system

Abstract

The system according to this embodiment aims to clearly explain the necessity of the repair plan and to gain the understanding of the residents. [Solution] The system according to the embodiment comprises an analysis unit, an explanation unit, a proposal unit, and an interactive support unit. The analysis unit analyzes the necessity of each item included in the repair plan. The explanation unit clearly explains the rationale based on the results analyzed by the analysis unit. The proposal unit presents cost reduction proposals and gradual price increase plans based on the content explained by the explanation unit. The interactive support unit responds to questions from residents.

Description

Technical Field

[0006] , ,

[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, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 it is difficult to clearly explain the necessity and basis of the repair plan and obtain the understanding of residents.

[0005] [[ID=三十九]]

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, an explanation unit, a proposal unit, and an interactive support unit. The analysis unit analyzes the necessity of each item included in the repair plan. The explanation unit clearly explains the rationale based on the results analyzed by the analysis unit. The proposal unit presents cost reduction proposals and gradual price increase plans based on the content explained by the explanation unit. The interactive support unit responds to questions from residents. [Effects of the Invention]

[0007] The system according to this embodiment can clearly explain the necessity of the repair plan and gain the understanding of the residents. [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, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 repair plan support system according to an embodiment of the present invention is a system that provides an AI agent that analyzes the necessity of each item included in the repair plan and clearly explains the rationale for it. This repair plan support system refers to actual examples of other condominiums and market data to present cost reduction proposals and gradual price increase plans. It also provides an interactive support function that can respond to questions from residents at any time. For example, the repair plan support system analyzes the necessity of each item included in the repair plan. In this process, the AI ​​performs a detailed analysis of each repair item and clarifies its necessity. For example, if exterior wall repair is necessary, the AI ​​analyzes the deterioration status of the exterior wall and past repair history and clearly explains the rationale. Next, the repair plan support system refers to actual examples of other condominiums and market data to present cost reduction proposals and gradual price increase plans. For example, it presents repair cost reduction proposals based on repair examples and market data from other condominiums. It also simulates a gradual price increase plan and proposes it to residents and owners. Furthermore, the repair plan support system provides an interactive support function that can respond to questions from residents at any time. For example, when a resident inputs a question about the repair plan into the AI ​​agent, the AI ​​immediately provides information on the latest repair plan, cost breakdown, and progress. This system improves the transparency and credibility of the condominium repair plan, making it easier to reach agreements among residents and owners. Furthermore, it reduces the burden on residents and owners by proposing plans to reduce repair costs or gradually increase fees. In addition, the interactive support function allows for quick and clear answers to residents' questions. In this way, the repair plan support system improves the transparency and credibility of the repair plan and facilitates agreements among residents and owners.

[0029] The repair plan support system according to this embodiment comprises an analysis unit, an explanation unit, a proposal unit, and an interactive support unit. The analysis unit analyzes the necessity of each item included in the repair plan. The analysis unit, for example, performs a detailed analysis of each repair item and clarifies its necessity. For example, if the exterior wall needs repair, the analysis unit analyzes the deterioration status of the exterior wall and past repair history and clearly explains the basis for the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. The explanation unit explains the necessity of the repair items to residents and owners based on the results analyzed by the analysis unit. The explanation unit explains the necessity of the repair items to residents and owners based on the results analyzed by the analysis unit. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. The proposal unit presents cost reduction proposals and gradual price increase plans based on the content explained by the explanation unit. The proposal unit presents cost reduction proposals and gradual price increase plans by referring to other condominium case studies and market data, for example. Some or all of the above-described processes in the proposal section may be performed using AI, for example, or without AI. The interactive support section responds to questions from residents. The interactive support section immediately responds to questions from residents with information on the latest repair plan, cost breakdown, and progress. Some or all of the above-described processes in the interactive support section may be performed using AI, for example, or without AI. As a result, the repair plan support system according to the embodiment can analyze the necessity of the repair plan, clearly explain the rationale, present cost reduction proposals and gradual price increase plans, and respond to questions from residents.

[0030] The analysis unit analyzes the necessity of each item included in the repair plan. Specifically, if exterior wall repair is necessary, the analysis unit thoroughly analyzes the deterioration status of the exterior wall and past repair history, and clearly explains the rationale. For example, to evaluate the deterioration status of the exterior wall, image analysis technology is used to detect cracks and discoloration and quantify the degree of deterioration. It also refers to past repair history and predicts the timing of the next repair, taking into account the time elapsed since the last repair and the durability of the materials used. Furthermore, the analysis unit determines the priority of each repair item in order to optimize the repair plan for the entire building. For example, if exterior wall repair is more urgent than other repair items, the plan is adjusted to prioritize that repair. Some or all of the above processing in the analysis unit is often performed using AI. AI can rapidly analyze large amounts of data and predict deterioration patterns and the necessity of repairs with high accuracy. For example, deep learning technology is used to automatically detect signs of deterioration from exterior wall image data and evaluate the necessity of repairs. Furthermore, the AI ​​can learn from past repair data and market data to propose the optimal repair plan. This allows the analysis unit to scientifically and objectively analyze the necessity of the repair plan and clearly explain the rationale.

[0031] The explanatory department explains the necessity of repair items to residents and owners based on the results analyzed by the analysis department. Specifically, the explanatory department organizes the data and analysis results provided by the analysis department in an easy-to-understand manner and presents them to residents and owners. For example, it uses images and graphs showing the deterioration of the exterior walls to visually explain the progress of deterioration and the necessity of repairs. It also provides information on past repair history and the durability of materials used to clarify the basis for the next repair timing. It is important for the explanatory department to avoid technical jargon and explain in simple language so that residents and owners can easily understand the necessity of repairs. Furthermore, the explanatory department carefully answers questions from residents and owners to alleviate doubts and concerns. For example, it provides detailed information and satisfactory explanations in response to questions about the specific content, cost, and duration of the repairs. Some or all of the above processing in the explanatory department may be performed using AI. AI can automatically organize the analysis results and generate presentation materials. For example, AI can automatically create images and graphs showing the deterioration of the exterior walls to visually explain the necessity of repairs. Furthermore, the AI ​​can quickly and accurately answer questions from residents and owners. This allows the explanatory department to explain the analysis results in an easy-to-understand manner, deepening the understanding of residents and owners.

