system

Abstract

The system according to this embodiment aims to identify detailed shooting locations within a building and provide this information to multiple developers to propose the optimal course of action. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a shooting unit, an information input unit, a provision unit, a proposal unit, and a planning unit. The reception unit inputs recorded video data. The analysis unit analyzes the recorded video data input by the reception unit and indicates areas that should be photographed in more detail. The shooting unit photographs the areas indicated by the analysis unit. The information input unit inputs basic information in addition to the additionally photographed video information. The provision unit provides the information input by the information input unit to multiple developers in a single batch. The proposal unit proposes the optimal action based on the information provided by the provision unit. The planning unit jointly proposes a construction plan based on the action proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of identifying detailed shooting locations of a building and providing information to multiple developers to propose optimal actions is complex and difficult to perform efficiently.

[0005] The system according to the embodiment aims to identify detailed shooting locations of a building and provide information to multiple developers to propose optimal actions. [[ID= forty]]

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a shooting unit, an information input unit, a provision unit, a proposal unit, and a planning unit. The reception unit inputs recorded video data. The analysis unit analyzes the recorded video data input by the reception unit and indicates areas that should be photographed in more detail. The shooting unit photographs the areas indicated by the analysis unit. The information input unit inputs basic information in addition to the additionally photographed video information. The provision unit provides the information input by the information input unit to multiple developers in a single batch. The proposal unit proposes the optimal action based on the information provided by the provision unit. The planning unit jointly proposes a construction plan based on the action proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify detailed shooting locations within a building and provide information to multiple developers to propose the optimal course of action. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The building information provision system according to an embodiment of the present invention is a system that roughly records the exterior of a building and has an AI analyze it to instruct the system on areas that should be further filmed or photographed in detail. The building information provision system roughly records the exterior of a building and inputs the recorded data into the AI. Next, the AI ​​analyzes the recorded data and instructs the system on areas that should be filmed in more detail, such as the walls and ceilings of common areas and the ceilings of individual rooms. The user takes additional photos according to the AI's instructions. Next, in addition to the additionally filmed video information, the user inputs basic information such as the site area, year of construction, total floor area, and number of units. This information is provided to multiple developers in a single batch by the AI. Based on the provided information, the developers propose the most suitable actions, such as selling or repairing the building. Furthermore, the developers and the AI ​​cooperate to jointly propose construction plans such as rebuilding, repairing, and reinforcing the building. This significantly reduces the cost and speeds up the consideration of rebuilding or seismic reinforcement of condominiums and houses. For example, a roughly filmed aging condominium can be used, and the AI ​​instructs the user to film specific areas in detail. The system targets walls and ceilings in common areas, as well as walls and ceilings in individual rooms. Next, basic information such as site area, year of construction, total floor area, and number of units is entered and provided to the developer in a single batch. Based on the provided information, the developer proposes the most suitable course of action, such as selling or repairing. Furthermore, the developer and AI collaborate to jointly propose construction plans such as rebuilding, repairing, and reinforcing. This system significantly reduces the cost and speeds up the process of considering rebuilding or seismic reinforcement for condominiums and houses. In addition, users can smoothly collect information by following the AI's instructions, enabling them to make appropriate decisions even without specialized knowledge. This contributes to a safe and secure living environment. The building information provision system can roughly record the exterior of the building, and by having the AI ​​analyze it, it can instruct the AI ​​on areas that should be recorded in more detail via video or photo, and propose the most suitable course of action.

[0029] The building information provision system according to this embodiment comprises a reception unit, an analysis unit, a shooting unit, an information input unit, a provision unit, a proposal unit, and a planning unit. The reception unit inputs video data. For example, the reception unit can input data that roughly captures the exterior of a building. The analysis unit analyzes the video data input by the reception unit and indicates areas that should be photographed in more detail. For example, the analysis unit can indicate walls and ceilings in common areas, or ceilings in individual rooms. The shooting unit photographs the areas indicated by the analysis unit. For example, the shooting unit can photograph the indicated areas in high resolution. The information input unit inputs basic information in addition to the additionally photographed video information. For example, the information input unit can input basic information such as site area, year of construction, total floor area, and number of units. The provision unit provides the information input by the information input unit to multiple developers in a batch. For example, the provision unit can transmit the input information to multiple developers in a digital format. The proposal unit proposes the optimal action based on the provided information. The proposal department can suggest optimal actions, such as selling or repairing the property. The planning department then jointly proposes a construction plan based on the actions suggested by the proposal department. The planning department can, for example, have the developer and AI collaborate to propose construction plans such as rebuilding, repairing, or reinforcing the property. As a result, the building information provision system according to this embodiment can roughly record the exterior of the building, have the AI ​​analyze it, and then instruct the AI ​​on areas that should be recorded in more detail via video or photographs, thereby proposing the optimal action.

[0030] The reception desk inputs the recorded data. For example, the reception desk can input data from a rough recording of the building's exterior. Specifically, drones or high-resolution cameras could be used to record a wide area of ​​the building's exterior. Drones can capture the overall view from above the building, while cameras can photograph detailed parts from the ground. This collects basic data to understand the building's overall condition. Furthermore, the reception desk checks the quality of the recorded data and can request re-recording if there are any missing or unclear parts. For example, if the recorded data contains many shadows or reflections, a re-shoot can be instructed. This ensures that the analysis department has high-quality data for accurate analysis. The reception desk can also manage the metadata of the recorded data (date and time of shooting, location, photographer, etc.) and use it for subsequent processing. This improves data traceability and makes it possible to quickly search and refer to necessary information.

[0031] The analysis unit analyzes the video data entered by the reception unit and instructs the unit to further photograph areas that need to be filmed. For example, the analysis unit can instruct the unit to film walls and ceilings in common areas, or ceilings in individual rooms. Specifically, it uses AI to analyze the video data and automatically detect areas of deterioration or damage in the building. The AI ​​utilizes image recognition technology to identify abnormalities such as cracks, discoloration, and mold. For example, if a crack is found in a wall in a common area, the unit will instruct the unit to re-film that area at high resolution. Similarly, if stains or discoloration are detected on the ceiling, the unit will instruct the unit to film in detail. The analysis unit transmits these instructions to the filming unit in real time, enabling a rapid response. Furthermore, the analysis unit can also predict the deterioration risk of specific areas by utilizing past data and statistical information. For example, it can identify areas where certain building materials are prone to deterioration from past data and focus its analysis on those areas. This allows the analysis unit to efficiently and effectively understand the condition of the building and provide instructions for detailed filming of necessary areas.

[0032] The photography unit photographs the locations specified by the analysis unit. For example, the photography unit can photograph the specified locations at high resolution. Specifically, upon receiving instructions from the analysis unit, it uses drones and high-resolution cameras to perform detailed photography. Drones can easily photograph high places and hard-to-access areas of buildings, and high-resolution cameras can clearly capture minute cracks and discoloration. The photography unit utilizes these devices to accurately photograph the locations specified by the analysis unit and collect detailed data. The photography unit also checks the quality of the photographed data and can reshoot if there are any missing or unclear parts. For example, if the photographed data contains many shadows or reflections, it will reshoot to collect clear data. This ensures that the analysis unit has high-quality data to perform accurate analysis. Furthermore, the photography unit can manage the metadata of the photographed data (date and time of shooting, location, photographer, etc.) and use it for subsequent processing. This improves data traceability and makes it possible to quickly search and refer to necessary information.

[0033] The information input unit inputs basic information in addition to the newly recorded video information. For example, the information input unit can input basic information such as site area, year of construction, total floor area, and number of units. Specifically, it inputs basic building information in a digital format and registers it in the system. Site area and total floor area are important information for understanding the size and use of a building, while the year of construction serves as a criterion for evaluating the building's deterioration. The number of units is an indicator for understanding the number of residents and usage of the building. By accurately inputting this basic information and registering it in the system, the information input unit can be used for subsequent analysis and proposals. Furthermore, the information input unit uploads the newly recorded video information to the system, making it accessible to the analysis and provisioning units. This allows for centralized management of data across the entire system, enabling efficient information sharing. The information input unit also verifies the consistency of the input information and corrects any errors or omissions. This improves the overall data quality of the system, enabling accurate analysis and proposals.

