system

Abstract

The system according to this embodiment aims to optimize the budget planning for renovation projects. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, a planning unit, and a provision unit. The reception unit receives details of the renovation project. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes expenditures and cost reduction plans based on the information analyzed by the analysis unit. The planning unit creates a budget plan based on the proposals made by the proposal unit. The provision unit provides the budget plan created by the planning unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

[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 that responds 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, the budget plan of renovation projects has not been fully optimized, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the budget plan of renovation projects.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, a planning unit, and a provision unit. The reception unit receives details of the renovation project. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes expenditures and cost reduction plans based on the information analyzed by the analysis unit. The planning unit creates a budget plan based on the proposals made by the proposal unit. The provision unit provides the budget plan created by the planning unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize the budget planning for renovation projects. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The renovation project budget optimization system according to an embodiment of the present invention is a system that optimizes the budget of a renovation project using a generation AI. This system provides an efficient budget plan and improves the success rate of the project by having the user input details of the renovation project, having the generation AI analyze the input information, and proposing the necessary expenditures and cost reduction measures for the project. For example, the user inputs details of the renovation project. For example, the user inputs information about the building to be renovated, the desired renovation content, and the budget limit. This information is input into the generation AI. Next, the generation AI analyzes the input information. The generation AI identifies the expenditures necessary for the renovation project and makes suggestions for cost reduction. For example, it proposes specific methods to reduce costs, such as selecting building materials and adjusting the construction schedule. Based on the expenditures and cost reduction measures proposed by the generation AI, an efficient budget plan is created. As a result, the user can reduce unnecessary expenditures and proceed with the renovation project efficiently. For example, by using building materials proposed by the generation AI, it is possible to achieve a high-quality renovation while reducing costs. This mechanism leads to the optimization of the renovation project budget and a reduction in unnecessary expenditures. Users can create efficient budget plans based on suggestions from the generating AI, improving the project's success rate. For example, by implementing cost-reduction measures suggested by the generating AI, high-quality renovations can be achieved within budget, increasing the project's success rate. In this way, the renovation project budget optimization system enables users to efficiently advance their renovation projects.

[0029] The renovation project budget optimization system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, a planning unit, and a provision unit. The reception unit receives input from the user regarding the details of the renovation project. When the user inputs the details of the renovation project, they may input information such as the building to be renovated, the desired renovation content, and the budget limit. The reception unit saves the information entered by the user to a database and transmits it to the analysis unit. The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis unit identifies the expenditures necessary for the renovation project and makes proposals for cost reduction. The analysis unit uses a generation AI to propose specific methods for reducing costs, such as selecting building materials and adjusting the construction schedule. The proposal unit proposes expenditures and cost reduction plans based on the information analyzed by the analysis unit. The proposal unit proposes a method to achieve a high-quality renovation while reducing costs, for example, by using building materials proposed by the generation AI. The planning unit creates an efficient budget plan based on the content proposed by the proposal unit. The planning unit creates a budget plan that minimizes unnecessary spending, for example, based on the expenditures and cost reduction proposals suggested by the generation AI. The provision unit provides the user with the budget plan created by the planning unit. The provision unit provides the user with an efficient budget plan, for example, based on the content suggested by the generation AI. As a result, the renovation project budget optimization system according to this embodiment can efficiently advance the user's renovation project.

[0030] The reception department receives the details of the renovation project from the user. When users enter the details of the renovation project, they enter information such as the building to be renovated, the desired renovations, and the budget limit. Specifically, they are required to enter basic information such as the building's location, age, structure, area, and current problems. Users can also enter details such as the purpose of the renovation, desired renovation areas, building materials and equipment to be used, and design requests. Furthermore, they can enter any requests related to the project, such as the budget limit, desired construction period, and the designation of specific contractors or brands. The reception department stores this information in a database and sends it to the analysis department. The information entered by the user is centrally managed within the system and used for subsequent analysis and proposals. The reception department also has a checking function to verify the accuracy and completeness of the information entered by the user, and if there is any missing or inconsistent information, it prompts the user to correct or add it. In addition, the reception department encrypts and stores the information entered by the user to ensure privacy and security. This allows the reception desk to provide users with an environment where they can accurately and securely input details of their renovation projects, thereby improving the overall reliability and efficiency of the system.

[0031] The analysis unit uses a generation AI to analyze information received by the reception unit. For example, the analysis unit identifies the necessary expenditures for a renovation project and makes suggestions for cost reduction. Specifically, the generation AI calculates the necessary building materials, equipment, types of construction work, and their costs based on the building information and desired renovations entered by the user. Furthermore, the generation AI refers to data from past renovation projects and market price trends to select the most suitable building materials and equipment. For example, if there are multiple building materials with the same performance but different prices, the generation AI will select the most cost-effective material. In addition, when adjusting the construction schedule, the generation AI optimizes the order and duration of each construction step and proposes ways to reduce wasted time and costs. Based on these analysis results, the analysis unit presents the user with specific cost reduction proposals. For example, it provides useful information to the user, such as cases where costs can be reduced by using specific building materials or where the construction period can be shortened by changing the order of construction work. Furthermore, the analysis unit can simulate multiple scenarios according to the user's requests and budget and propose the optimal renovation plan. This allows the analysis unit to play a crucial role in efficiently and economically advancing the user's renovation project.

[0032] The proposal department proposes expenditure and cost reduction plans based on information analyzed by the analysis department. Specifically, it proposes methods to achieve high-quality renovations while reducing costs by using building materials suggested by the generation AI. The proposal department selects the optimal building materials, equipment, and construction methods according to the user's requests and budget, and makes concrete proposals. For example, it selects the optimal building materials and equipment to realize the design and functions desired by the user, and explains the cost and effect in detail. In addition, the proposal department proposes a specific construction schedule and procedure based on the cost reduction plans proposed by the analysis department. For example, it proposes methods to shorten the construction period and reduce costs by optimizing the order of construction. Furthermore, the proposal department presents the user with multiple options and explains the advantages and disadvantages of each, helping the user make the best choice. The proposal department emphasizes communication with the user and makes proposals that reflect the user's requests and opinions. In addition, the proposal department collaborates with experts and contractors to confirm the feasibility of the proposed content and improve the accuracy and reliability of the proposal. This allows the proposal department to provide users with concrete and feasible renovation plans, contributing to the success of the project.