[0032] The proposal department presents cost reduction plans and gradual price increase plans based on the information presented by the explanation department. Specifically, the proposal department refers to other condominium case studies and market data to formulate optimal cost reduction plans and gradual price increase plans. For example, they analyze successful repair plans implemented in other condominiums and propose ways to reduce costs by applying similar methods. They also investigate market prices for necessary materials and construction costs based on market data and develop plans to achieve optimal cost performance. It is also important for the proposal department to present gradual price increase plans to minimize the burden on residents and owners. For example, instead of bearing the full cost of repairs all at once, they reduce the financial burden on residents and owners by gradually increasing prices over several years. Some or all of the above processes in the proposal department may be performed using AI. AI can quickly analyze large amounts of data and propose optimal cost reduction plans and gradual price increase plans. For example, AI can learn from other condominium case studies and market data and automatically generate optimal repair plans. Furthermore, AI can continuously improve the proposals based on feedback from residents and owners. This allows the proposal department to effectively present cost reduction plans and gradual price increase plans, minimizing the burden on residents and owners.

[0033] The Interactive Support Department handles inquiries from residents. Specifically, it provides immediate answers to residents' questions regarding the latest repair plans, cost breakdowns, and progress. For example, if a resident asks about the details or costs of a repair plan, the Interactive Support Department retrieves the necessary information from its up-to-date database and provides a quick response. It also provides accurate answers to questions about the progress of repairs based on the latest information from the field. It is important for the Interactive Support Department to utilize multiple channels to facilitate smooth communication with residents. For example, it responds to residents' questions via telephone, email, and chatbots. In particular, using a chatbot makes it possible to respond to residents' questions 24 hours a day, 365 days a year. Some or all of the above processing in the Interactive Support Department may be performed using AI. AI can automatically analyze residents' questions and provide the most appropriate answers. For example, it can use natural language processing technology to understand the content of residents' questions and generate appropriate answers. AI can also learn from past question history and provide customized answers tailored to residents' needs. This allows the interactive support department to respond quickly and accurately to residents' questions and provide information about the repair plan.

[0034] The analysis unit can perform a detailed analysis of each repair item and clarify its necessity. For example, if the exterior wall needs repair, the analysis unit will analyze the deterioration status of the exterior wall and its past repair history, and clearly explain the rationale. This improves the reliability of the repair plan by performing a detailed analysis of each repair item and clarifying its necessity. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0035] The explanation unit can explain the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. For example, the explanation unit explains the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. For example, the explanation unit explains the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. This improves the transparency of the repair plan by explaining the necessity of repair items to residents and owners. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without using AI.

[0036] The proposal department can refer to other condominium case studies and market data to propose cost reduction plans and gradual price increase plans. The proposal department can, for example, refer to other condominium case studies and market data to propose cost reduction plans and gradual price increase plans. The proposal department can, for example, refer to other condominium case studies and market data to propose cost reduction plans and gradual price increase plans. This allows the proposal department to propose cost reduction plans and gradual price increase plans by referring to other condominium case studies and market data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0037] The interactive support unit can immediately answer residents' questions with information on the latest repair plan, cost breakdown, and progress. For example, the interactive support unit can immediately answer residents' questions with information on the latest repair plan, cost breakdown, and progress. This allows for immediate answers to residents' questions, thereby improving their understanding of and acceptance of the repair plan. Some or all of the above-described processes in the interactive support unit may be performed using AI, for example, or without AI.

[0038] The analysis unit can analyze the past repair history of each repair item in detail and predict the future need for repairs. For example, the analysis unit can analyze the repair history of the past 10 years and predict the timing of the next repair. For example, the analysis unit can analyze the rate of deterioration of each repair item and predict the future need for repairs. For example, the analysis unit can refer to the repair history of other condominiums to improve the accuracy of the repair need prediction. In this way, by analyzing past repair history, the future need for repairs can be predicted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0039] The analysis unit can monitor the deterioration status of repair items in real time and immediately reflect changes in their necessity. For example, the analysis unit uses sensors to monitor the deterioration status of exterior walls in real time. For example, the analysis unit immediately analyzes changes in the deterioration status and reflects them in the repair plan. For example, the analysis unit saves the deterioration status data to the cloud and makes it accessible at any time. This improves the accuracy of the repair plan by monitoring the deterioration status in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0040] The analysis unit can improve the accuracy of its analysis by referring to the repair history of other condominiums when analyzing repair items. For example, the analysis unit can refer to the repair history database of other condominiums and compare the analysis results. For example, the analysis unit can improve the accuracy of its analysis by referring to successful repair cases in other condominiums. For example, the analysis unit can analyze failed repair cases in other condominiums and perform analysis to avoid risks. In this way, the accuracy of the analysis is improved by referring to the repair history of other condominiums. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0041] The analysis unit can predict the progression of deterioration based on weather data when analyzing repair items. For example, the analysis unit predicts the rate of deterioration of exterior walls based on past weather data. For example, the analysis unit acquires weather data in real time and predicts the progression of deterioration. For example, the analysis unit combines weather data and repair history to predict the progression of deterioration with high accuracy. As a result, the accuracy of the repair plan is improved by predicting the progression of deterioration based on weather data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0042] The explanation unit can adjust the level of detail in its explanations based on the importance of the repair items. For example, it can provide detailed explanations for high-priority repair items and concise explanations for low-priority repair items. It can also adjust the order of explanations according to importance. By adjusting the level of detail in the explanations based on the importance of the repair items, residents' understanding is enhanced. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI.

[0043] The explanation unit can apply different explanation algorithms depending on the residents' level of understanding during the explanation. For example, if the residents have a high level of understanding, the explanation unit will use specialized terminology. For example, if the residents have a low level of understanding, the explanation unit will use simple language. For example, the explanation unit will adjust the level of detail in the explanation depending on the residents' level of understanding. In this way, by applying an explanation algorithm according to the residents' level of understanding, the residents' understanding will be deepened. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without using AI.