[0034] The information provision unit provides information entered by the information input unit to multiple developers in a single batch. For example, the information provision unit can transmit the entered information to multiple developers in a digital format. Specifically, it organizes the information registered in the system in a digital format and provides it to developers. The information provision unit can provide customized information to each developer, quickly delivering information tailored to each developer's needs. For example, it can prioritize providing information on areas or buildings of interest to specific developers. Furthermore, the information provision unit can diversify its information provision methods and share information in real time through a digital platform. This allows developers to quickly obtain the latest information and make appropriate decisions. In addition, the information provision unit manages the history of information provision and can refer to previously provided information. This facilitates smooth communication with developers and enables the provision of highly reliable information.

[0035] The proposal department proposes the optimal course of action based on the information provided. For example, it can propose the best course of action such as selling or repairing the property. Specifically, it analyzes the building's condition and market trends based on the provided information and proposes the optimal course of action. Using AI, it evaluates the building's deterioration and market value to determine the timing of selling or repairing the property. For example, if the building is deteriorating, it proposes early repairs; conversely, if the market value is high, it proposes selling the property. The proposal department can also simulate multiple scenarios and select the most effective course of action. This allows the proposal department to propose the optimal course of action to building owners and managers and help maximize asset value. Furthermore, the proposal department provides the rationale and detailed explanations for its proposals, enabling owners and managers to make informed decisions. This allows the proposal department to provide highly reliable proposals and support the decision-making of owners and managers.

[0036] The Planning Department jointly proposes construction plans based on the actions proposed by the Proposal Department. For example, the Planning Department can collaborate with developers and AI to propose construction plans for rebuilding, repair, reinforcement, etc. Specifically, it develops detailed construction plans based on proposals from the Proposal Department. The AI ​​analyzes past construction data and the condition of the building to propose the optimal construction method and schedule. For example, it may develop a plan to prioritize repairs to areas where the building is deteriorating to improve overall durability. In addition, by collaborating with developers, it optimizes costs and construction periods, achieving efficient construction. The Planning Department monitors the progress of the construction in real time and can revise the plan as needed. This allows the Planning Department to always provide flexible construction plans based on the latest information and support the success of the construction. Furthermore, the Planning Department can take measures to ensure the safety and quality of the construction and minimize construction risks. This allows the Planning Department to provide reliable construction plans and give peace of mind to building owners and managers.

[0037] The analysis unit can specify areas that should be photographed in more detail, such as walls and ceilings in common areas, or ceilings in individual rooms. For example, the analysis unit can analyze data from a rough recording of the building's exterior and specify shooting locations based on the degree and importance of damage. For example, the analysis unit can use AI to analyze the recorded data and evaluate the degree of damage. For example, the analysis unit can use AI to analyze the recorded data and specify shooting locations based on importance. This allows for efficient information gathering by enabling the analysis unit to specify detailed shooting locations.

[0038] The information input section allows for the input of basic information such as site area, year of construction, total floor area, and number of units. For example, the information input section can input the building's site area. For example, the information input section can input the building's year of construction. For example, the information input section can input the building's total floor area. For example, the information input section can input the building's number of units. This improves the accuracy of the information provided to developers by allowing for the input of basic information.

[0039] The information provision unit can provide information entered by the information input unit to multiple developers in a single batch. For example, the information provision unit can send the entered information to multiple developers in a digital format. For example, the information provision unit can upload the entered information to cloud storage, making it accessible to developers. For example, the information provision unit can send the entered information to multiple developers via email. This streamlines information sharing among developers by providing information in a batch.

[0040] The proposal unit can suggest the most appropriate action, such as selling or repairing, based on the information provided. For example, the proposal unit can suggest selling the building based on the information provided. For example, the proposal unit can suggest repairing the building based on the information provided. For example, the proposal unit can suggest rebuilding the building based on the information provided. By suggesting the most appropriate action, the user can make an appropriate decision.

[0041] The planning department can collaborate with developers and AI to jointly propose construction plans such as rebuilding, repair, and reinforcement. For example, the planning department can collaborate with developers and AI to propose a rebuilding plan. For example, the planning department can collaborate with developers and AI to propose a repair plan. For example, the planning department can collaborate with developers and AI to propose a reinforcement plan. This collaboration between developers and AI improves the accuracy of construction plans.

[0042] The reception unit can analyze the user's past recording history when receiving recording data and select the optimal reception method. For example, the reception unit can automatically display locations that the user has frequently recorded in the past as candidates. For example, the reception unit can prioritize suggesting recording methods that the user has used in the past (manual, voice, etc.). For example, the reception unit can predict and suggest recording methods to be used during specific time periods based on the user's past recording history. In this way, by analyzing past recording history, the reception unit can provide the optimal reception method.

[0043] The reception unit can filter recorded data upon receipt based on the user's current projects and areas of interest. For example, the reception unit prioritizes receiving recorded data related to the user's current projects. For example, the reception unit filters and receives relevant recorded data based on the user's areas of interest. For example, the reception unit suggests the optimal method for receiving recorded data according to the progress of the user's projects. This allows for the priority of receiving highly relevant data by filtering based on the current project and areas of interest.

[0044] The reception unit can prioritize receiving highly relevant data when receiving recorded data, taking into account the user's geographical location information. For example, the reception unit prioritizes receiving recorded data related to the user's current location. For example, the reception unit filters and receives relevant recorded data based on the user's geographical location information. For example, the reception unit proposes the optimal method for receiving recorded data based on the user's location information. In this way, by considering geographical location information, highly relevant data can be prioritized.

[0045] The reception unit can analyze the user's social media activity when receiving recorded data and receive relevant data. For example, the reception unit can prioritize receiving relevant recorded data based on the user's social media activity. For example, the reception unit can filter and receive relevant recorded data based on the user's interests on social media. For example, the reception unit can analyze the user's social media activity and propose the optimal method for receiving recorded data. This allows for the priority of receiving relevant data by analyzing social media activity.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the recorded data during the analysis. For example, the analysis unit performs a detailed analysis on important recorded data. For example, the analysis unit performs a simplified analysis on less important recorded data. The analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the recorded data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the recorded data.

[0047] The analysis unit can apply different analysis algorithms depending on the category of the recorded data during analysis. For example, the analysis unit applies a specific analysis algorithm to recorded data of shared areas. For example, the analysis unit applies a different analysis algorithm to recorded data of individual rooms. For example, the analysis unit dynamically selects the optimal analysis algorithm depending on the category of the recorded data. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the category of the recorded data.

[0048] The analysis unit can determine the priority of analysis based on the submission date of the recorded data during the analysis process. For example, the analysis unit will prioritize the analysis of recently submitted recorded data. For example, the analysis unit will postpone the analysis of older recorded data. For example, the analysis unit can dynamically adjust the analysis priority based on the submission date. This enables efficient analysis by determining the analysis priority based on the submission date.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the recorded data during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant recorded data. For example, the analysis unit postpones the analysis of less relevant recorded data. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the recorded data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the recorded data.

[0050] The camera unit can analyze the user's past shooting history and select the optimal shooting method during shooting. For example, the camera unit can automatically display locations that the user has frequently photographed in the past as candidates. For example, the camera unit can prioritize suggesting shooting methods that the user has used in the past (manual, voice, etc.). For example, the camera unit can predict and suggest a shooting method to be used at a specific time of day based on the user's past shooting history. In this way, by analyzing past shooting history, the camera unit can provide the optimal shooting method.