[0033] The Planning Department creates an efficient budget plan based on the proposals submitted by the Proposal Department. Specifically, it creates a budget plan that minimizes unnecessary spending based on the expenditures and cost reduction proposals suggested by the Generating AI. The Planning Department manages the overall project cost by optimally allocating necessary expenditures while considering the user's budget limit. For example, it estimates the cost of purchasing building materials and equipment, labor costs for construction, and other related expenses in detail, and allocates an appropriate budget to each item. The Planning Department also monitors the progress of construction and actual expenditures in real time to prevent budget overruns or underruns. Furthermore, it is important for the Planning Department to include contingency funds and emergency response measures to ensure flexibility in the budget plan. For example, it sets aside a certain amount of contingency funds to prepare for unexpected problems and enable a quick response. The Planning Department provides these budget plans to the user, allowing the user to understand the project's progress and expenditure status. Through regular communication with the user, the Planning Department reports on the progress of the budget plan and revises or modifies the plan as needed. This allows the planning department to play a crucial role in efficiently and economically advancing users' renovation projects.

[0034] The service provider provides users with budget plans created by the planning department. Specifically, they provide users with efficient budget plans based on suggestions from the generation AI. The service provider uses visual graphs and charts to explain the budget plan in a way that is easy for users to understand. For example, they use pie charts and bar graphs that show the proportion of each expenditure item and the allocation of the budget so that users can grasp the overall picture of the budget at a glance. The service provider also creates and provides users with detailed explanations and reports of the budget plan. This allows users to review the budget plan in detail and ask questions or make revisions as needed. Furthermore, the service provider provides the necessary support when users implement the budget plan. For example, they assist with the purchase of building materials and equipment based on the budget plan, monitor the progress of the construction, and manage to prevent budget overruns or underruns. The service provider also responds quickly and appropriately to any problems or questions that users may have when implementing the budget plan. In this way, the service provider can play a crucial role in enabling users to smoothly implement the budget plan and ensure the success of the renovation project. Furthermore, the service provider collects feedback from users and uses it to improve the entire system. For example, by understanding which parts of the budget plan users are satisfied with and which parts could be improved, the service provider can incorporate this feedback into future projects. This allows the service provider to increase user satisfaction and improve the overall reliability and efficiency of the system.

[0035] The reception desk can analyze the user's past renovation project history and suggest the optimal input method. For example, the reception desk can automatically display details of renovation projects that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest details to be used at specific times based on the user's past renovation project history. This improves input efficiency by suggesting the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past renovation project history data into a generating AI and have the generating AI suggest the optimal input method.

[0036] The reception desk can customize input fields based on the user's current living situation and areas of interest when the user enters details of a renovation project. For example, if the user lives with family, the reception desk can suggest renovation items that suit the family structure. It can also suggest pet-friendly renovation items if the user has pets. Furthermore, if the user enjoys gardening as a hobby, the reception desk can suggest garden renovation items. This allows for the creation of more appropriate renovation plans by providing input fields tailored to the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's living situation data into a generating AI and have the generating AI customize the input fields.

[0037] The reception desk can prioritize inputting highly relevant information when users enter details of a renovation project, taking into account their geographical location. For example, if a user lives in a cold region, the reception desk can prioritize inputting information about insulation materials. If a user lives in an urban area, the reception desk can also prioritize inputting information about soundproofing measures. Furthermore, if a user lives near the coast, the reception desk can prioritize inputting information about salt damage prevention measures. By providing input items based on geographical location information, it is possible to create a renovation plan that is appropriate for the region. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize inputting highly relevant information.

[0038] The reception desk can analyze the user's social media activity and prompt them to input relevant information when entering details of a renovation project. For example, the reception desk can automatically suggest renovation ideas that the user has shared on social media as input fields. It can also suggest information obtained from renovation-related accounts that the user follows as input fields. Furthermore, the reception desk can suggest input fields based on renovation projects that the user has previously "liked". This allows for the creation of renovation plans tailored to the user's interests by providing input fields based on social media activity. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI input relevant information.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the renovation project. For example, for high-importance projects, the analysis unit performs a detailed analysis and provides comprehensive results. For low-importance projects, the analysis unit can perform a simplified analysis and provide results quickly. Furthermore, for projects of moderate importance, the analysis unit can perform an analysis with an appropriate level of detail and provide balanced results. This ensures efficient analysis results by performing analyses according to the importance of the project. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input renovation project importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the renovation project during the analysis. For example, in the case of residential renovation, the analysis unit applies an analysis algorithm specialized for residential properties. Furthermore, in the case of commercial facility renovation, the analysis unit can apply an analysis algorithm specialized for commercial facilities. In addition, in the case of public facility renovation, the analysis unit can apply an analysis algorithm specialized for public facilities. This allows the analysis to be tailored to the project category, providing appropriate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input renovation project category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0041] The analysis unit can determine the priority of the analysis based on the submission dates of the renovation projects. For example, the analysis unit may prioritize projects with approaching submission deadlines. It can also postpone projects with distant submission deadlines. Furthermore, it can analyze projects with medium-term submission deadlines with appropriate priority. This allows for efficient analysis by setting priorities based on submission dates. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input renovation project submission date data into a generating AI and have the generating AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the renovation projects during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant projects. It can also postpone the analysis of less relevant projects. Furthermore, it can analyze projects with moderate relevance in an appropriate order. This allows for efficient analysis by setting an order based on relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the renovation projects into a generating AI and have the generating AI adjust the order of analysis.