[0044] The explanation department can determine the priority of explanations based on the submission timing of repair items during the explanation. For example, the explanation department will prioritize explaining repair items that have been submitted earlier. For example, the explanation department will postpone explaining repair items that have been submitted later. For example, the explanation department will adjust the order of explanations according to the submission timing. This will deepen residents' understanding by determining the priority of explanations based on the submission timing of repair items. Some or all of the above processing in the explanation department may be performed using AI, for example, or not using AI.

[0045] The explanatory unit can adjust the order of explanations based on the relationships between repair items during the explanation. For example, the explanatory unit will explain highly related repair items together. For example, the explanatory unit will explain less related repair items individually. The explanatory unit will adjust the order of explanations according to their relationships. This will deepen residents' understanding by adjusting the order of explanations based on the relationships between repair items. Some or all of the above processing in the explanatory unit may be performed using AI, for example, or without using AI.

[0046] The proposal department can adjust the level of detail in its proposals based on the importance of the repair items. For example, the proposal department will provide detailed proposals for high-priority repair items, and concise proposals for low-priority repair items. The proposal department will also adjust the order of proposals according to their importance. By adjusting the level of detail in proposals based on the importance of the repair items, residents' understanding will be enhanced. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI.

[0047] The proposal unit can apply different proposal algorithms depending on the category of the repair item when making a proposal. For example, for exterior wall repairs, the proposal unit will make a proposal that is appropriate to the degree of deterioration. For example, for roof repairs, the proposal unit will make a proposal that takes weather data into consideration. For example, for interior repairs, the proposal unit will make a proposal that reflects the wishes of the residents. This allows for the provision of the most appropriate proposal for each category of repair item. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0048] The proposal department can determine the priority of proposals based on the submission timing of repair items. For example, the proposal department will prioritize proposals for repair items submitted earlier. For example, the proposal department will postpone proposals for repair items submitted later. For example, the proposal department will adjust the order of proposals according to the submission timing. This will deepen residents' understanding by determining the priority of proposals based on the submission timing of repair items. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0049] The proposal department can adjust the order of proposals based on the relationships between repair items. For example, the proposal department may group together highly related repair items in its proposals. For example, it may propose individually for less related repair items. The proposal department can adjust the order of proposals according to their relationships. This allows residents to better understand the project by adjusting the order of proposals based on the relationships between repair items. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI.

[0050] The interactive support unit can provide the best possible answer by referring to the resident's past question history during support. For example, the interactive support unit provides the best answer based on the content of questions the resident has asked in the past. For example, the interactive support unit provides relevant information from the resident's past question history. For example, the interactive support unit analyzes the resident's past question history to provide the most efficient support. This allows the unit to provide the best possible answer by referring to the resident's past question history. Some or all of the above processes in the interactive support unit may be performed using AI, for example, or without using AI.

[0051] The interactive support unit can apply different support algorithms depending on the resident's level of understanding during support. For example, if the resident has a high level of understanding, the interactive support unit will provide support using specialized terminology. If the resident has a low level of understanding, the interactive support unit will provide support using simple language. For example, the interactive support unit will adjust the level of detail of support according to the resident's level of understanding. In this way, by applying support algorithms according to the resident's level of understanding, the resident's understanding will be deepened. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without using AI.

[0052] The interactive support unit can select the optimal support method by considering the resident's device information during support. For example, if the resident is using a smartphone, the interactive support unit provides a support method that is adapted to the screen size. For example, if the resident is using a tablet, the interactive support unit provides a support method optimized for a large screen. For example, if the resident is using a smartwatch, the interactive support unit provides a concise and highly visible support method. In this way, the optimal support method can be provided by considering the resident's device information. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without using AI.

[0053] The interactive support unit can provide highly relevant information by considering the resident's geographical location during support. For example, the interactive support unit can provide information on the nearest repair company based on the resident's current location. For example, the interactive support unit can provide support by referring to local repair examples based on the resident's geographical location. For example, the interactive support unit can propose an optimal repair plan by considering the resident's geographical location. In this way, highly relevant information can be provided by considering the resident's geographical location. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without using AI.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can improve the accuracy of its analysis by referring to the repair history of other condominiums when performing a detailed analysis of each repair item. For example, it can refer to the repair history database of other condominiums and compare the analysis results. It can also improve the accuracy of the analysis by referring to successful repair cases in other condominiums. Furthermore, it can analyze failed repair cases in other condominiums and perform analyses to avoid risks. In this way, the accuracy of the analysis is improved by referring to the repair history of other condominiums. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0056] The analysis unit can monitor the deterioration status of repair items in real time and immediately reflect changes in their necessity. For example, it can use sensors to monitor the deterioration status of exterior walls in real time. It can also immediately analyze changes in the deterioration status and reflect them in the repair plan. Furthermore, it can save the deterioration status data to the cloud and make it accessible at any time. This improves the accuracy of the repair plan by monitoring the deterioration status in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0057] The explanation unit can adjust the level of detail in the explanation based on the importance of the repair items. For example, it can provide detailed explanations for high-priority repair items, and concise explanations for low-priority items. Furthermore, it can adjust the order of the explanations according to their importance. By adjusting the level of detail in the explanation based on the importance of the repair items, residents' understanding is deepened. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without using AI.

[0058] The proposal department can apply different proposal algorithms depending on the category of repair item when making a proposal. For example, for exterior wall repairs, it can make proposals that are appropriate to the degree of deterioration. For roof repairs, it can make proposals that take weather data into consideration. Furthermore, for interior repairs, it can make proposals that reflect the residents' requests. This allows for the provision of optimal proposals according to the category of repair item. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI.

[0059] The interactive support unit can provide the best possible answer by referring to the resident's past question history during support. For example, it can provide the best answer based on the content of questions the resident has asked in the past. It can also provide relevant information from the resident's past question history. Furthermore, it can analyze the resident's past question history to provide the most efficient support. In this way, the best possible answer can be provided by referring to the resident's past question history. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without using AI.