[0051] The camera unit can filter images based on the user's current projects and areas of interest during the shooting process. For example, the camera unit prioritizes capturing data related to the user's current project. For example, the camera unit filters and captures relevant data based on the user's areas of interest. For example, the camera unit suggests the optimal shooting method according to the progress of the user's project. This allows for the priority capture of highly relevant data by filtering based on the current project and areas of interest.

[0052] The camera unit can prioritize capturing highly relevant locations by considering the user's geographical location information during shooting. For example, the camera unit will prioritize capturing locations related to the user's current location. For example, the camera unit will filter and capture relevant locations based on the user's geographical location information. For example, the camera unit will suggest the optimal shooting method based on the user's location information. In this way, by considering geographical location information, highly relevant locations can be prioritized for shooting.

[0053] The photography team can analyze the user's social media activity during shooting and photograph relevant areas. For example, the photography team can prioritize photographing relevant areas based on the user's social media activity. For example, the photography team can filter and photograph relevant areas based on the user's interests on social media. For example, the photography team can analyze the user's social media activity and suggest the optimal shooting method. This allows for prioritizing the photography of relevant areas by analyzing social media activity.

[0054] The information input unit can analyze the user's past input history and select the optimal input method when information is entered. For example, the information input unit can automatically display information that the user has frequently entered in the past as a suggestion. For example, the information input unit can prioritize suggesting input methods that the user has used in the past (manual, voice, etc.). For example, the information input unit can predict and suggest input methods to be used during a specific time period based on the user's past input history. In this way, by analyzing past input history, the optimal input method can be provided.

[0055] The information input unit can filter information based on the user's current projects and areas of interest during input. For example, the information input unit prioritizes inputting information related to the user's current ongoing projects. For example, the information input unit filters and inputs relevant information based on the user's areas of interest. For example, the information input unit suggests the optimal information input method according to the progress of the user's projects. This allows for the priority input of highly relevant information by filtering based on the current projects and areas of interest.

[0056] The information input unit can prioritize inputting highly relevant information by considering the user's geographical location during information input. For example, the information input unit prioritizes inputting information related to the user's current location. For example, the information input unit filters and inputs relevant information based on the user's geographical location. For example, the information input unit suggests the optimal information input method based on the user's location. In this way, by considering geographical location information, highly relevant information can be prioritized for input.

[0057] The information input unit can analyze the user's social media activity and input relevant information during data entry. For example, the information input unit prioritizes inputting relevant information based on the user's social media activity. For example, the information input unit filters and inputs relevant information based on the user's interests on social media. For example, the information input unit analyzes the user's social media activity and suggests the optimal information input method. This allows for the priority input of relevant information by analyzing social media activity.

[0058] The information delivery unit can analyze the user's past delivery history and select the optimal delivery method when providing information. For example, the unit can automatically display information that the user has frequently provided in the past as a candidate. For example, the unit can prioritize suggesting delivery methods that the user has used in the past (manual, voice, etc.). For example, the unit can predict and suggest a delivery method to be used during a specific time period based on the user's past delivery history. In this way, the optimal delivery method can be provided by analyzing past delivery history.

[0059] The information provider can filter information based on the user's current projects and areas of interest. For example, the provider can prioritize providing information related to the user's current projects. For example, the provider can filter and provide relevant information based on the user's areas of interest. For example, the provider can suggest the most appropriate information delivery method according to the progress of the user's projects. This allows for the priority provision of highly relevant information by filtering based on the user's current projects and areas of interest.

[0060] The information provider can prioritize providing highly relevant information by considering the user's geographical location when providing information. For example, the provider can prioritize providing information related to the user's current location. For example, the provider can filter and provide relevant information based on the user's geographical location. For example, the provider can propose the optimal information provision method based on the user's location. In this way, by considering geographical location, highly relevant information can be prioritized.

[0061] The information provider can analyze the user's social media activity and provide relevant information when providing information. For example, the provider can prioritize providing relevant information based on the user's social media activity. For example, the provider can filter and provide relevant information based on the user's interests on social media. For example, the provider can analyze the user's social media activity and propose the optimal method of providing information. In this way, by analyzing social media activity, it is possible to prioritize providing relevant information.

[0062] The proposal department can adjust the level of detail in a proposal based on the importance of the information. For example, the proposal department will provide a detailed proposal for important information, and a concise proposal for less important information. The proposal department can dynamically adjust the level of detail in a proposal according to the importance of the information. This allows for more efficient proposals by adjusting the level of detail according to the importance of the information.

[0063] The proposal unit can apply different proposal algorithms depending on the category of information when making a proposal. For example, the proposal unit applies a specific proposal algorithm to information related to sales. For example, the proposal unit applies a different proposal algorithm to information related to repairs. For example, the proposal unit dynamically selects the optimal proposal algorithm depending on the category of information. This improves the accuracy of proposals by applying the most suitable proposal algorithm according to the category of information.

[0064] The proposal department can prioritize proposals based on when the information was submitted. For example, the proposal department might prioritize recently submitted information. For example, the proposal department might postpone submitting older information. For example, the proposal department can dynamically adjust the priority of proposals based on the submission date. This allows for more efficient proposals by prioritizing proposals based on the submission date.

[0065] The proposal department can adjust the order of proposals based on the relevance of the information during the proposal process. For example, the proposal department will prioritize proposing highly relevant information. For example, the proposal department will postpone proposing less relevant information. For example, the proposal department can dynamically adjust the order of proposals based on the relevance of the information. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of the information.

[0066] The planning department can adjust the level of detail in a plan based on the importance of the information during the planning stage. For example, the planning department will create a detailed plan for important information. For example, the planning department will create a concise plan for less important information. The planning department can dynamically adjust the level of detail in a plan according to the importance of the information. This allows for efficient planning by adjusting the level of detail in a plan according to the importance of the information.

[0067] The planning department can apply different planning algorithms depending on the category of information during the planning stage. For example, the planning department applies a specific planning algorithm to information related to rebuilding. For example, the planning department applies a different planning algorithm to information related to repairs. For example, the planning department dynamically selects the optimal planning algorithm depending on the category of information. This improves the accuracy of the plan by applying the optimal planning algorithm according to the category of information.

[0068] The planning department can prioritize plans based on when information is submitted. For example, the planning department might prioritize recently submitted information. For example, the planning department might postpone planning older information. For example, the planning department can dynamically adjust plan priorities based on submission dates. This enables efficient planning by prioritizing plans based on submission dates.

[0069] The planning department can adjust the order of plans based on the relevance of information during the planning process. For example, the planning department prioritizes highly relevant information in its planning. For example, the planning department postpones planning less relevant information. For example, the planning department dynamically adjusts the order of plans based on the relevance of information. This allows for efficient planning by adjusting the order of plans based on the relevance of information.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The building information system can further analyze the user's past behavior history to provide optimal suggestions. For example, it can prioritize displaying suggestions that the user has frequently selected in the past. It can also prioritize suggestion methods the user has used in the past (e.g., email, chat). Furthermore, it can predict and suggest the most suitable suggestion method for a specific time period based on the user's past behavior history. In this way, by analyzing past behavior history, the system provides the most suitable suggestions for the user.

[0072] The building information system can further filter suggestions based on the user's current projects and areas of interest. For example, it can prioritize displaying suggestions related to the user's current projects. It can also filter and display relevant suggestions based on the user's areas of interest. Furthermore, it can suggest the most suitable proposal method according to the progress of the user's project. As a result, highly relevant suggestions are provided by filtering based on the current project and areas of interest.

[0073] The building information provision system can further customize its suggestions by taking into account the user's geographical location. For example, it can prioritize displaying suggestions relevant to the user's current location. It can also filter and display relevant suggestions based on the user's geographical location. Furthermore, it can suggest the most appropriate suggestion method based on the user's location. In this way, highly relevant suggestions are provided by considering geographical location.

