, the estimation unit can prioritize displaying results that highlight the main points when the user is in a hurry. Furthermore, the estimation unit can prioritize displaying visually appealing results when the user is excited. In this way, more appropriate information can be provided by adjusting the display order of the results according to the user's emotion.

[0095] The estimation unit can perform estimation taking into account the geographical distribution of properties when estimating radio wave propagation. For example, the estimation unit can perform estimation taking into account the geographical distribution of properties when estimating radio wave propagation. For example, the estimation unit can perform radio wave propagation estimation based on the geographical distribution of properties. The estimation unit can also analyze the geographical distribution of properties and optimize the path of the estimated radio wave propagation. Furthermore, the estimation unit can adjust the attenuation of the estimated radio wave propagation taking into account the geographical distribution of properties. In this way, the accuracy of the radio wave propagation estimation can be improved by taking into account the geographical distribution of properties.

[0096] The estimation unit can improve the accuracy of the estimation by referring to literature related to the property when estimating radio wave propagation. For example, the estimation unit can improve the accuracy of the estimation by referring to literature related to the property when estimating radio wave propagation. For example, the estimation unit can improve the accuracy of the radio wave propagation estimation based on literature related to the property. The estimation unit can also analyze literature related to the property and optimize the path of the radio wave propagation to be estimated. Furthermore, the estimation unit can adjust the attenuation of the radio wave propagation to be estimated by referring to literature related to the property. In this way, the accuracy of the radio wave propagation estimation can be improved by referring to literature related to the property.

[0097] The estimation unit can perform estimation taking into account the market value of a property when estimating radio wave propagation. For example, the estimation unit can perform estimation taking into account the market value of a property when estimating radio wave propagation. For example, the estimation unit can perform radio wave propagation estimation based on the market value of a property. The estimation unit can also analyze the market value of a property and optimize the path of the radio wave propagation to be estimated. Furthermore, the estimation unit can adjust the attenuation of the radio wave propagation to be estimated taking into account the market value of the property. In this way, by taking into account the market value of the property, the accuracy of the radio wave propagation estimation can be improved.

[0098] The improvement unit can estimate the user's emotions and adjust the area improvement method based on the estimated user's emotions. The improvement unit can, for example, estimate the user's emotions and adjust the area improvement method based on the estimated user's emotions. For example, the improvement unit can suggest a detailed area improvement method when the user is relaxed. Furthermore, the improvement unit can suggest a simplified area improvement method when the user is in a hurry. Furthermore, the improvement unit can suggest a visually appealing area improvement method when the user is excited. In this way, by adjusting the area improvement method according to the user's emotions, more appropriate improvements can be made.

[0099] When improving an area, the improvement department can analyze the past improvement history of the property to select the optimal improvement method. When improving an area, for example, the improvement department can analyze the past improvement history of the property to select the optimal improvement method. For example, the improvement department can select the optimal improvement method based on the past improvement history of the property. The improvement department can also analyze the past improvement history of the property and determine the priority of the areas to be improved. Furthermore, the improvement department can optimize the improvement measures by referring to the past improvement history of the property. In this way, the optimal improvement method can be selected by analyzing the past improvement history of the property.

[0100] The improvement department can customize improvement measures based on the current status of the property when improving an area. For example, the improvement department can customize improvement measures based on the current status of the property when improving an area. For example, the improvement department can select the optimal improvement measures based on the current status of the property. The improvement department can also analyze the current status of the property and determine the priority of the areas to be improved. Furthermore, the improvement department can optimize the improvement measures with reference to the current status of the property. In this way, by customizing improvement measures based on the current status of the property, more appropriate improvements can be made.

[0101] The improvement department can improve the improvement method by reflecting property feedback when improving an area. For example, the improvement department can improve the improvement method by reflecting property feedback when improving an area. For example, the improvement department can determine the priority of areas to be improved based on property feedback. The improvement department can also analyze property feedback and optimize improvement measures. Furthermore, the improvement department can customize the improvement method by referring to property feedback. In this way, the improvement method can be optimized by reflecting property feedback.

[0102] The improvement unit can estimate the user's emotions and determine the priority of area improvements based on the estimated user's emotions. The improvement unit can, for example, estimate the user's emotions and determine the priority of area improvements based on the estimated user's emotions. For example, when the user is relaxed, the improvement unit can prioritize improving areas of high importance. Furthermore, when the user is in a hurry, the improvement unit can prioritize areas that require immediate improvement. Furthermore, when the user is excited, the improvement unit can prioritize improving visually attractive areas. In this way, by determining the priority of area improvements according to the user's emotions, more appropriate improvements can be made.