[0060] The interactive support unit can select the optimal support method by considering the resident's device information during support. For example, if the resident is using a smartphone, it can provide a support method adapted to the screen size. If the resident is using a tablet, it can provide a support method optimized for the larger screen. Furthermore, if the resident is using a smartwatch, it can provide a concise and highly visible support method. In this way, the optimal support method can be provided by considering the resident's device information. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without AI.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The analysis unit analyzes the necessity of each item included in the repair plan. For example, if exterior wall repairs are necessary, it analyzes the deterioration status of the exterior wall and past repair history to clarify the rationale. The processing in the analysis unit may or may not be performed using AI. Step 2: The explanation unit explains the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. The processing in the explanation unit may or may not be performed using AI. Step 3: The proposal department presents cost reduction proposals and gradual price increase plans based on the information presented by the explanation department. The proposal department refers to other condominium case studies and market data when presenting cost reduction proposals and gradual price increase plans. The processing in the proposal department may or may not be performed using AI. Step 4: The Interactive Support Department responds to residents' questions. For example, it provides immediate answers to residents' questions regarding the latest repair plans, cost breakdowns, and progress. Processing by the Interactive Support Department may or may not be done using AI.

[0063] (Example of form 2) The repair plan support system according to an embodiment of the present invention is a system that provides an AI agent that analyzes the necessity of each item included in the repair plan and clearly explains the rationale for it. This repair plan support system refers to actual examples of other condominiums and market data to present cost reduction proposals and gradual price increase plans. It also provides an interactive support function that can respond to questions from residents at any time. For example, the repair plan support system analyzes the necessity of each item included in the repair plan. In this process, the AI ​​performs a detailed analysis of each repair item and clarifies its necessity. For example, if exterior wall repair is necessary, the AI ​​analyzes the deterioration status of the exterior wall and past repair history and clearly explains the rationale. Next, the repair plan support system refers to actual examples of other condominiums and market data to present cost reduction proposals and gradual price increase plans. For example, it presents repair cost reduction proposals based on repair examples and market data from other condominiums. It also simulates a gradual price increase plan and proposes it to residents and owners. Furthermore, the repair plan support system provides an interactive support function that can respond to questions from residents at any time. For example, when a resident inputs a question about the repair plan into the AI ​​agent, the AI ​​immediately provides information on the latest repair plan, cost breakdown, and progress. This system improves the transparency and credibility of the condominium repair plan, making it easier to reach agreements among residents and owners. Furthermore, it reduces the burden on residents and owners by proposing plans to reduce repair costs or gradually increase fees. In addition, the interactive support function allows for quick and clear answers to residents' questions. In this way, the repair plan support system improves the transparency and credibility of the repair plan and facilitates agreements among residents and owners.

[0064] The repair plan support system according to this embodiment comprises an analysis unit, an explanation unit, a proposal unit, and an interactive support unit. The analysis unit analyzes the necessity of each item included in the repair plan. The analysis unit, for example, performs a detailed analysis of each repair item and clarifies its necessity. For example, if the exterior wall needs repair, the analysis unit analyzes the deterioration status of the exterior wall and past repair history and clearly explains the basis for the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. The explanation unit explains the necessity of the repair items to residents and owners based on the results analyzed by the analysis unit. The explanation unit explains the necessity of the repair items to residents and owners based on the results analyzed by the analysis unit. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. The proposal unit presents cost reduction proposals and gradual price increase plans based on the content explained by the explanation unit. The proposal unit presents cost reduction proposals and gradual price increase plans by referring to other condominium case studies and market data, for example. Some or all of the above-described processes in the proposal section may be performed using AI, for example, or without AI. The interactive support section responds to questions from residents. The interactive support section immediately responds to questions from residents with information on the latest repair plan, cost breakdown, and progress. Some or all of the above-described processes in the interactive support section may be performed using AI, for example, or without AI. As a result, the repair plan support system according to the embodiment can analyze the necessity of the repair plan, clearly explain the rationale, present cost reduction proposals and gradual price increase plans, and respond to questions from residents.

[0065] The analysis unit analyzes the necessity of each item included in the repair plan. Specifically, if exterior wall repair is necessary, the analysis unit thoroughly analyzes the deterioration status of the exterior wall and past repair history, and clearly explains the rationale. For example, to evaluate the deterioration status of the exterior wall, image analysis technology is used to detect cracks and discoloration and quantify the degree of deterioration. It also refers to past repair history and predicts the timing of the next repair, taking into account the time elapsed since the last repair and the durability of the materials used. Furthermore, the analysis unit determines the priority of each repair item in order to optimize the repair plan for the entire building. For example, if exterior wall repair is more urgent than other repair items, the plan is adjusted to prioritize that repair. Some or all of the above processing in the analysis unit is often performed using AI. AI can rapidly analyze large amounts of data and predict deterioration patterns and the necessity of repairs with high accuracy. For example, deep learning technology is used to automatically detect signs of deterioration from exterior wall image data and evaluate the necessity of repairs. Furthermore, the AI ​​can learn from past repair data and market data to propose the optimal repair plan. This allows the analysis unit to scientifically and objectively analyze the necessity of the repair plan and clearly explain the rationale.

[0066] The explanatory department explains the necessity of repair items to residents and owners based on the results analyzed by the analysis department. Specifically, the explanatory department organizes the data and analysis results provided by the analysis department in an easy-to-understand manner and presents them to residents and owners. For example, it uses images and graphs showing the deterioration of the exterior walls to visually explain the progress of deterioration and the necessity of repairs. It also provides information on past repair history and the durability of materials used to clarify the basis for the next repair timing. It is important for the explanatory department to avoid technical jargon and explain in simple language so that residents and owners can easily understand the necessity of repairs. Furthermore, the explanatory department carefully answers questions from residents and owners to alleviate doubts and concerns. For example, it provides detailed information and satisfactory explanations in response to questions about the specific content, cost, and duration of the repairs. Some or all of the above processing in the explanatory department may be performed using AI. AI can automatically organize the analysis results and generate presentation materials. For example, AI can automatically create images and graphs showing the deterioration of the exterior walls to visually explain the necessity of repairs. Furthermore, the AI ​​can quickly and accurately answer questions from residents and owners. This allows the explanatory department to explain the analysis results in an easy-to-understand manner, deepening the understanding of residents and owners.