[0043] The proposal unit can adjust the level of detail in its proposals based on the importance of the renovation project. For example, for high-importance projects, the proposal unit provides detailed proposals. For low-importance projects, it can provide simplified proposals. Furthermore, for projects of moderate importance, it can provide proposals with an appropriate level of detail. This allows for efficient proposals by tailoring them to the importance of the project. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input renovation project importance data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0044] The proposal unit can apply different proposal algorithms depending on the category of the renovation project when making a proposal. For example, in the case of residential renovation, the proposal unit applies a proposal algorithm specialized for residential properties. Furthermore, in the case of commercial facility renovation, the proposal unit can apply a proposal algorithm specialized for commercial facilities. In addition, in the case of public facility renovation, the proposal unit can apply a proposal algorithm specialized for public facilities. This ensures that appropriate proposals are provided by tailoring them to the project category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input renovation project category data into a generating AI and have the generating AI apply different proposal algorithms.

[0045] The proposal department can determine the priority of proposals based on the submission deadlines for renovation projects. For example, the proposal department can prioritize projects with approaching deadlines. It can also postpone projects with distant deadlines. Furthermore, it can propose projects with medium-term deadlines with appropriate priority. This allows for efficient proposals by setting priorities based on submission timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input renovation project submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0046] The proposal unit can adjust the order of proposals based on the relevance of the renovation projects during the proposal process. For example, the proposal unit can prioritize projects with high relevance. It can also postpone projects with low relevance. Furthermore, it can propose projects with moderate relevance in an appropriate order. This allows for efficient proposals by setting an order based on relevance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input relevance data of renovation projects into a generating AI and have the generating AI adjust the order of proposals.

[0047] The planning department can analyze the user's past renovation project history to select the optimal planning method when creating a budget plan. For example, the planning department can refer to the budget plans of the user's past successful renovation projects. It can also adjust the plan by utilizing the lessons learned from the user's past unsuccessful renovation projects. Furthermore, the planning department can analyze the trends of the user's past renovation projects and propose the optimal budget plan. This improves the accuracy of the plan by selecting the optimal planning method based on past history. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input the user's past renovation project history data into a generating AI and have the generating AI select the optimal planning method.

[0048] The planning unit can customize the planning methods based on the user's current living situation when creating a budget plan. For example, if the user lives with family, the planning unit can propose a budget plan tailored to the family structure. If the user has pets, the planning unit can also propose a budget plan that takes pets into consideration. Furthermore, if the user enjoys gardening as a hobby, the planning unit can propose a budget plan specifically for garden renovations. This allows for the creation of more appropriate budget plans by providing planning methods tailored to the user's living situation. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input user living situation data into a generating AI and have the generating AI perform the customization of the planning methods.

[0049] The planning department can select the optimal planning method when creating a budget plan, taking into account the user's geographical location information. For example, if the user lives in a cold region, the planning department can prioritize the inclusion of insulation materials in the budget plan. Similarly, if the user lives in an urban area, the planning department can prioritize the inclusion of soundproofing measures. Furthermore, if the user lives near the coast, the planning department can prioritize the inclusion of salt damage prevention measures. This allows for the creation of a budget plan tailored to the region by providing a planning method based on geographical location information. Some or all of the above-described processes in the planning department may be performed using AI, for example, or without AI. For instance, the planning department can input the user's geographical location data into a generating AI and have the generating AI select the optimal planning method.

[0050] The planning department can analyze a user's social media activity and propose planning methods when creating a budget plan. For example, the planning department can incorporate renovation ideas shared by the user on social media into the budget plan. It can also incorporate information obtained from renovation-related accounts that the user follows into the budget plan. Furthermore, the planning department can propose a budget plan by referring to renovation projects that the user has previously "liked". In this way, by providing planning methods based on social media activity, it is possible to create a budget plan that is tailored to the user's interests. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of planning methods.

[0051] The service provider can select the optimal service method when providing a budget plan by referring to the user's past renovation project history. For example, the service provider can refer to the budget plans of the user's past successful renovation projects. Furthermore, the service provider can adjust the service method by utilizing lessons learned from the user's past unsuccessful renovation projects. In addition, the service provider can analyze the trends of the user's past renovation projects and propose the optimal service method. This improves the accuracy of the service by selecting the optimal service method based on past history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past renovation project history data into a generating AI and have the generating AI select the optimal service method.

[0052] The service provider can select the optimal delivery method when providing a budget plan, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a delivery method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible delivery method. This allows the service provider to provide a budget plan optimized for the user's device by providing a delivery method based on device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user device information data into a generating AI and have the generating AI select the optimal delivery method.

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

[0054] The reception desk can display the progress of a renovation project in real time based on user input. For example, it can visually display the project's progress using graphs and charts based on the selection of building materials and construction schedule entered by the user. The reception desk can also display the budget consumption status in real time based on the budget limit entered by the user. Furthermore, the reception desk can display details of ongoing work based on the desired renovations entered by the user. This makes it easier for users to understand the progress of the project and manage it efficiently.

[0055] The proposal department can analyze the user's past renovation project history and select the optimal proposal method. For example, it can refer to proposal methods from the user's past successful renovation projects. It can also adjust the proposal method by utilizing lessons learned from past unsuccessful renovation projects. Furthermore, it can analyze the trends of the user's past renovation projects and propose the optimal proposal method. In this way, the accuracy of proposals is improved by selecting the optimal proposal method based on past history.

[0056] The service provider can select the optimal delivery method by considering the user's device information. For example, if the user is using a smartphone, they can provide a delivery method that matches the screen size. If the user is using a tablet, they can provide a delivery method optimized for the larger screen. Furthermore, if the user is using a smartwatch, they can provide a concise and highly visible delivery method. By providing a delivery method based on device information, they can provide a budget plan optimized for the user's device.