[0074] The building information system can further analyze users' social media activity and provide relevant suggestions. For example, it can prioritize displaying relevant suggestions based on users' social media activity. It can also filter and display relevant suggestions based on users' interests on social media. Furthermore, it can analyze users' social media activity and suggest the most appropriate suggestion method. In this way, relevant suggestions are provided by analyzing social media activity.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The reception desk inputs the video data. For example, they can input data from a rough recording of the building's exterior. Step 2: The analysis unit analyzes the recorded data entered by the reception unit and provides further instructions on areas that should be filmed. For example, it can specify walls and ceilings in common areas, or ceilings in individual rooms. Step 3: The imaging unit photographs the area indicated by the analysis unit. For example, it can photograph the indicated area at high resolution. Step 4: The information input section allows you to input basic information in addition to the additional video footage. For example, you can input basic information such as site area, year of construction, total floor area, and number of units. Step 5: The provisioning unit provides the information entered by the information input unit to multiple developers in a single batch. For example, the entered information can be sent to multiple developers in a digital format. Step 6: The proposal team will suggest the best course of action based on the information provided. For example, they may suggest selling or repairing the property. Step 7: The Planning Department jointly proposes a construction plan based on the actions proposed by the Proposal Department. For example, the developer and AI can collaborate to propose construction plans such as rebuilding, repair, or reinforcement.

[0077] (Example of form 2) The building information provision system according to an embodiment of the present invention is a system that roughly records the exterior of a building and has an AI analyze it to instruct the system on areas that should be further filmed or photographed in detail. The building information provision system roughly records the exterior of a building and inputs the recorded data into the AI. Next, the AI ​​analyzes the recorded data and instructs the system on areas that should be filmed in more detail, such as the walls and ceilings of common areas and the ceilings of individual rooms. The user takes additional photos according to the AI's instructions. Next, in addition to the additionally filmed video information, the user inputs basic information such as the site area, year of construction, total floor area, and number of units. This information is provided to multiple developers in a single batch by the AI. Based on the provided information, the developers propose the most suitable actions, such as selling or repairing the building. Furthermore, the developers and the AI ​​cooperate to jointly propose construction plans such as rebuilding, repairing, and reinforcing the building. This significantly reduces the cost and speeds up the consideration of rebuilding or seismic reinforcement of condominiums and houses. For example, a roughly filmed aging condominium can be used, and the AI ​​instructs the user to film specific areas in detail. The system targets walls and ceilings in common areas, as well as walls and ceilings in individual rooms. Next, basic information such as site area, year of construction, total floor area, and number of units is entered and provided to the developer in a single batch. Based on the provided information, the developer proposes the most suitable course of action, such as selling or repairing. Furthermore, the developer and AI collaborate to jointly propose construction plans such as rebuilding, repairing, and reinforcing. This system significantly reduces the cost and speeds up the process of considering rebuilding or seismic reinforcement for condominiums and houses. In addition, users can smoothly collect information by following the AI's instructions, enabling them to make appropriate decisions even without specialized knowledge. This contributes to a safe and secure living environment. The building information provision system can roughly record the exterior of the building, and by having the AI ​​analyze it, it can instruct the AI ​​on areas that should be recorded in more detail via video or photo, and propose the most suitable course of action.

[0078] The building information provision system according to this embodiment comprises a reception unit, an analysis unit, a shooting unit, an information input unit, a provision unit, a proposal unit, and a planning unit. The reception unit inputs video data. For example, the reception unit can input data that roughly captures the exterior of a building. The analysis unit analyzes the video data input by the reception unit and indicates areas that should be photographed in more detail. For example, the analysis unit can indicate walls and ceilings in common areas, or ceilings in individual rooms. The shooting unit photographs the areas indicated by the analysis unit. For example, the shooting unit can photograph the indicated areas in high resolution. The information input unit inputs basic information in addition to the additionally photographed video information. For example, the information input unit can input basic information such as site area, year of construction, total floor area, and number of units. The provision unit provides the information input by the information input unit to multiple developers in a batch. For example, the provision unit can transmit the input information to multiple developers in a digital format. The proposal unit proposes the optimal action based on the provided information. The proposal department can suggest optimal actions, such as selling or repairing the property. The planning department then jointly proposes a construction plan based on the actions suggested by the proposal department. The planning department can, for example, have the developer and AI collaborate to propose construction plans such as rebuilding, repairing, or reinforcing the property. As a result, the building information provision system according to this embodiment can roughly record the exterior of the building, have the AI ​​analyze it, and then instruct the AI ​​on areas that should be recorded in more detail via video or photographs, thereby proposing the optimal action.

[0079] The reception desk inputs the recorded data. For example, the reception desk can input data from a rough recording of the building's exterior. Specifically, drones or high-resolution cameras could be used to record a wide area of ​​the building's exterior. Drones can capture the overall view from above the building, while cameras can photograph detailed parts from the ground. This collects basic data to understand the building's overall condition. Furthermore, the reception desk checks the quality of the recorded data and can request re-recording if there are any missing or unclear parts. For example, if the recorded data contains many shadows or reflections, a re-shoot can be instructed. This ensures that the analysis department has high-quality data for accurate analysis. The reception desk can also manage the metadata of the recorded data (date and time of shooting, location, photographer, etc.) and use it for subsequent processing. This improves data traceability and makes it possible to quickly search and refer to necessary information.

[0080] The analysis unit analyzes the video data entered by the reception unit and instructs the unit to further photograph areas that need to be filmed. For example, the analysis unit can instruct the unit to film walls and ceilings in common areas, or ceilings in individual rooms. Specifically, it uses AI to analyze the video data and automatically detect areas of deterioration or damage in the building. The AI ​​utilizes image recognition technology to identify abnormalities such as cracks, discoloration, and mold. For example, if a crack is found in a wall in a common area, the unit will instruct the unit to re-film that area at high resolution. Similarly, if stains or discoloration are detected on the ceiling, the unit will instruct the unit to film in detail. The analysis unit transmits these instructions to the filming unit in real time, enabling a rapid response. Furthermore, the analysis unit can also predict the deterioration risk of specific areas by utilizing past data and statistical information. For example, it can identify areas where certain building materials are prone to deterioration from past data and focus its analysis on those areas. This allows the analysis unit to efficiently and effectively understand the condition of the building and provide instructions for detailed filming of necessary areas.

[0081] The photography unit photographs the locations specified by the analysis unit. For example, the photography unit can photograph the specified locations at high resolution. Specifically, upon receiving instructions from the analysis unit, it uses drones and high-resolution cameras to perform detailed photography. Drones can easily photograph high places and hard-to-access areas of buildings, and high-resolution cameras can clearly capture minute cracks and discoloration. The photography unit utilizes these devices to accurately photograph the locations specified by the analysis unit and collect detailed data. The photography unit also checks the quality of the photographed data and can reshoot if there are any missing or unclear parts. For example, if the photographed data contains many shadows or reflections, it will reshoot to collect clear data. This ensures that the analysis unit has high-quality data to perform accurate analysis. Furthermore, the photography unit can manage the metadata of the photographed data (date and time of shooting, location, photographer, etc.) and use it for subsequent processing. This improves data traceability and makes it possible to quickly search and refer to necessary information.

[0082] The information input unit inputs basic information in addition to the newly recorded video information. For example, the information input unit can input basic information such as site area, year of construction, total floor area, and number of units. Specifically, it inputs basic building information in a digital format and registers it in the system. Site area and total floor area are important information for understanding the size and use of a building, while the year of construction serves as a criterion for evaluating the building's deterioration. The number of units is an indicator for understanding the number of residents and usage of the building. By accurately inputting this basic information and registering it in the system, the information input unit can be used for subsequent analysis and proposals. Furthermore, the information input unit uploads the newly recorded video information to the system, making it accessible to the analysis and provisioning units. This allows for centralized management of data across the entire system, enabling efficient information sharing. The information input unit also verifies the consistency of the input information and corrects any errors or omissions. This improves the overall data quality of the system, enabling accurate analysis and proposals.