[0067] The proposal department presents cost reduction plans and gradual price increase plans based on the information presented by the explanation department. Specifically, the proposal department refers to other condominium case studies and market data to formulate optimal cost reduction plans and gradual price increase plans. For example, they analyze successful repair plans implemented in other condominiums and propose ways to reduce costs by applying similar methods. They also investigate market prices for necessary materials and construction costs based on market data and develop plans to achieve optimal cost performance. It is also important for the proposal department to present gradual price increase plans to minimize the burden on residents and owners. For example, instead of bearing the full cost of repairs all at once, they reduce the financial burden on residents and owners by gradually increasing prices over several years. Some or all of the above processes in the proposal department may be performed using AI. AI can quickly analyze large amounts of data and propose optimal cost reduction plans and gradual price increase plans. For example, AI can learn from other condominium case studies and market data and automatically generate optimal repair plans. Furthermore, AI can continuously improve the proposals based on feedback from residents and owners. This allows the proposal department to effectively present cost reduction plans and gradual price increase plans, minimizing the burden on residents and owners.

[0068] The Interactive Support Department handles inquiries from residents. Specifically, it provides immediate answers to residents' questions regarding the latest repair plans, cost breakdowns, and progress. For example, if a resident asks about the details or costs of a repair plan, the Interactive Support Department retrieves the necessary information from its up-to-date database and provides a quick response. It also provides accurate answers to questions about the progress of repairs based on the latest information from the field. It is important for the Interactive Support Department to utilize multiple channels to facilitate smooth communication with residents. For example, it responds to residents' questions via telephone, email, and chatbots. In particular, using a chatbot makes it possible to respond to residents' questions 24 hours a day, 365 days a year. Some or all of the above processing in the Interactive Support Department may be performed using AI. AI can automatically analyze residents' questions and provide the most appropriate answers. For example, it can use natural language processing technology to understand the content of residents' questions and generate appropriate answers. AI can also learn from past question history and provide customized answers tailored to residents' needs. This allows the interactive support department to respond quickly and accurately to residents' questions and provide information about the repair plan.

[0069] The analysis unit can perform a detailed analysis of each repair item and clarify its necessity. For example, if the exterior wall needs repair, the analysis unit will analyze the deterioration status of the exterior wall and its past repair history, and clearly explain the rationale. This improves the reliability of the repair plan by performing a detailed analysis of each repair item and clarifying its necessity. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0070] The explanation unit can explain the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. For example, the explanation unit explains the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. For example, the explanation unit explains the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. This improves the transparency of the repair plan by explaining the necessity of repair items to residents and owners. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without using AI.

[0071] The proposal department can refer to other condominium case studies and market data to propose cost reduction plans and gradual price increase plans. The proposal department can, for example, refer to other condominium case studies and market data to propose cost reduction plans and gradual price increase plans. The proposal department can, for example, refer to other condominium case studies and market data to propose cost reduction plans and gradual price increase plans. This allows the proposal department to propose cost reduction plans and gradual price increase plans by referring to other condominium case studies and market data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0072] The interactive support unit can immediately answer residents' questions with information on the latest repair plan, cost breakdown, and progress. For example, the interactive support unit can immediately answer residents' questions with information on the latest repair plan, cost breakdown, and progress. This allows for immediate answers to residents' questions, thereby improving their understanding of and acceptance of the repair plan. Some or all of the above-described processes in the interactive support unit may be performed using AI, for example, or without AI.

[0073] The analysis unit can estimate the emotions of residents and adjust the presentation method of the analysis results based on the estimated emotions. For example, if a resident is feeling anxious, the analysis unit will present the results using detailed data and graphs. For example, if a resident is feeling agitated, the analysis unit will present the results in a concise and to-the-point manner. For example, if a resident is relaxed, the analysis unit will present the results using a visually appealing infographic. By adjusting the presentation method of the analysis results according to the emotions of the residents, their understanding and acceptance are improved. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0074] The analysis unit can analyze the past repair history of each repair item in detail and predict the future need for repairs. For example, the analysis unit can analyze the repair history of the past 10 years and predict the timing of the next repair. For example, the analysis unit can analyze the rate of deterioration of each repair item and predict the future need for repairs. For example, the analysis unit can refer to the repair history of other condominiums to improve the accuracy of the repair need prediction. In this way, by analyzing past repair history, the future need for repairs can be predicted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0075] The analysis unit can monitor the deterioration status of repair items in real time and immediately reflect changes in their necessity. For example, the analysis unit uses sensors to monitor the deterioration status of exterior walls in real time. For example, the analysis unit immediately analyzes changes in the deterioration status and reflects them in the repair plan. For example, the analysis unit saves the deterioration status data to the cloud and makes it accessible at any time. This improves the accuracy of the repair plan by monitoring the deterioration status in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0076] The analysis unit can estimate the residents' emotions and determine the priority of the analysis based on the estimated emotions. For example, if a resident is feeling anxious, the analysis unit will prioritize the analysis of the most important repair items. For example, if a resident is agitated, the analysis unit will present the entire repair plan at once. For example, if a resident is relaxed, the analysis unit will present the analysis results in stages. This allows for analysis tailored to the residents' interests by determining the priority of the analysis according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0077] The analysis unit can improve the accuracy of its analysis by referring to the repair history of other condominiums when analyzing repair items. For example, the analysis unit can refer to the repair history database of other condominiums and compare the analysis results. For example, the analysis unit can improve the accuracy of its analysis by referring to successful repair cases in other condominiums. For example, the analysis unit can analyze failed repair cases in other condominiums and perform analysis to avoid risks. In this way, the accuracy of the analysis is improved by referring to the repair history of other condominiums. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0078] The analysis unit can predict the progression of deterioration based on weather data when analyzing repair items. For example, the analysis unit predicts the rate of deterioration of exterior walls based on past weather data. For example, the analysis unit acquires weather data in real time and predicts the progression of deterioration. For example, the analysis unit combines weather data and repair history to predict the progression of deterioration with high accuracy. As a result, the accuracy of the repair plan is improved by predicting the progression of deterioration based on weather data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0079] The explanatory unit can estimate the residents' emotions and adjust the way it presents the explanation based on those estimated emotions. For example, if a resident is feeling anxious, the explanatory unit will use detailed data and graphs in its explanation. If a resident is excited, the explanatory unit will provide a concise and to-the-point explanation. If a resident is relaxed, the explanatory unit will use visually appealing infographics in its explanation. By adjusting the way the explanation is presented according to the resident's emotions, the resident's understanding and acceptance are improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the explanatory unit may be performed using AI or not using AI.