[0057] The analysis unit can prioritize analyses based on the progress of the renovation project. For example, for ongoing projects, it can perform detailed analyses and provide comprehensive results. For projects in the early stages, it can perform simplified analyses and provide results quickly. Furthermore, for ongoing projects, it can provide only the most important analysis results, with other results provided later. This allows for efficient analysis results by performing analyses according to the project's progress.

[0058] The planning department can analyze the user's past renovation project history and select the optimal method for creating a budget plan. For example, it can refer to the budget plans of the user's past successful renovation projects. It can also adjust the plan by incorporating lessons learned from past unsuccessful renovation projects. Furthermore, it can analyze the trends of the user's past renovation projects and propose the optimal budget plan. By selecting the best planning method based on past history, the accuracy of the plan is improved.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives the user's renovation project details. The user enters information about the building to be renovated, the desired renovations, the budget limit, etc. The reception desk saves this information to the database and sends it to the analysis department. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis unit identifies the necessary expenditures for the renovation project and proposes specific methods for cost reduction. For example, this may include selecting building materials and adjusting the construction schedule. Step 3: The proposal team proposes expenditure and cost reduction plans based on the information analyzed by the analysis team. The proposal team proposes a method to achieve high-quality renovation while reducing costs by using building materials suggested by the generation AI. Step 4: The Planning Department creates an efficient budget plan based on the proposals made by the Proposal Department. The Planning Department creates a budget plan that minimizes wasteful spending based on the expenditures and cost reduction proposals suggested by the Generating AI. Step 5: The provisioning department provides the user with the budget plan created by the planning department. The provisioning department provides the user with an efficient budget plan based on the suggestions made by the generation AI.

[0061] (Example of form 2) The renovation project budget optimization system according to an embodiment of the present invention is a system that optimizes the budget of a renovation project using a generation AI. This system provides an efficient budget plan and improves the success rate of the project by having the user input details of the renovation project, having the generation AI analyze the input information, and proposing the necessary expenditures and cost reduction measures for the project. For example, the user inputs details of the renovation project. For example, the user inputs information about the building to be renovated, the desired renovation content, and the budget limit. This information is input into the generation AI. Next, the generation AI analyzes the input information. The generation AI identifies the expenditures necessary for the renovation project and makes suggestions for cost reduction. For example, it proposes specific methods to reduce costs, such as selecting building materials and adjusting the construction schedule. Based on the expenditures and cost reduction measures proposed by the generation AI, an efficient budget plan is created. As a result, the user can reduce unnecessary expenditures and proceed with the renovation project efficiently. For example, by using building materials proposed by the generation AI, it is possible to achieve a high-quality renovation while reducing costs. This mechanism leads to the optimization of the renovation project budget and a reduction in unnecessary expenditures. Users can create efficient budget plans based on suggestions from the generating AI, improving the project's success rate. For example, by implementing cost-reduction measures suggested by the generating AI, high-quality renovations can be achieved within budget, increasing the project's success rate. In this way, the renovation project budget optimization system enables users to efficiently advance their renovation projects.

[0062] The renovation project budget optimization system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, a planning unit, and a provision unit. The reception unit receives input from the user regarding the details of the renovation project. When the user inputs the details of the renovation project, they may input information such as the building to be renovated, the desired renovation content, and the budget limit. The reception unit saves the information entered by the user to a database and transmits it to the analysis unit. The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis unit identifies the expenditures necessary for the renovation project and makes proposals for cost reduction. The analysis unit uses a generation AI to propose specific methods for reducing costs, such as selecting building materials and adjusting the construction schedule. The proposal unit proposes expenditures and cost reduction plans based on the information analyzed by the analysis unit. The proposal unit proposes a method to achieve a high-quality renovation while reducing costs, for example, by using building materials proposed by the generation AI. The planning unit creates an efficient budget plan based on the content proposed by the proposal unit. The planning unit creates a budget plan that minimizes unnecessary spending, for example, based on the expenditures and cost reduction proposals suggested by the generation AI. The provision unit provides the user with the budget plan created by the planning unit. The provision unit provides the user with an efficient budget plan, for example, based on the content suggested by the generation AI. As a result, the renovation project budget optimization system according to this embodiment can efficiently advance the user's renovation project.

[0063] The reception department receives the details of the renovation project from the user. When users enter the details of the renovation project, they enter information such as the building to be renovated, the desired renovations, and the budget limit. Specifically, they are required to enter basic information such as the building's location, age, structure, area, and current problems. Users can also enter details such as the purpose of the renovation, desired renovation areas, building materials and equipment to be used, and design requests. Furthermore, they can enter any requests related to the project, such as the budget limit, desired construction period, and the designation of specific contractors or brands. The reception department stores this information in a database and sends it to the analysis department. The information entered by the user is centrally managed within the system and used for subsequent analysis and proposals. The reception department also has a checking function to verify the accuracy and completeness of the information entered by the user, and if there is any missing or inconsistent information, it prompts the user to correct or add it. In addition, the reception department encrypts and stores the information entered by the user to ensure privacy and security. This allows the reception desk to provide users with an environment where they can accurately and securely input details of their renovation projects, thereby improving the overall reliability and efficiency of the system.