[0083] The information provision unit provides information entered by the information input unit to multiple developers in a single batch. For example, the information provision unit can transmit the entered information to multiple developers in a digital format. Specifically, it organizes the information registered in the system in a digital format and provides it to developers. The information provision unit can provide customized information to each developer, quickly delivering information tailored to each developer's needs. For example, it can prioritize providing information on areas or buildings of interest to specific developers. Furthermore, the information provision unit can diversify its information provision methods and share information in real time through a digital platform. This allows developers to quickly obtain the latest information and make appropriate decisions. In addition, the information provision unit manages the history of information provision and can refer to previously provided information. This facilitates smooth communication with developers and enables the provision of highly reliable information.

[0084] The proposal department proposes the optimal course of action based on the information provided. For example, it can propose the best course of action such as selling or repairing the property. Specifically, it analyzes the building's condition and market trends based on the provided information and proposes the optimal course of action. Using AI, it evaluates the building's deterioration and market value to determine the timing of selling or repairing the property. For example, if the building is deteriorating, it proposes early repairs; conversely, if the market value is high, it proposes selling the property. The proposal department can also simulate multiple scenarios and select the most effective course of action. This allows the proposal department to propose the optimal course of action to building owners and managers and help maximize asset value. Furthermore, the proposal department provides the rationale and detailed explanations for its proposals, enabling owners and managers to make informed decisions. This allows the proposal department to provide highly reliable proposals and support the decision-making of owners and managers.

[0085] The Planning Department jointly proposes construction plans based on the actions proposed by the Proposal Department. For example, the Planning Department can collaborate with developers and AI to propose construction plans for rebuilding, repair, reinforcement, etc. Specifically, it develops detailed construction plans based on proposals from the Proposal Department. The AI ​​analyzes past construction data and the condition of the building to propose the optimal construction method and schedule. For example, it may develop a plan to prioritize repairs to areas where the building is deteriorating to improve overall durability. In addition, by collaborating with developers, it optimizes costs and construction periods, achieving efficient construction. The Planning Department monitors the progress of the construction in real time and can revise the plan as needed. This allows the Planning Department to always provide flexible construction plans based on the latest information and support the success of the construction. Furthermore, the Planning Department can take measures to ensure the safety and quality of the construction and minimize construction risks. This allows the Planning Department to provide reliable construction plans and give peace of mind to building owners and managers.

[0086] The analysis unit can specify areas that should be photographed in more detail, such as walls and ceilings in common areas, or ceilings in individual rooms. For example, the analysis unit can analyze data from a rough recording of the building's exterior and specify shooting locations based on the degree and importance of damage. For example, the analysis unit can use AI to analyze the recorded data and evaluate the degree of damage. For example, the analysis unit can use AI to analyze the recorded data and specify shooting locations based on importance. This allows for efficient information gathering by enabling the analysis unit to specify detailed shooting locations.

[0087] The information input section allows for the input of basic information such as site area, year of construction, total floor area, and number of units. For example, the information input section can input the building's site area. For example, the information input section can input the building's year of construction. For example, the information input section can input the building's total floor area. For example, the information input section can input the building's number of units. This improves the accuracy of the information provided to developers by allowing for the input of basic information.

[0088] The information provision unit can provide information entered by the information input unit to multiple developers in a single batch. For example, the information provision unit can send the entered information to multiple developers in a digital format. For example, the information provision unit can upload the entered information to cloud storage, making it accessible to developers. For example, the information provision unit can send the entered information to multiple developers via email. This streamlines information sharing among developers by providing information in a batch.

[0089] The proposal unit can suggest the most appropriate action, such as selling or repairing, based on the information provided. For example, the proposal unit can suggest selling the building based on the information provided. For example, the proposal unit can suggest repairing the building based on the information provided. For example, the proposal unit can suggest rebuilding the building based on the information provided. By suggesting the most appropriate action, the user can make an appropriate decision.

[0090] The planning department can collaborate with developers and AI to jointly propose construction plans such as rebuilding, repair, and reinforcement. For example, the planning department can collaborate with developers and AI to propose a rebuilding plan. For example, the planning department can collaborate with developers and AI to propose a repair plan. For example, the planning department can collaborate with developers and AI to propose a reinforcement plan. This collaboration between developers and AI improves the accuracy of construction plans.

[0091] The reception unit can estimate the user's emotions and adjust the timing of recording data reception based on the estimated emotions. For example, if the user is stressed, the reception unit will quickly receive the recording data to minimize the user's effort. For example, if the user is relaxed, the reception unit will provide detailed explanations and carefully receive the recording data. For example, if the user is in a hurry, the reception unit will prioritize voice input and quickly receive the recording data. This reduces the user's burden by adjusting the timing of recording data reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The reception unit can analyze the user's past recording history when receiving recording data and select the optimal reception method. For example, the reception unit can automatically display locations that the user has frequently recorded in the past as candidates. For example, the reception unit can prioritize suggesting recording methods that the user has used in the past (manual, voice, etc.). For example, the reception unit can predict and suggest recording methods to be used during specific time periods based on the user's past recording history. In this way, by analyzing past recording history, the reception unit can provide the optimal reception method.

[0093] The reception unit can filter recorded data upon receipt based on the user's current projects and areas of interest. For example, the reception unit prioritizes receiving recorded data related to the user's current projects. For example, the reception unit filters and receives relevant recorded data based on the user's areas of interest. For example, the reception unit suggests the optimal method for receiving recorded data according to the progress of the user's projects. This allows for the priority of receiving highly relevant data by filtering based on the current project and areas of interest.

[0094] The reception unit can estimate the user's emotions and determine the priority of the recorded data to be received based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize receiving important recorded data. For example, if the user is relaxed, the reception unit will prioritize receiving detailed recorded data. For example, if the user is in a hurry, the reception unit will prioritize receiving recorded data that can be processed quickly. In this way, by prioritizing recorded data according to the user's emotions, important data can be received preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The reception unit can prioritize receiving highly relevant data when receiving recorded data, taking into account the user's geographical location information. For example, the reception unit prioritizes receiving recorded data related to the user's current location. For example, the reception unit filters and receives relevant recorded data based on the user's geographical location information. For example, the reception unit proposes the optimal method for receiving recorded data based on the user's location information. In this way, by considering geographical location information, highly relevant data can be prioritized.

[0096] The reception unit can analyze the user's social media activity when receiving recorded data and receive relevant data. For example, the reception unit can prioritize receiving relevant recorded data based on the user's social media activity. For example, the reception unit can filter and receive relevant recorded data based on the user's interests on social media. For example, the reception unit can analyze the user's social media activity and propose the optimal method for receiving recorded data. This allows for the priority of receiving relevant data by analyzing social media activity.

[0097] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. If the user is excited, the analysis unit provides analysis results with visually stimulating effects. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The analysis unit can adjust the level of detail of the analysis based on the importance of the recorded data during the analysis. For example, the analysis unit performs a detailed analysis on important recorded data. For example, the analysis unit performs a simplified analysis on less important recorded data. The analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the recorded data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the recorded data.

[0099] The analysis unit can apply different analysis algorithms depending on the category of the recorded data during analysis. For example, the analysis unit applies a specific analysis algorithm to recorded data of shared areas. For example, the analysis unit applies a different analysis algorithm to recorded data of individual rooms. For example, the analysis unit dynamically selects the optimal analysis algorithm depending on the category of the recorded data. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the category of the recorded data.

[0100] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit will perform a detailed analysis. If the user is excited, the analysis unit will perform an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The analysis unit can determine the priority of analysis based on the submission date of the recorded data during the analysis process. For example, the analysis unit will prioritize the analysis of recently submitted recorded data. For example, the analysis unit will postpone the analysis of older recorded data. For example, the analysis unit can dynamically adjust the analysis priority based on the submission date. This enables efficient analysis by determining the analysis priority based on the submission date.