[0080] The explanation unit can adjust the level of detail in its explanations based on the importance of the repair items. For example, it can provide detailed explanations for high-priority repair items and concise explanations for low-priority repair items. It can also adjust the order of explanations according to importance. By adjusting the level of detail in the explanations based on the importance of the repair items, residents' understanding is enhanced. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI.

[0081] The explanation unit can apply different explanation algorithms depending on the residents' level of understanding during the explanation. For example, if the residents have a high level of understanding, the explanation unit will use specialized terminology. For example, if the residents have a low level of understanding, the explanation unit will use simple language. For example, the explanation unit will adjust the level of detail in the explanation depending on the residents' level of understanding. In this way, by applying an explanation algorithm according to the residents' level of understanding, the residents' understanding will be deepened. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without using AI.

[0082] The explanatory unit can estimate the emotions of residents and adjust the length of the explanation based on the estimated emotions. For example, if a resident is feeling anxious, the explanatory unit will provide a detailed explanation. For example, if a resident is excited, the explanatory unit will provide a concise explanation. For example, if a resident is relaxed, the explanatory unit will provide an explanation of appropriate length. By adjusting the length of the explanation according to the emotions of the residents, their understanding and acceptance are improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanatory unit may be performed using AI, for example, or without AI.

[0083] The explanation department can determine the priority of explanations based on the submission timing of repair items during the explanation. For example, the explanation department will prioritize explaining repair items that have been submitted earlier. For example, the explanation department will postpone explaining repair items that have been submitted later. For example, the explanation department will adjust the order of explanations according to the submission timing. This will deepen residents' understanding by determining the priority of explanations based on the submission timing of repair items. Some or all of the above processing in the explanation department may be performed using AI, for example, or not using AI.

[0084] The explanatory unit can adjust the order of explanations based on the relationships between repair items during the explanation. For example, the explanatory unit will explain highly related repair items together. For example, the explanatory unit will explain less related repair items individually. The explanatory unit will adjust the order of explanations according to their relationships. This will deepen residents' understanding by adjusting the order of explanations based on the relationships between repair items. Some or all of the above processing in the explanatory unit may be performed using AI, for example, or without using AI.

[0085] The proposal unit can estimate residents' emotions and adjust the presentation of the proposal based on the estimated emotions. For example, if residents are feeling anxious, the proposal unit will use detailed data and graphs in its proposal. If residents are excited, the proposal unit will make a concise and to-the-point proposal. If residents are relaxed, the proposal unit will use visually appealing infographics in its proposal. By adjusting the presentation of the proposal according to residents' emotions, the unit can improve residents' understanding and acceptance. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not using AI.

[0086] The proposal department can adjust the level of detail in its proposals based on the importance of the repair items. For example, the proposal department will provide detailed proposals for high-priority repair items, and concise proposals for low-priority repair items. The proposal department will also adjust the order of proposals according to their importance. By adjusting the level of detail in proposals based on the importance of the repair items, residents' understanding will be enhanced. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI.

[0087] The proposal unit can apply different proposal algorithms depending on the category of the repair item when making a proposal. For example, for exterior wall repairs, the proposal unit will make a proposal that is appropriate to the degree of deterioration. For example, for roof repairs, the proposal unit will make a proposal that takes weather data into consideration. For example, for interior repairs, the proposal unit will make a proposal that reflects the wishes of the residents. This allows for the provision of the most appropriate proposal for each category of repair item. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0088] The proposal unit can estimate the residents' emotions and adjust the length of the proposal based on the estimated emotions. For example, if a resident is feeling anxious, the proposal unit will provide a detailed proposal. For example, if a resident is excited, the proposal unit will provide a concise proposal. For example, if a resident is relaxed, the proposal unit will provide a proposal of appropriate length. By adjusting the length of the proposal according to the resident's emotions, the resident's understanding and acceptance will be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI.

[0089] The proposal department can determine the priority of proposals based on the submission timing of repair items. For example, the proposal department will prioritize proposals for repair items submitted earlier. For example, the proposal department will postpone proposals for repair items submitted later. For example, the proposal department will adjust the order of proposals according to the submission timing. This will deepen residents' understanding by determining the priority of proposals based on the submission timing of repair items. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0090] The proposal department can adjust the order of proposals based on the relationships between repair items. For example, the proposal department may group together highly related repair items in its proposals. For example, it may propose individually for less related repair items. The proposal department can adjust the order of proposals according to their relationships. This allows residents to better understand the project by adjusting the order of proposals based on the relationships between repair items. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI.

[0091] The interactive support unit can estimate the emotions of residents and adjust the way support is presented based on the estimated emotions. For example, if a resident is feeling anxious, the interactive support unit will provide support using detailed data and graphs. For example, if a resident is agitated, the interactive support unit will provide concise and to-the-point support. For example, if a resident is relaxed, the interactive support unit will provide support using visually appealing infographics. By adjusting the way support is presented according to the resident's emotions, the resident's understanding and acceptance are improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without AI.

[0092] The interactive support unit can provide the best possible answer by referring to the resident's past question history during support. For example, the interactive support unit provides the best answer based on the content of questions the resident has asked in the past. For example, the interactive support unit provides relevant information from the resident's past question history. For example, the interactive support unit analyzes the resident's past question history to provide the most efficient support. This allows the unit to provide the best possible answer by referring to the resident's past question history. Some or all of the above processes in the interactive support unit may be performed using AI, for example, or without using AI.

[0093] The interactive support unit can apply different support algorithms depending on the resident's level of understanding during support. For example, if the resident has a high level of understanding, the interactive support unit will provide support using specialized terminology. If the resident has a low level of understanding, the interactive support unit will provide support using simple language. For example, the interactive support unit will adjust the level of detail of support according to the resident's level of understanding. In this way, by applying support algorithms according to the resident's level of understanding, the resident's understanding will be deepened. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without using AI.