[0064] The analysis unit uses a generation AI to analyze information received by the reception unit. For example, the analysis unit identifies the necessary expenditures for a renovation project and makes suggestions for cost reduction. Specifically, the generation AI calculates the necessary building materials, equipment, types of construction work, and their costs based on the building information and desired renovations entered by the user. Furthermore, the generation AI refers to data from past renovation projects and market price trends to select the most suitable building materials and equipment. For example, if there are multiple building materials with the same performance but different prices, the generation AI will select the most cost-effective material. In addition, when adjusting the construction schedule, the generation AI optimizes the order and duration of each construction step and proposes ways to reduce wasted time and costs. Based on these analysis results, the analysis unit presents the user with specific cost reduction proposals. For example, it provides useful information to the user, such as cases where costs can be reduced by using specific building materials or where the construction period can be shortened by changing the order of construction work. Furthermore, the analysis unit can simulate multiple scenarios according to the user's requests and budget and propose the optimal renovation plan. This allows the analysis unit to play a crucial role in efficiently and economically advancing the user's renovation project.

[0065] The proposal department proposes expenditure and cost reduction plans based on information analyzed by the analysis department. Specifically, it proposes methods to achieve high-quality renovations while reducing costs by using building materials suggested by the generation AI. The proposal department selects the optimal building materials, equipment, and construction methods according to the user's requests and budget, and makes concrete proposals. For example, it selects the optimal building materials and equipment to realize the design and functions desired by the user, and explains the cost and effect in detail. In addition, the proposal department proposes a specific construction schedule and procedure based on the cost reduction plans proposed by the analysis department. For example, it proposes methods to shorten the construction period and reduce costs by optimizing the order of construction. Furthermore, the proposal department presents the user with multiple options and explains the advantages and disadvantages of each, helping the user make the best choice. The proposal department emphasizes communication with the user and makes proposals that reflect the user's requests and opinions. In addition, the proposal department collaborates with experts and contractors to confirm the feasibility of the proposed content and improve the accuracy and reliability of the proposal. This allows the proposal department to provide users with concrete and feasible renovation plans, contributing to the success of the project.

[0066] The Planning Department creates an efficient budget plan based on the proposals submitted by the Proposal Department. Specifically, it creates a budget plan that minimizes unnecessary spending based on the expenditures and cost reduction proposals suggested by the Generating AI. The Planning Department manages the overall project cost by optimally allocating necessary expenditures while considering the user's budget limit. For example, it estimates the cost of purchasing building materials and equipment, labor costs for construction, and other related expenses in detail, and allocates an appropriate budget to each item. The Planning Department also monitors the progress of construction and actual expenditures in real time to prevent budget overruns or underruns. Furthermore, it is important for the Planning Department to include contingency funds and emergency response measures to ensure flexibility in the budget plan. For example, it sets aside a certain amount of contingency funds to prepare for unexpected problems and enable a quick response. The Planning Department provides these budget plans to the user, allowing the user to understand the project's progress and expenditure status. Through regular communication with the user, the Planning Department reports on the progress of the budget plan and revises or modifies the plan as needed. This allows the planning department to play a crucial role in efficiently and economically advancing users' renovation projects.

[0067] The service provider provides users with budget plans created by the planning department. Specifically, they provide users with efficient budget plans based on suggestions from the generation AI. The service provider uses visual graphs and charts to explain the budget plan in a way that is easy for users to understand. For example, they use pie charts and bar graphs that show the proportion of each expenditure item and the allocation of the budget so that users can grasp the overall picture of the budget at a glance. The service provider also creates and provides users with detailed explanations and reports of the budget plan. This allows users to review the budget plan in detail and ask questions or make revisions as needed. Furthermore, the service provider provides the necessary support when users implement the budget plan. For example, they assist with the purchase of building materials and equipment based on the budget plan, monitor the progress of the construction, and manage to prevent budget overruns or underruns. The service provider also responds quickly and appropriately to any problems or questions that users may have when implementing the budget plan. In this way, the service provider can play a crucial role in enabling users to smoothly implement the budget plan and ensure the success of the renovation project. Furthermore, the service provider collects feedback from users and uses it to improve the entire system. For example, by understanding which parts of the budget plan users are satisfied with and which parts could be improved, the service provider can incorporate this feedback into future projects. This allows the service provider to increase user satisfaction and improve the overall reliability and efficiency of the system.

[0068] The reception desk can estimate the user's emotions and adjust the interface for entering renovation project details based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick entry of renovation project details. This improves the ease of input by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0069] The reception desk can analyze the user's past renovation project history and suggest the optimal input method. For example, the reception desk can automatically display details of renovation projects that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest details to be used at specific times based on the user's past renovation project history. This improves input efficiency by suggesting the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past renovation project history data into a generating AI and have the generating AI suggest the optimal input method.

[0070] The reception desk can customize input fields based on the user's current living situation and areas of interest when the user enters details of a renovation project. For example, if the user lives with family, the reception desk can suggest renovation items that suit the family structure. It can also suggest pet-friendly renovation items if the user has pets. Furthermore, if the user enjoys gardening as a hobby, the reception desk can suggest garden renovation items. This allows for the creation of more appropriate renovation plans by providing input fields tailored to the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's living situation data into a generating AI and have the generating AI customize the input fields.

[0071] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of project details to be entered. For example, if the user is stressed, the reception desk can prioritize the entry of important items and postpone other items. If the user is relaxed, the reception desk can also allow the user to enter all items in order. Furthermore, if the user is in a hurry, the reception desk can allow the user to enter only the most important items and leave the others for later. This allows important items to be entered preferentially by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0072] The reception desk can prioritize inputting highly relevant information when users enter details of a renovation project, taking into account their geographical location. For example, if a user lives in a cold region, the reception desk can prioritize inputting information about insulation materials. If a user lives in an urban area, the reception desk can also prioritize inputting information about soundproofing measures. Furthermore, if a user lives near the coast, the reception desk can prioritize inputting information about salt damage prevention measures. By providing input items based on geographical location information, it is possible to create a renovation plan that is appropriate for the region. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize inputting highly relevant information.