[0102] The analysis unit can adjust the order of analysis based on the relevance of the recorded data during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant recorded data. For example, the analysis unit postpones the analysis of less relevant recorded data. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the recorded data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the recorded data.

[0103] The shooting unit can estimate the user's emotions and adjust the timing of shooting based on the estimated emotions. For example, if the user is relaxed, the shooting unit will take detailed shots. If the user is in a hurry, the shooting unit will take concise shots focusing on key points. If the user is excited, the shooting unit will take shots with visually stimulating effects. This reduces the burden on the user by adjusting the timing of shooting according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The camera unit can analyze the user's past shooting history and select the optimal shooting method during shooting. For example, the camera unit can automatically display locations that the user has frequently photographed in the past as candidates. For example, the camera unit can prioritize suggesting shooting methods that the user has used in the past (manual, voice, etc.). For example, the camera unit can predict and suggest a shooting method to be used at a specific time of day based on the user's past shooting history. In this way, by analyzing past shooting history, the camera unit can provide the optimal shooting method.

[0105] The camera unit can filter images based on the user's current projects and areas of interest during the shooting process. For example, the camera unit prioritizes capturing data related to the user's current project. For example, the camera unit filters and captures relevant data based on the user's areas of interest. For example, the camera unit suggests the optimal shooting method according to the progress of the user's project. This allows for the priority capture of highly relevant data by filtering based on the current project and areas of interest.

[0106] The camera unit can estimate the user's emotions and determine the priority of areas to photograph based on the estimated emotions. For example, if the user is stressed, the camera unit will prioritize photographing important areas. If the user is relaxed, the camera unit will prioritize photographing detailed areas. If the user is in a hurry, the camera unit will prioritize photographing areas that can be processed quickly. This allows for prioritizing important areas by determining the priority of areas to photograph according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The camera unit can prioritize capturing highly relevant locations by considering the user's geographical location information during shooting. For example, the camera unit will prioritize capturing locations related to the user's current location. For example, the camera unit will filter and capture relevant locations based on the user's geographical location information. For example, the camera unit will suggest the optimal shooting method based on the user's location information. In this way, by considering geographical location information, highly relevant locations can be prioritized for shooting.

[0108] The photography team can analyze the user's social media activity during shooting and photograph relevant areas. For example, the photography team can prioritize photographing relevant areas based on the user's social media activity. For example, the photography team can filter and photograph relevant areas based on the user's interests on social media. For example, the photography team can analyze the user's social media activity and suggest the optimal shooting method. This allows for prioritizing the photography of relevant areas by analyzing social media activity.

[0109] The information input unit can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, the information input unit will input information quickly to minimize effort. For example, if the user is relaxed, the information input unit will provide detailed explanations and input information carefully. For example, if the user is in a hurry, the information input unit will prioritize voice input and input information quickly. In this way, the user's burden is reduced by adjusting the timing of information input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The information input unit can analyze the user's past input history and select the optimal input method when information is entered. For example, the information input unit can automatically display information that the user has frequently entered in the past as a suggestion. For example, the information input unit can prioritize suggesting input methods that the user has used in the past (manual, voice, etc.). For example, the information input unit can predict and suggest input methods to be used during a specific time period based on the user's past input history. In this way, by analyzing past input history, the optimal input method can be provided.

[0111] The information input unit can filter information based on the user's current projects and areas of interest during input. For example, the information input unit prioritizes inputting information related to the user's current ongoing projects. For example, the information input unit filters and inputs relevant information based on the user's areas of interest. For example, the information input unit suggests the optimal information input method according to the progress of the user's projects. This allows for the priority input of highly relevant information by filtering based on the current projects and areas of interest.

[0112] The information input unit can estimate the user's emotions and determine the priority of the information to be input based on the estimated emotions. For example, if the user is stressed, the information input unit will prioritize inputting important information. For example, if the user is relaxed, the information input unit will prioritize inputting detailed information. For example, if the user is in a hurry, the information input unit will prioritize inputting information that can be processed quickly. In this way, by determining the priority of information according to the user's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The information input unit can prioritize inputting highly relevant information by considering the user's geographical location during information input. For example, the information input unit prioritizes inputting information related to the user's current location. For example, the information input unit filters and inputs relevant information based on the user's geographical location. For example, the information input unit suggests the optimal information input method based on the user's location. In this way, by considering geographical location information, highly relevant information can be prioritized for input.

[0114] The information input unit can analyze the user's social media activity and input relevant information during data entry. For example, the information input unit prioritizes inputting relevant information based on the user's social media activity. For example, the information input unit filters and inputs relevant information based on the user's interests on social media. For example, the information input unit analyzes the user's social media activity and suggests the optimal information input method. This allows for the priority input of relevant information by analyzing social media activity.

[0115] The information provider can estimate the user's emotions and adjust the timing of information delivery based on the estimated emotions. For example, if the user is stressed, the information provider will deliver information quickly to minimize the user's effort. For example, if the user is relaxed, the information provider will provide detailed explanations and deliver information carefully. For example, if the user is in a hurry, the information provider will prioritize voice input and deliver information quickly. In this way, the burden on the user is reduced by adjusting the timing of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The information delivery unit can analyze the user's past delivery history and select the optimal delivery method when providing information. For example, the unit can automatically display information that the user has frequently provided in the past as a candidate. For example, the unit can prioritize suggesting delivery methods that the user has used in the past (manual, voice, etc.). For example, the unit can predict and suggest a delivery method to be used during a specific time period based on the user's past delivery history. In this way, the optimal delivery method can be provided by analyzing past delivery history.

[0117] The information provider can filter information based on the user's current projects and areas of interest. For example, the provider can prioritize providing information related to the user's current projects. For example, the provider can filter and provide relevant information based on the user's areas of interest. For example, the provider can suggest the most appropriate information delivery method according to the progress of the user's projects. This allows for the priority provision of highly relevant information by filtering based on the user's current projects and areas of interest.

[0118] The information provider can estimate the user's emotions and prioritize the information to be provided based on those emotions. For example, if the user is stressed, the provider will prioritize providing important information. For example, if the user is relaxed, the provider will prioritize providing detailed information. For example, if the user is in a hurry, the provider will prioritize providing information that can be processed quickly. In this way, by prioritizing information according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The information provider can prioritize providing highly relevant information by considering the user's geographical location when providing information. For example, the provider can prioritize providing information related to the user's current location. For example, the provider can filter and provide relevant information based on the user's geographical location. For example, the provider can propose the optimal information provision method based on the user's location. In this way, by considering geographical location, highly relevant information can be prioritized.

[0120] The information provider can analyze the user's social media activity and provide relevant information when providing information. For example, the provider can prioritize providing relevant information based on the user's social media activity. For example, the provider can filter and provide relevant information based on the user's interests on social media. For example, the provider can analyze the user's social media activity and propose the optimal method of providing information. In this way, by analyzing social media activity, it is possible to prioritize providing relevant information.

[0121] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will provide detailed suggestions. If the user is in a hurry, the suggestion function will provide concise suggestions that get straight to the point. If the user is excited, the suggestion function will provide suggestions with visually stimulating effects. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The proposal department can adjust the level of detail in a proposal based on the importance of the information. For example, the proposal department will provide a detailed proposal for important information, and a concise proposal for less important information. The proposal department can dynamically adjust the level of detail in a proposal according to the importance of the information. This allows for more efficient proposals by adjusting the level of detail according to the importance of the information.

[0123] The proposal unit can apply different proposal algorithms depending on the category of information when making a proposal. For example, the proposal unit applies a specific proposal algorithm to information related to sales. For example, the proposal unit applies a different proposal algorithm to information related to repairs. For example, the proposal unit dynamically selects the optimal proposal algorithm depending on the category of information. This improves the accuracy of proposals by applying the most suitable proposal algorithm according to the category of information.