[0094] The interactive support unit can estimate the emotions of residents and adjust the length of support based on the estimated emotions. For example, if a resident is feeling anxious, the interactive support unit will provide detailed support. For example, if a resident is agitated, the interactive support unit will provide concise support. For example, if a resident is relaxed, the interactive support unit will provide support of an appropriate length. By adjusting the length of support according to the resident's emotions, the resident's understanding and sense of satisfaction are improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without using AI.

[0095] The interactive support unit can select the optimal support method by considering the resident's device information during support. For example, if the resident is using a smartphone, the interactive support unit provides a support method that is adapted to the screen size. For example, if the resident is using a tablet, the interactive support unit provides a support method optimized for a large screen. For example, if the resident is using a smartwatch, the interactive support unit provides a concise and highly visible support method. In this way, the optimal support method can be provided by considering the resident's device information. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without using AI.

[0096] The interactive support unit can provide highly relevant information by considering the resident's geographical location during support. For example, the interactive support unit can provide information on the nearest repair company based on the resident's current location. For example, the interactive support unit can provide support by referring to local repair examples based on the resident's geographical location. For example, the interactive support unit can propose an optimal repair plan by considering the resident's geographical location. In this way, highly relevant information can be provided by considering the resident's geographical location. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without using AI.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The analysis unit can estimate residents' emotions when analyzing the necessity of repair items and adjust the presentation method of the analysis results based on the estimated emotions. For example, if residents are feeling anxious, the analysis results can be presented using detailed data and graphs. If residents are agitated, the analysis results can be presented concisely and to the point. Furthermore, if residents are relaxed, the analysis results can be presented using visually appealing infographics. By adjusting the presentation method of the analysis results according to the residents' emotions, residents' understanding and acceptance are improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0099] The analysis unit can improve the accuracy of its analysis by referring to the repair history of other condominiums when performing a detailed analysis of each repair item. For example, it can refer to the repair history database of other condominiums and compare the analysis results. It can also improve the accuracy of the analysis by referring to successful repair cases in other condominiums. Furthermore, it can analyze failed repair cases in other condominiums and perform analyses to avoid risks. In this way, the accuracy of the analysis is improved by referring to the repair history of other condominiums. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0100] The explanatory unit can estimate the residents' emotions and adjust the way it presents the explanation based on those estimated emotions. For example, if residents are feeling anxious, the explanation can use detailed data and graphs. If residents are excited, the explanation can be concise and to the point. Furthermore, if residents are relaxed, the explanation can use visually appealing infographics. By adjusting the way the explanation is presented according to the residents' emotions, their understanding and acceptance are improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanatory unit may be performed using AI, or not using AI.

[0101] The proposal section can estimate residents' emotions and adjust the presentation of the proposal based on those estimated emotions. For example, if residents are feeling anxious, the proposal can use detailed data and graphs. If residents are excited, the proposal can be concise and to the point. Furthermore, if residents are relaxed, the proposal can use visually appealing infographics. By adjusting the presentation of the proposal according to residents' emotions, understanding and acceptance of the proposal are improved. 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. Some or all of the above processing in the proposal section may be performed using AI, or not using AI.

[0102] The interactive support unit can estimate the emotions of residents and adjust the way support is presented based on those estimated emotions. For example, if a resident is feeling anxious, support can be provided using detailed data and graphs. If a resident is agitated, concise and to-the-point support can be provided. Furthermore, if a resident is relaxed, support can be provided using visually appealing infographics. By adjusting the way support is presented according to the resident's emotions, resident understanding and acceptance are improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interactive support unit may be performed using AI, or not using AI.

[0103] The analysis unit can monitor the deterioration status of repair items in real time and immediately reflect changes in their necessity. For example, it can use sensors to monitor the deterioration status of exterior walls in real time. It can also immediately analyze changes in the deterioration status and reflect them in the repair plan. Furthermore, it can save the deterioration status data to the cloud and make it accessible at any time. This improves the accuracy of the repair plan by monitoring the deterioration status in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0104] The explanation unit can adjust the level of detail in the explanation based on the importance of the repair items. For example, it can provide detailed explanations for high-priority repair items, and concise explanations for low-priority items. Furthermore, it can adjust the order of the explanations according to their importance. By adjusting the level of detail in the explanation based on the importance of the repair items, residents' understanding is deepened. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without using AI.

[0105] The proposal department can apply different proposal algorithms depending on the category of repair item when making a proposal. For example, for exterior wall repairs, it can make proposals that are appropriate to the degree of deterioration. For roof repairs, it can make proposals that take weather data into consideration. Furthermore, for interior repairs, it can make proposals that reflect the residents' requests. This allows for the provision of optimal proposals according to the category of repair item. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI.

[0106] The interactive support unit can provide the best possible answer by referring to the resident's past question history during support. For example, it can provide the best answer based on the content of questions the resident has asked in the past. It can also provide relevant information from the resident's past question history. Furthermore, it can analyze the resident's past question history to provide the most efficient support. In this way, the best possible answer can be provided by referring to the resident's past question history. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without using AI.