[0073] The reception desk can analyze the user's social media activity and prompt them to input relevant information when entering details of a renovation project. For example, the reception desk can automatically suggest renovation ideas that the user has shared on social media as input fields. It can also suggest information obtained from renovation-related accounts that the user follows as input fields. Furthermore, the reception desk can suggest input fields based on renovation projects that the user has previously "liked". This allows for the creation of renovation plans tailored to the user's interests by providing input fields based on social media activity. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI input relevant information.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can simplify the analysis algorithm and provide results quickly. If the user is relaxed, the analysis unit can also perform a detailed analysis and provide comprehensive results. Furthermore, if the user is in a hurry, the analysis unit can prioritize providing only the most important analysis results. This improves the accuracy of the analysis results by providing an analysis algorithm tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI adjust the analysis algorithm.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the renovation project. For example, for high-importance projects, the analysis unit performs a detailed analysis and provides comprehensive results. For low-importance projects, the analysis unit can perform a simplified analysis and provide results quickly. Furthermore, for projects of moderate importance, the analysis unit can perform an analysis with an appropriate level of detail and provide balanced results. This ensures efficient analysis results by performing analyses according to the importance of the project. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input renovation project importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of the renovation project during the analysis. For example, in the case of residential renovation, the analysis unit applies an analysis algorithm specialized for residential properties. Furthermore, in the case of commercial facility renovation, the analysis unit can apply an analysis algorithm specialized for commercial facilities. In addition, in the case of public facility renovation, the analysis unit can apply an analysis algorithm specialized for public facilities. This allows the analysis to be tailored to the project category, providing appropriate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input renovation project category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This deepens the understanding of the analysis results by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0078] The analysis unit can determine the priority of the analysis based on the submission dates of the renovation projects. For example, the analysis unit may prioritize projects with approaching submission deadlines. It can also postpone projects with distant submission deadlines. Furthermore, it can analyze projects with medium-term submission deadlines with appropriate priority. This allows for efficient analysis by setting priorities based on submission dates. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input renovation project submission date data into a generating AI and have the generating AI determine the analysis priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the renovation projects during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant projects. It can also postpone the analysis of less relevant projects. Furthermore, it can analyze projects with moderate relevance in an appropriate order. This allows for efficient analysis by setting an order based on relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the renovation projects into a generating AI and have the generating AI adjust the order of analysis.

[0080] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions. This improves the acceptability of suggestions by providing suggestions that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into the generative AI and have the generative AI adjust the way suggestions are presented.

[0081] The proposal unit can adjust the level of detail in its proposals based on the importance of the renovation project. For example, for high-importance projects, the proposal unit provides detailed proposals. For low-importance projects, it can provide simplified proposals. Furthermore, for projects of moderate importance, it can provide proposals with an appropriate level of detail. This allows for efficient proposals by tailoring them to the importance of the project. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input renovation project importance data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0082] The proposal unit can apply different proposal algorithms depending on the category of the renovation project when making a proposal. For example, in the case of residential renovation, the proposal unit applies a proposal algorithm specialized for residential properties. Furthermore, in the case of commercial facility renovation, the proposal unit can apply a proposal algorithm specialized for commercial facilities. In addition, in the case of public facility renovation, the proposal unit can apply a proposal algorithm specialized for public facilities. This ensures that appropriate proposals are provided by tailoring them to the project category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input renovation project category data into a generating AI and have the generating AI apply different proposal algorithms.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide longer suggestions with more detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This improves the acceptability of suggestions by providing suggestions of appropriate lengths according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into the generative AI and have the generative AI adjust the length of the suggestions.

[0084] The proposal department can determine the priority of proposals based on the submission deadlines for renovation projects. For example, the proposal department can prioritize projects with approaching deadlines. It can also postpone projects with distant deadlines. Furthermore, it can propose projects with medium-term deadlines with appropriate priority. This allows for efficient proposals by setting priorities based on submission timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input renovation project submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0085] The proposal unit can adjust the order of proposals based on the relevance of the renovation projects during the proposal process. For example, the proposal unit can prioritize projects with high relevance. It can also postpone projects with low relevance. Furthermore, it can propose projects with moderate relevance in an appropriate order. This allows for efficient proposals by setting an order based on relevance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input relevance data of renovation projects into a generating AI and have the generating AI adjust the order of proposals.

[0086] The planning department can estimate the user's emotions and adjust how the budget plan is created based on those emotions. For example, if the user is stressed, the planning department can provide a simple budget plan and create it quickly. If the user is relaxed, the planning department can also provide a detailed budget plan that includes comprehensive content. Furthermore, if the user is in a hurry, the planning department can prioritize including only the most important items in the budget plan. This improves the acceptability of the plan by providing a budget plan that is tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as 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 planning department may be performed using AI or not. For example, the planning department can input user facial expression data into a generative AI and have the generative AI adjust how the budget plan is created.

[0087] The planning department can analyze the user's past renovation project history to select the optimal planning method when creating a budget plan. For example, the planning department can refer to the budget plans of the user's past successful renovation projects. It can also adjust the plan by utilizing the lessons learned from the user's past unsuccessful renovation projects. Furthermore, the planning department can analyze the trends of the user's past renovation projects and propose the optimal budget plan. This improves the accuracy of the plan by selecting the optimal planning method based on past history. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input the user's past renovation project history data into a generating AI and have the generating AI select the optimal planning method.

[0088] The planning unit can customize the planning methods based on the user's current living situation when creating a budget plan. For example, if the user lives with family, the planning unit can propose a budget plan tailored to the family structure. If the user has pets, the planning unit can also propose a budget plan that takes pets into consideration. Furthermore, if the user enjoys gardening as a hobby, the planning unit can propose a budget plan specifically for garden renovations. This allows for the creation of more appropriate budget plans by providing planning methods tailored to the user's living situation. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input user living situation data into a generating AI and have the generating AI perform the customization of the planning methods.