[0103] When improving an area, the improvement department can select the optimal improvement method by taking into consideration the geographical location information of the property. For example, when improving an area, the improvement department can select the optimal improvement method by taking into consideration the geographical location information of the property. For example, the improvement department can select the optimal improvement method based on the geographical location information of the property. The improvement department can also analyze the geographical location information of the property and determine the priority of the areas to be improved. Furthermore, the improvement department can optimize the improvement measures by referring to the geographical location information of the property. In this way, the optimal improvement method can be selected by taking into consideration the geographical location information of the property.

[0104] When making area improvements, the improvement department can analyze the social media activity of the property and propose improvement measures. When making area improvements, the improvement department can analyze the social media activity of the property and propose improvement measures. For example, when making area improvements, the improvement department can propose improvement measures based on the ratings and reviews of the property on social media. The improvement department can also propose improvement measures based on photos and videos of the property on social media. Furthermore, the improvement department can also propose improvement measures based on event information on social media for the property. In this way, by analyzing the social media activity of the property, appropriate improvement measures can be proposed.

[0105] When improving an area, the improvement department can customize the improvement method by reflecting past feedback on the property. For example, when improving an area, the improvement department can customize the improvement method by reflecting past feedback on the property. For example, the improvement department can determine the priority of areas to be improved based on past feedback on the property. The improvement department can also analyze past feedback on the property and optimize the means of improvement. Furthermore, the improvement department can customize the improvement method by referring to past feedback on the property. In this way, the improvement method can be optimized by reflecting past feedback on the property. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, estimation unit, and improvement unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects floor plans and indoor videos of rental properties using the camera 42 and communication I / F 44 of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs an indoor layout using a generation AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates indoor and outdoor radio wave propagation. The improvement unit is realized, for example, by the control unit 46A of the smart device 14 and installs repeaters and adjusts antennas in areas with low radio wave strength. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, estimation unit, and improvement 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 collection unit collects floor plans and indoor videos of rental properties using the camera 42 and communication I / F 44 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs an indoor layout using a generation AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates indoor and outdoor radio wave propagation. The improvement unit is realized, for example, by the control unit 46A of the smart glasses 214 and installs repeaters and adjusts antennas in areas with low radio wave strength. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, estimation unit, and improvement unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects floor plans and indoor videos of rental properties using the camera 42 and communication I / F 44 of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and constructs an indoor layout using a generation AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and simulates indoor and outdoor radio wave propagation. The improvement unit is realized, for example, by the control unit 46A of the headset type terminal 314, and installs repeaters and adjusts antennas in areas with low radio wave strength. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, estimation unit, and improvement unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects floor plans and indoor videos of rental properties using the camera 42 and communication I / F 44 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and constructs an indoor layout using a generation AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and simulates indoor and outdoor radio wave propagation. The improvement unit is realized, for example, by the control unit 46A of the robot 414, and installs repeaters and adjusts antennas in areas with low radio wave strength.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit can estimate the user's emotions and adjust the timing of collecting real estate information based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect information when the user is relaxed. Also, if the user is relaxed, the collection unit can immediately start collecting real estate information. Furthermore, if the user is in a hurry, the collection unit can quickly collect real estate information. In this way, by adjusting the collection timing according to the user's emotions, information can be collected at a more appropriate time.

[0108] The generation unit can estimate the user's emotion and adjust the indoor layout generation method based on the estimated user's emotion. For example, the generation unit can generate a detailed indoor layout when the user is relaxed. Alternatively, the generation unit can generate a simplified indoor layout when the user is in a hurry. Furthermore, the generation unit can generate a visually appealing indoor layout when the user is excited. In this way, a more appropriate indoor layout can be generated by adjusting the generation method according to the user's emotion.

[0109] The estimation unit can estimate the user's emotion and adjust the criteria for radio wave propagation estimation based on the estimated user's emotion. For example, the estimation unit can perform detailed radio wave propagation estimation when the user is relaxed. Alternatively, the estimation unit can perform simplified radio wave propagation estimation when the user is in a hurry. Furthermore, the estimation unit can perform visually appealing radio wave propagation estimation when the user is excited. In this way, by adjusting the criteria for radio wave propagation estimation according to the user's emotion, more appropriate estimation results can be obtained.

[0110] The improvement unit can estimate the user's emotions and adjust the area improvement method based on the estimated user's emotions. For example, if the user is relaxed, the improvement unit can suggest a detailed area improvement method. If the user is in a hurry, the improvement unit can also suggest a simplified area improvement method. Furthermore, if the user is excited, the improvement unit can also suggest a visually appealing area improvement method. In this way, by adjusting the area improvement method according to the user's emotions, more appropriate improvements can be made.