[0124] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion function will provide short, concise suggestions. If the user is relaxed, the suggestion function will provide detailed suggestions. If the user is excited, the suggestion function will provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The proposal department can prioritize proposals based on when the information was submitted. For example, the proposal department might prioritize recently submitted information. For example, the proposal department might postpone submitting older information. For example, the proposal department can dynamically adjust the priority of proposals based on the submission date. This allows for more efficient proposals by prioritizing proposals based on the submission date.

[0126] The proposal department can adjust the order of proposals based on the relevance of the information during the proposal process. For example, the proposal department will prioritize proposing highly relevant information. For example, the proposal department will postpone proposing less relevant information. For example, the proposal department can dynamically adjust the order of proposals based on the relevance of the information. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of the information.

[0127] The planning unit can estimate the user's emotions and adjust the way the plan is presented based on those emotions. For example, if the user is relaxed, the planning unit will provide a detailed plan. If the user is in a hurry, the planning unit will provide a concise plan that gets straight to the point. If the user is excited, the planning unit will provide a plan with visually stimulating effects. By adjusting the way the plan is presented according to the user's emotions, the system can provide a plan that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0128] The planning department can adjust the level of detail in a plan based on the importance of the information during the planning stage. For example, the planning department will create a detailed plan for important information. For example, the planning department will create a concise plan for less important information. The planning department can dynamically adjust the level of detail in a plan according to the importance of the information. This allows for efficient planning by adjusting the level of detail in a plan according to the importance of the information.

[0129] The planning department can apply different planning algorithms depending on the category of information during the planning stage. For example, the planning department applies a specific planning algorithm to information related to rebuilding. For example, the planning department applies a different planning algorithm to information related to repairs. For example, the planning department dynamically selects the optimal planning algorithm depending on the category of information. This improves the accuracy of the plan by applying the optimal planning algorithm according to the category of information.

[0130] The planning unit can estimate the user's emotions and adjust the length of the plan based on those emotions. For example, if the user is in a hurry, the planning unit will create a short, concise plan. If the user is relaxed, the planning unit will create a detailed plan. If the user is excited, the planning unit will create a plan with visually stimulating effects. By adjusting the length of the plan according to the user's emotions, the system can provide the user with the most suitable plan. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0131] The planning department can prioritize plans based on when information is submitted. For example, the planning department might prioritize recently submitted information. For example, the planning department might postpone planning older information. For example, the planning department can dynamically adjust plan priorities based on submission dates. This enables efficient planning by prioritizing plans based on submission dates.

[0132] The planning department can adjust the order of plans based on the relevance of information during the planning process. For example, the planning department prioritizes highly relevant information in its planning. For example, the planning department postpones planning less relevant information. For example, the planning department dynamically adjusts the order of plans based on the relevance of information. This allows for efficient planning by adjusting the order of plans based on the relevance of information.

[0133] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0134] The building information system can further estimate the user's emotions and customize its suggestions based on those emotions. For example, if the user is stressed, the suggestion system can provide concise and to-the-point suggestions. If the user is relaxed, the suggestion system can provide suggestions with detailed explanations. If the user is excited, the suggestion system can provide suggestions with visually appealing effects. This ensures that suggestions are tailored to the user's emotions, improving user satisfaction.

[0135] The building information system can further analyze the user's past behavior history to provide optimal suggestions. For example, it can prioritize displaying suggestions that the user has frequently selected in the past. It can also prioritize suggestion methods the user has used in the past (e.g., email, chat). Furthermore, it can predict and suggest the most suitable suggestion method for a specific time period based on the user's past behavior history. In this way, by analyzing past behavior history, the system provides the most suitable suggestions for the user.

[0136] The building information system can further filter suggestions based on the user's current projects and areas of interest. For example, it can prioritize displaying suggestions related to the user's current projects. It can also filter and display relevant suggestions based on the user's areas of interest. Furthermore, it can suggest the most suitable proposal method according to the progress of the user's project. As a result, highly relevant suggestions are provided by filtering based on the current project and areas of interest.

[0137] The building information provision system can further customize its suggestions by taking into account the user's geographical location. For example, it can prioritize displaying suggestions relevant to the user's current location. It can also filter and display relevant suggestions based on the user's geographical location. Furthermore, it can suggest the most appropriate suggestion method based on the user's location. In this way, highly relevant suggestions are provided by considering geographical location.

[0138] The building information system can further analyze users' social media activity and provide relevant suggestions. For example, it can prioritize displaying relevant suggestions based on users' social media activity. It can also filter and display relevant suggestions based on users' interests on social media. Furthermore, it can analyze users' social media activity and suggest the most appropriate suggestion method. In this way, relevant suggestions are provided by analyzing social media activity.

[0139] The building information system can also estimate the user's emotions and adjust the timing of information delivery based on those emotions. For example, if the user is stressed, the system can provide information quickly, minimizing the user's effort. If the user is relaxed, the system can provide detailed explanations and deliver information carefully. If the user is in a hurry, the system can prioritize voice input and provide information quickly. In this way, the system reduces the burden on the user by adjusting the timing of information delivery according to the user's emotions.

[0140] The building information system can further estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. If the user is excited, the analysis unit can provide analysis results with visually stimulating effects. By adjusting the presentation of the analysis according to the user's emotions, the system provides analysis results that are easy for the user to understand.

[0141] The building information system can also estimate the user's emotions and adjust the timing of photography based on those emotions. For example, if the user is relaxed, the photography unit can take detailed shots. If the user is in a hurry, the photography unit can take concise shots focusing on key points. If the user is excited, the photography unit can take shots with visually stimulating effects. By adjusting the timing of photography according to the user's emotions, the system reduces the burden on the user.

[0142] The building information system can also estimate the user's emotions and adjust the timing of information input based on those emotions. For example, if the user is stressed, the information input unit can input information quickly, minimizing the effort required. If the user is relaxed, the information input unit can provide detailed explanations and input information carefully. If the user is in a hurry, the information input unit can prioritize voice input and input information quickly. In this way, the system reduces the burden on the user by adjusting the timing of information input according to their emotions.

[0143] The building information system can further estimate the user's emotions and adjust the way the plan is presented based on those emotions. For example, if the user is relaxed, the planning system can provide a detailed plan. If the user is in a hurry, the planning system can provide a concise plan that gets straight to the point. If the user is excited, the planning system can provide a plan with visually stimulating effects. By adjusting the way the plan is presented according to the user's emotions, the system can provide a plan that is easy for the user to understand.

[0144] The following briefly describes the processing flow for example form 2.

[0145] Step 1: The reception desk inputs the video data. For example, they can input data from a rough recording of the building's exterior. Step 2: The analysis unit analyzes the recorded data entered by the reception unit and provides further instructions on areas that should be filmed. For example, it can specify walls and ceilings in common areas, or ceilings in individual rooms. Step 3: The imaging unit photographs the area indicated by the analysis unit. For example, it can photograph the indicated area at high resolution. Step 4: The information input section allows you to input basic information in addition to the additional video footage. For example, you can input basic information such as site area, year of construction, total floor area, and number of units. Step 5: The provisioning unit provides the information entered by the information input unit to multiple developers in a single batch. For example, the entered information can be sent to multiple developers in a digital format. Step 6: The proposal team will suggest the best course of action based on the information provided. For example, they may suggest selling or repairing the property. Step 7: The Planning Department jointly proposes a construction plan based on the actions proposed by the Proposal Department. For example, the developer and AI can collaborate to propose construction plans such as rebuilding, repair, or reinforcement.