[0089] The planning department can estimate the user's emotions and determine the priorities of the budget plan based on those emotions. For example, if the user is stressed, the planning department can prioritize including important items in the budget plan and postpone others. If the user is relaxed, the planning department can also include all items in the budget plan in order. Furthermore, if the user is in a hurry, the planning department can include only the most important items in the budget plan and allow others to be added later. This allows important items to be included in the plan preferentially by setting priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 planning department may be performed using AI or not. For example, the planning department can input user facial expression data into a generative AI and have the generative AI determine the priorities of the budget plan.

[0090] The planning department can select the optimal planning method when creating a budget plan, taking into account the user's geographical location information. For example, if the user lives in a cold region, the planning department can prioritize the inclusion of insulation materials in the budget plan. Similarly, if the user lives in an urban area, the planning department can prioritize the inclusion of soundproofing measures. Furthermore, if the user lives near the coast, the planning department can prioritize the inclusion of salt damage prevention measures. This allows for the creation of a budget plan tailored to the region by providing a planning method based on geographical location information. Some or all of the above-described processes in the planning department may be performed using AI, for example, or without AI. For instance, the planning department can input the user's geographical location data into a generating AI and have the generating AI select the optimal planning method.

[0091] The planning department can analyze a user's social media activity and propose planning methods when creating a budget plan. For example, the planning department can incorporate renovation ideas shared by the user on social media into the budget plan. It can also incorporate information obtained from renovation-related accounts that the user follows into the budget plan. Furthermore, the planning department can propose a budget plan by referring to renovation projects that the user has previously "liked". In this way, by providing planning methods based on social media activity, it is possible to create a budget plan that is tailored to the user's interests. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of planning methods.

[0092] The service provider can estimate the user's emotions and adjust the way the budget plan is presented based on those emotions. For example, if the user is stressed, the service provider can provide a simple and easy-to-understand budget plan. If the user is relaxed, the service provider can also provide a budget plan with more detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise budget plan. This improves the acceptability of the budget plan by providing a presentation method that suits the user's emotions. 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 service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI adjust the way the budget plan is presented.

[0093] The service provider can select the optimal service method when providing a budget plan by referring to the user's past renovation project history. For example, the service provider can refer to the budget plans of the user's past successful renovation projects. Furthermore, the service provider can adjust the service method by utilizing lessons learned from the user's past unsuccessful renovation projects. In addition, the service provider can analyze the trends of the user's past renovation projects and propose the optimal service method. This improves the accuracy of the service by selecting the optimal service method based on past history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past renovation project history data into a generating AI and have the generating AI select the optimal service method.

[0094] The delivery unit can estimate the user's emotions and determine the order in which the budget plan is delivered based on those emotions. For example, if the user is stressed, the delivery unit can prioritize delivering important items and postpone others. If the user is relaxed, the delivery unit can deliver all items in order. Furthermore, if the user is in a hurry, the delivery unit can prioritize delivering only the most important items and allow others to be added later. This allows for the prioritization of important items by setting the delivery order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may 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 delivery unit may be performed using AI or not. For example, the delivery unit can input user facial expression data into a generative AI and have the generative AI determine the order in which the budget plan is delivered.

[0095] The service provider can select the optimal delivery method when providing a budget plan, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a delivery method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible delivery method. This allows the service provider to provide a budget plan optimized for the user's device by providing a delivery method based on device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user device information data into a generating AI and have the generating AI select the optimal delivery method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can display the progress of a renovation project in real time based on user input. For example, it can visually display the project's progress using graphs and charts based on the selection of building materials and construction schedule entered by the user. The reception desk can also display the budget consumption status in real time based on the budget limit entered by the user. Furthermore, the reception desk can display details of ongoing work based on the desired renovations entered by the user. This makes it easier for users to understand the progress of the project and manage it efficiently.

[0098] The analysis unit can estimate the user's emotions and prioritize the analysis results based on those emotions. For example, if the user is stressed, important analysis results will be displayed first, while other results will be displayed later. If the user is relaxed, all analysis results can be displayed in order. Furthermore, if the user is in a hurry, only the most important analysis results can be displayed, with other results shown later. In this way, by setting priorities according to the user's emotions, important analysis results can be provided preferentially.

[0099] The proposal department can analyze the user's past renovation project history and select the optimal proposal method. For example, it can refer to proposal methods from the user's past successful renovation projects. It can also adjust the proposal method by utilizing lessons learned from past unsuccessful renovation projects. Furthermore, it can analyze the trends of the user's past renovation projects and propose the optimal proposal method. In this way, the accuracy of proposals is improved by selecting the optimal proposal method based on past history.

[0100] The planning department can estimate the user's emotions and adjust the level of detail in the budget plan based on those estimates. For example, if the user is stressed, a simplified budget plan can be provided and created quickly. If the user is relaxed, a detailed budget plan can be provided, including comprehensive content. Furthermore, if the user is in a hurry, only the most important items can be prioritized and included in the budget plan. This improves the acceptability of the plan by providing a budget plan that is tailored to the user's emotions.

[0101] The service provider can select the optimal delivery method by considering the user's device information. For example, if the user is using a smartphone, they can provide a delivery method that matches the screen size. If the user is using a tablet, they can provide a delivery method optimized for the larger screen. Furthermore, if the user is using a smartwatch, they can provide a concise and highly visible delivery method. By providing a delivery method based on device information, they can provide a budget plan optimized for the user's device.

[0102] The reception desk can estimate the user's emotions and adjust the input confirmation method based on those emotions. For example, if the user is stressed, it can provide a concise confirmation method to allow for quick completion. If the user is relaxed, it can provide a detailed confirmation method to allow them to review all input. Furthermore, if the user is in a hurry, it can allow them to review only the most important input and leave the rest for later. This allows for efficient input confirmation by providing confirmation methods tailored to the user's emotions.