[0111] The improvement unit can estimate the user's emotions and determine the priority of area improvements based on the estimated user's emotions. For example, if the user is relaxed, the improvement unit can prioritize improving areas of high importance. Also, if the user is in a hurry, the improvement unit can prioritize areas that need to be improved quickly. Furthermore, if the user is excited, the improvement unit can prioritize improving visually attractive areas. In this way, by determining the priority of area improvements according to the user's emotions, more appropriate improvements can be made.

[0112] The collection unit can analyze past historical information about rental properties and select the optimal collection method. For example, the collection unit can select the most efficient collection method based on previously collected rental property information. The collection unit can also identify the most suitable time of day for collection from the past historical information about rental properties. Furthermore, the collection unit can analyze the past historical information about rental properties and select the most suitable device or means for collection. This allows information to be collected efficiently by selecting the optimal collection method based on the past historical information.

[0113] When collecting real estate information, the collection unit can filter the information based on the type of property or the region. For example, the collection unit can filter the information to be collected based on the type of property (rental, sale, etc.). The collection unit can also filter the information to be collected based on the region of the property (urban, suburban, etc.). Furthermore, the collection unit can filter the information to be collected based on the price range of the property. In this way, by filtering the information based on the type of property or the region, highly relevant information can be collected.

[0114] When collecting real estate information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Also, if the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method.

[0115] When generating an indoor layout, the generation unit can adjust the level of detail of the generation based on the importance of the property. For example, the generation unit can generate a detailed indoor layout for a property with high importance. The generation unit can also generate a simplified indoor layout for a property with low importance. Furthermore, the generation unit can adjust the accuracy of the generated layout depending on the importance of the property. In this way, by adjusting the level of detail of the generation based on the importance of the property, an indoor layout can be generated efficiently.

[0116] The estimation unit can improve the accuracy of the estimation by taking into account the interrelationships between properties when estimating radio wave propagation. For example, the estimation unit can improve the accuracy of the radio wave propagation estimation based on the interrelationships between properties. The estimation unit can also analyze the interrelationships between properties and optimize the path of the estimated radio wave propagation. Furthermore, the estimation unit can adjust the attenuation of the estimated radio wave propagation by taking into account the interrelationships between properties. In this way, the accuracy of the radio wave propagation estimation can be improved by taking into account the interrelationships between properties.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects real estate information such as rental properties. For example, the collection unit can collect information such as floor plans of rental properties and indoor videos taken during viewings. The collection unit can also automatically collect information from real estate information sites on the Internet. Step 2: The generation unit uses a generation AI to construct an indoor layout based on the information collected by the collection unit. For example, the generation unit identifies the location of each room and the thickness of walls from a floor plan, and identifies the placement and materials of furniture from indoor video. The generation AI can generate a detailed indoor layout using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The estimation unit performs radio wave propagation estimation based on the indoor layout and outdoor map information constructed by the generation unit. For example, the estimation unit simulates the radio wave propagation path and attenuation for all indoor and outdoor areas. The estimation unit can use algorithms such as cosine similarity and Jaccard coefficient to simulate the radio wave propagation path. Step 4: The improvement unit performs area improvement based on the distribution of radio wave strength obtained by the estimation unit. For example, the improvement unit can install repeaters or adjust antennas in areas with low radio wave strength. This allows the radio wave propagation estimation system according to the embodiment to accurately estimate radio wave strength in indoor areas and perform comprehensive area improvement.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

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

[0140] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0153] 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.

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

[0156] 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.

[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 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.

[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 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).

[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] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection department that collects real estate information such as rental properties; a generation unit that constructs an indoor layout based on the information collected by the collection unit; an estimation unit that performs radio wave propagation estimation based on the indoor layout and outdoor map information constructed by the generation unit; an improvement unit that performs area improvement based on the distribution of radio wave intensity obtained by the estimation unit. A system characterized by:

2. The collecting unit Collect floor plan or interior video information 2. The system of claim 1.

3. The generation unit Identify the location of each room and the thickness of the walls from the floor plan, and identify the furniture layout and materials from indoor video.

2. The system of claim 1.

4. The estimation unit Simulates radio wave propagation paths and attenuation for indoor and outdoor areas 2. The system of claim 1.

5. The estimation unit Consider the attenuation of radio waves as they penetrate from outdoors to indoors, as well as the reflection and absorption by walls and furniture indoors.

2. The system of claim 1.

6. The improvement unit Install repeaters and adjust antennas in areas with weak signal strength 2. The system of claim 1.

7. The collecting unit Estimates user emotions and adjusts the timing of collecting real estate information based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the historical information of rental properties and select the optimal collection method 2. The system of claim 1.

9. The collecting unit When gathering real estate information, filter based on property type and location.

2. The system of claim 1.

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