[0107] The interactive support unit can select the optimal support method by considering the resident's device information during support. For example, if the resident is using a smartphone, it can provide a support method adapted to the screen size. If the resident is using a tablet, it can provide a support method optimized for the larger screen. Furthermore, if the resident is using a smartwatch, it can provide a concise and highly visible support method. In this way, the optimal support method can be provided by considering the resident's device information. Some or all of the above processing in the interactive support unit may be performed using AI, for example, or without AI.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The analysis unit analyzes the necessity of each item included in the repair plan. For example, if exterior wall repairs are necessary, it analyzes the deterioration status of the exterior wall and past repair history to clarify the rationale. The processing in the analysis unit may or may not be performed using AI. Step 2: The explanation unit explains the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. The processing in the explanation unit may or may not be performed using AI. Step 3: The proposal department presents cost reduction proposals and gradual price increase plans based on the information presented by the explanation department. The proposal department refers to other condominium case studies and market data when presenting cost reduction proposals and gradual price increase plans. The processing in the proposal department may or may not be performed using AI. Step 4: The Interactive Support Department responds to residents' questions. For example, it provides immediate answers to residents' questions regarding the latest repair plans, cost breakdowns, and progress. Processing by the Interactive Support Department may or may not be done using AI.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the analysis unit, explanation unit, proposal unit, and interactive support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14, which performs a detailed analysis of repair items and clarifies their necessity. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12, which explains the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. The proposal unit is implemented by the control unit 46A of the smart device 14, which refers to other condominium case studies and market data and presents cost reduction proposals and gradual price increase plans. The interactive support unit is implemented by the specific processing unit 290 of the data processing unit 12, which immediately answers questions from residents with information on the latest repair plan, cost breakdown, and progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the analysis unit, explanation unit, proposal unit, and interactive support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214, which performs a detailed analysis of repair items and clarifies their necessity. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12, which explains the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. The proposal unit is implemented by the control unit 46A of the smart glasses 214, which refers to other condominium case studies and market data and presents cost reduction proposals and gradual price increase plans. The interactive support unit is implemented by the specific processing unit 290 of the data processing unit 12, which immediately answers questions from residents with information on the latest repair plan, cost breakdown, and progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the analysis unit, explanation unit, proposal unit, and interactive support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314, which performs a detailed analysis of repair items and clarifies their necessity. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12, which explains the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. The proposal unit is implemented by the control unit 46A of the headset terminal 314, which refers to other condominium case studies and market data and presents cost reduction proposals and gradual price increase plans. The interactive support unit is implemented by the specific processing unit 290 of the data processing unit 12, which immediately answers questions from residents with information on the latest repair plan, cost breakdown, and progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the analysis unit, explanation unit, proposal unit, and interactive support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414, which performs a detailed analysis of repair items and clarifies their necessity. The explanation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which explains the necessity of repair items to residents and owners based on the results analyzed by the analysis unit. The proposal unit is implemented by, for example, the control unit 46A of the robot 414, which refers to other condominium case studies and market data and presents cost reduction proposals and gradual price increase plans. The interactive support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which immediately answers questions from residents with information on the latest repair plan, cost breakdown, and progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) An analysis unit that analyzes the necessity of each item included in the repair plan, An explanatory unit that clearly explains the basis based on the results of the analysis performed by the aforementioned analysis unit, Based on the information explained by the aforementioned explanatory section, the proposal section presents cost reduction proposals and gradual price increase plans. It includes an interactive support unit that responds to questions from residents. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Conduct a detailed analysis of each repair item to clarify its necessity. The system described in Appendix 1, characterized by the features described herein. (Note 3) The above explanatory section is, Based on the results of the analysis performed by the aforementioned analysis unit, the necessity of the repair items will be explained to the residents and owners. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We will refer to other condominium case studies and market data to propose cost reduction plans and gradual price increase plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned interactive support unit is We will immediately respond to residents' questions with information regarding the latest repair plans, cost breakdowns, and progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We estimate the residents' emotions and adjust the presentation method of the analysis results based on the estimated emotions of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We will conduct a detailed analysis of the past repair history for each repair item and predict the future need for repairs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The deterioration status of repair items is monitored in real time, and changes in their necessity are reflected immediately. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, We estimate the sentiments of the residents and determine the priority of analysis based on the estimated sentiments of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing repair items, we improve the accuracy of the analysis by referring to the repair history of other condominiums. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing repair items, weather data is used to predict the progression of deterioration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The above explanatory section is, We estimate the residents' feelings and adjust the way explanations are presented based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 13) The above explanatory section is, During the explanation, adjust the level of detail based on the importance of the repair items. The system described in Appendix 1, characterized by the features described herein. (Note 14) The above explanatory section is, During the explanation, different explanation algorithms are applied depending on the residents' level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 15) The above explanatory section is, Estimate the residents' feelings and adjust the length of the explanation based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 16) The above explanatory section is, During the explanation, the priority of the explanation will be determined based on the submission timing of the repair items. The system described in Appendix 1, characterized by the features described herein. (Note 17) The above explanatory section is, During the explanation, adjust the order of the explanations based on the relevance of the repair items. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the residents' feelings and adjust the way the proposal is expressed based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the repair items. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the repair item. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Estimate the residents' sentiments and adjust the length of the proposal based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, the priority of the proposals will be determined based on the timing of submission of the repair items. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the order of the proposed repair items based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned interactive support unit is We estimate the residents' feelings and adjust the way we express support based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned interactive support unit is When providing support, we refer to the resident's past question history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned interactive support unit is When providing support, different support algorithms are applied depending on the residents' level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned interactive support unit is The system estimates the residents' feelings and adjusts the length of support based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned interactive support unit is When providing support, the optimal support method will be selected considering the resident's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned interactive support unit is When providing support, we will consider the geographical location of residents to provide highly relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes the necessity of each item included in the repair plan, An explanatory unit that clearly explains the basis based on the results of the analysis performed by the aforementioned analysis unit, Based on the information explained by the aforementioned explanatory section, the proposal section presents cost reduction proposals and gradual price increase plans. It includes an interactive support unit that responds to questions from residents. A system characterized by the following features.

2. The aforementioned analysis unit, Conduct a detailed analysis of each repair item to clarify its necessity. The system according to feature 1.

3. The above explanatory section is, Based on the results of the analysis performed by the aforementioned analysis unit, the necessity of the repair items will be explained to the residents and owners. The system according to feature 1.

4. The aforementioned proposal section is, We will refer to other condominium case studies and market data to propose cost reduction plans and gradual price increase plans. The system according to feature 1.

5. The aforementioned interactive support unit is We will immediately respond to residents' questions with information regarding the latest repair plans, cost breakdowns, and progress. The system according to feature 1.

6. The aforementioned analysis unit, We estimate the residents' emotions and adjust the presentation method of the analysis results based on the estimated emotions of the residents. The system according to feature 1.

7. The aforementioned analysis unit, We will conduct a detailed analysis of the past repair history for each repair item and predict the future need for repairs. The system according to feature 1.

8. The aforementioned analysis unit, The deterioration status of repair items is monitored in real time, and changes in their necessity are reflected immediately. The system according to feature 1.

9. The aforementioned analysis unit, We estimate the sentiments of the residents and determine the priority of analysis based on the estimated sentiments of the residents. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing repair items, we improve the accuracy of the analysis by referring to the repair history of other condominiums. The system according to feature 1.

Images