[0103] The analysis unit can prioritize analyses based on the progress of the renovation project. For example, for ongoing projects, it can perform detailed analyses and provide comprehensive results. For projects in the early stages, it can perform simplified analyses and provide results quickly. Furthermore, for ongoing projects, it can provide only the most important analysis results, with other results provided later. This allows for efficient analysis results by performing analyses according to the project's progress.

[0104] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, it can delay the suggestion and wait until the user is relaxed. Conversely, if the user is relaxed, it can speed up the suggestion and deliver it quickly. Furthermore, if the user is in a hurry, it can prioritize only the most important suggestions and deliver others later. This improves the likelihood of suggestions being accepted by setting the timing of suggestions according to the user's emotions.

[0105] The planning department can analyze the user's past renovation project history and select the optimal method for creating a budget plan. For example, it can refer to the budget plans of the user's past successful renovation projects. It can also adjust the plan by incorporating lessons learned from past unsuccessful renovation projects. Furthermore, it can analyze the trends of the user's past renovation projects and propose the optimal budget plan. By selecting the best planning method based on past history, the accuracy of the plan is improved.

[0106] The delivery unit can estimate the user's emotions and adjust how the budget plan is delivered based on those estimates. For example, if the user is stressed, a simple and easy-to-understand budget plan can be provided. If the user is relaxed, a budget plan with more detailed information can be provided. Furthermore, if the user is in a hurry, a budget plan that gets straight to the point can be provided. This improves the acceptability of the budget plan by providing a delivery method that suits the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives the user's renovation project details. The user enters information about the building to be renovated, the desired renovations, the budget limit, etc. The reception desk saves this information to the database and sends it to the analysis department. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis unit identifies the necessary expenditures for the renovation project and proposes specific methods for cost reduction. For example, this may include selecting building materials and adjusting the construction schedule. Step 3: The proposal team proposes expenditure and cost reduction plans based on the information analyzed by the analysis team. The proposal team proposes a method to achieve high-quality renovation while reducing costs by using building materials suggested by the generation AI. Step 4: The Planning Department creates an efficient budget plan based on the proposals made by the Proposal Department. The Planning Department creates a budget plan that minimizes wasteful spending based on the expenditures and cost reduction proposals suggested by the Generating AI. Step 5: The provisioning department provides the user with the budget plan created by the planning department. The provisioning department provides the user with an efficient budget plan based on the suggestions made by the generation AI.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, planning unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs details of the renovation project. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the information received from the reception unit using generating AI. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes expenditure and cost reduction plans based on the information analyzed by the analysis unit. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which creates an efficient budget plan based on the content proposed by the proposal unit. The provision unit is implemented by, for example, the output device 40 of the smart device 14, which provides the budget plan created by the planning unit to the user. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, planning unit, and delivery unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs details of the renovation project. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the information received from the reception unit using generating AI. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes expenditure and cost reduction plans based on the information analyzed by the analysis unit. The planning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which creates an efficient budget plan based on the content proposed by the proposal unit. The delivery unit is implemented, for example, by the speaker 240 of the smart glasses 214, which provides the budget plan created by the planning unit to the user. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, planning unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs details of the renovation project. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the information received from the reception unit using generating AI. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes expenditure and cost reduction plans based on the information analyzed by the analysis unit. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which creates an efficient budget plan based on the content proposed by the proposal unit. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, which provides the user with the budget plan created by the planning unit. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, planning unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs details of the renovation project. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the information received from the reception unit using generating AI. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes expenditure and cost reduction plans based on the information analyzed by the analysis unit. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which creates an efficient budget plan based on the content proposed by the proposal unit. The provision unit is implemented by, for example, the speaker 240 of the robot 414, which provides the user with the budget plan created by the planning unit. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The reception desk handles inquiries about renovation projects, An analysis unit that analyzes the information received by the reception unit, Based on the information analyzed by the aforementioned analysis unit, the proposal unit proposes expenditure and cost reduction plans. The Planning Department prepares a budget plan based on the proposals made by the aforementioned Proposal Department, The system comprises a provisioning unit that provides the budget plan created by the planning unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the interface for entering details about the renovation project based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is We analyze the user's past renovation project history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When entering details for a renovation project, the input fields are customized based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of project details to input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter details for a renovation project, the system prioritizes input of highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter details about a renovation project, the system analyzes their social media activity and prompts them to input relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During the analysis, the level of detail is adjusted based on the importance of the renovation project. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the renovation project. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the submission date of the renovation project. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relevance of the renovation projects. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the renovation project. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the category of the renovation project. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When submitting proposals, we prioritize them based on the submission deadline for the renovation project. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the renovation project. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planning department, It estimates user emotions and adjusts how budget plans are created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, When creating a budget plan, we analyze the user's past renovation project history to select the optimal planning method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, When creating a budget plan, customize the planning method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning department, It estimates user emotions and prioritizes budget plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned planning department, When creating a budget plan, the optimal planning method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned planning department, When creating a budget plan, we analyze users' social media activity and propose methods for planning. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the budget plan is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a budget plan, the system will refer to the user's past renovation project history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which budget plans are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing budget plans, the optimal delivery method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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. The reception desk handles inquiries about renovation projects, An analysis unit that analyzes the information received by the reception unit, Based on the information analyzed by the aforementioned analysis unit, the proposal unit proposes expenditure and cost reduction plans. The Planning Department prepares a budget plan based on the proposals made by the aforementioned Proposal Department, The system comprises a provisioning unit that provides the budget plan created by the planning unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the interface for entering details about the renovation project based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is We analyze the user's past renovation project history and suggest the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When entering details for a renovation project, the input fields are customized based on the user's current living situation and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of project details to input based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter details for a renovation project, the system prioritizes input of highly relevant information, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users enter details about a renovation project, the system analyzes their social media activity and prompts them to input relevant information. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on those estimated emotions. The system according to feature 1.

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