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0148] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, shooting unit, information input unit, provision unit, proposal unit, and planning unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs data of a rough recording of the building's exterior. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the recorded data to indicate areas that should be photographed in more detail. The shooting unit is implemented by the camera 42 of the smart device 14 and photographs the indicated areas in high resolution. The information input unit is implemented by the control unit 46A of the smart device 14 and inputs basic information in addition to the additionally photographed video information. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and transmits the input information in digital format to multiple developers. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal action based on the provided information. The planning function is implemented by the specific processing unit 290 of the data processing device 12, where the developer and AI collaborate to propose construction plans such as rebuilding, repair, and reinforcement. The correspondence between each part and the devices and control units is not limited to the example described above and can be modified in various ways.

[0150] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0151] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, shooting unit, information input unit, provision unit, proposal unit, and planning unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and inputs data of a rough recording of the building's exterior. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the recorded data to indicate areas that should be photographed in more detail. The shooting unit is implemented by the camera 42 of the smart glasses 214 and photographs the indicated areas in high resolution. The information input unit is implemented by the control unit 46A of the smart glasses 214 and inputs basic information in addition to the additionally photographed video information. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and transmits the input information in digital format to multiple developers. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal action based on the provided information. The planning function is implemented by the specific processing unit 290 of the data processing device 12, where the developer and AI collaborate to propose construction plans such as rebuilding, repair, and reinforcement. The correspondence between each part and the devices and control units is not limited to the example described above and can be modified in various ways.

[0166] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0167] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the reception unit, analysis unit, shooting unit, information input unit, provision unit, proposal unit, and planning unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and inputs data of a rough recording of the building's exterior. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the recorded data, instructing the areas that should be photographed in more detail. The shooting unit is implemented by the camera 42 of the headset terminal 314 and photographs the instructed areas in high resolution. The information input unit is implemented by the control unit 46A of the headset terminal 314 and inputs basic information in addition to the additionally photographed video information. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and transmits the input information in digital format to multiple developers. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal action based on the provided information. The planning function is implemented by the specific processing unit 290 of the data processing device 12, where the developer and AI collaborate to propose construction plans such as rebuilding, repair, and reinforcement. The correspondence between each part and the devices and control units is not limited to the example described above and can be modified in various ways.

[0182] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0183] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0184] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0185] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0186] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0187] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0188] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0189] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0190] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0191] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0192] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0193] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0194] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0195] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0197] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0198] Each of the multiple elements described above, including the reception unit, analysis unit, shooting unit, information input unit, provision unit, proposal unit, and planning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and inputs data of a rough recording of the building's exterior. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the recorded data to indicate areas that should be photographed in more detail. The shooting unit is implemented by the camera 42 of the robot 414 and photographs the indicated areas at high resolution. The information input unit is implemented by the control unit 46A of the robot 414 and inputs basic information in addition to the additionally photographed video information. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and transmits the input information to multiple developers in digital format. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal action based on the provided information. The planning function is implemented by the specific processing unit 290 of the data processing device 12, where the developer and AI collaborate to propose construction plans such as rebuilding, repair, and reinforcement. The correspondence between each part and the devices and control units is not limited to the example described above and can be modified in various ways.

[0199] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0200] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0201] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0202] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0203] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0204] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0205] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0206] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0207] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0208] 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.

[0209] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0210] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0211] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0212] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0213] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0214] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0215] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0216] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0217] (Note 1) The reception area for inputting recorded data, The aforementioned reception unit analyzes the recorded data input and provides further instructions on which areas should be filmed, A shooting unit that photographs the location indicated by the analysis unit, An information input section for entering basic information in addition to the video information that has been filmed, A providing unit that provides the information entered by the aforementioned information input unit to multiple developers in a single batch, A proposal unit that proposes the optimal action based on the information provided by the aforementioned provision unit, The system comprises a planning department that jointly proposes a construction plan based on the actions proposed by the aforementioned proposal department. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Instruct them to photograph specific areas in more detail, such as the walls and ceilings of shared areas, and the ceilings of individual rooms. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned information input unit is Enter basic information such as site area, year of construction, total floor area, and number of units. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The information input unit provides the information entered to multiple developers in a single batch. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the information provided, we will propose the most suitable course of action, such as selling or repairing the item. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned planning department, Developers and AI collaborate to jointly propose construction plans for rebuilding, repairs, and reinforcements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of recording data reception based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving recorded data, the system analyzes the user's past recording history and selects the most suitable method of receiving the data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving recorded data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the recording data to be received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving recorded data, the system prioritizes receiving data that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving recorded data, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the recorded data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the recorded data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the video data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the recorded data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned imaging unit is During shooting, the system analyzes the user's past shooting history and selects the optimal shooting method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned imaging unit is During shooting, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned imaging unit is The system estimates the user's emotions and prioritizes the locations to photograph based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned imaging unit is During shooting, the system prioritizes capturing highly relevant locations by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned imaging unit is During shooting, the system analyzes the user's social media activity and captures relevant sections. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned information input unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned information input unit is When users enter information, the system analyzes their past input history and selects the most suitable input method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned information input unit is When entering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned information input unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned information input unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned information input unit is When entering information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of information delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, the system analyzes the user's past information provision history and selects the most suitable method of provision. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing information, we prioritize providing highly relevant information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned proposal section is, Apply different proposal algorithms according to the category of information at the time of proposal The system according to Addendum 1, characterized in that (Addendum 40) The proposal department Estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion The system according to Addendum 1, characterized in that (Addendum 41) The proposal department Determine the priority of the proposal based on the submission time of the information at the time of proposal The system according to Addendum 1, characterized in that (Addendum 42) The proposal department Adjust the order of the proposals based on the relevance of the information at the time of proposal The system according to Addendum 1, characterized in that (Addendum 43) The planning department Estimate the user's emotion and adjust the expression method of the plan based on the estimated user's emotion The system according to Addendum 1, characterized in that (Addendum 44) The planning department Adjust the detail level of the plan based on the importance of the information at the time of planning The system according to Addendum 1, characterized in that (Addendum 45) The planning department Apply different planning algorithms according to the category of information at the time of planning The system according to Addendum 1, characterized in that (Addendum 46) The planning department Estimate the user's emotion and adjust the length of the plan based on the estimated user's emotion The system according to Addendum 1, characterized in that (Addendum 47) The planning department Determine the priority of the plan based on the submission time of the information at the time of planning The system according to Addendum 1, characterized in that (Appended Note 48) The planning unit adjusts the order of planning based on the relevance of information during planning The system according to Appended Note 1, characterized by the above.

Explanation of Signs

[0218] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal <N 414 Robot

Claims

1. The reception area for inputting recorded data, The aforementioned reception unit analyzes the recorded data input and provides further instructions on which areas should be filmed, A shooting unit that photographs the location indicated by the analysis unit, An information input section for entering basic information in addition to the video information that has been filmed, A providing unit that provides the information entered by the aforementioned information input unit to multiple developers in a single batch, A proposal unit that proposes the optimal action based on the information provided by the aforementioned provision unit, The system comprises a planning department that jointly proposes a construction plan based on the actions proposed by the aforementioned proposal department. A system characterized by the following features.

2. The aforementioned analysis unit, Instruct them to photograph specific areas in more detail, such as the walls and ceilings of shared areas, and the ceilings of individual rooms. The system according to feature 1.

3. The aforementioned information input unit is Enter basic information such as site area, year of construction, total floor area, and number of units. The system according to feature 1.

4. The aforementioned supply unit is, The information input unit provides the information entered to multiple developers in a single batch. The system according to feature 1.

5. The aforementioned proposal section is, Based on the information provided, we will propose the most suitable course of action, such as selling or repairing the item. The system according to feature 1.

6. The aforementioned planning department, Developers and AI collaborate to jointly propose construction plans for rebuilding, repairs, and reinforcements. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of recording data reception based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving recorded data, the system analyzes the user's past recording history and selects the most suitable method of receiving the data. The system according to feature 1.

9. The aforementioned reception unit is When receiving recorded data, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the recording data to be received based on those estimated emotions. The system according to feature 1.

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