system

Abstract

The system according to this embodiment aims to select components that are in line with the budget and to balance cost, strength, and delivery time. [Solution] The system according to the embodiment comprises a component selection unit, an advice unit, a simulation unit, and a database linkage unit. The component selection unit selects the optimal components based on the budget provided by the user. The advice unit provides specific advice considering the strength and delivery time of the components selected by the component selection unit. The simulation unit displays the simulation results in real time, corresponding to design changes and budget adjustments based on the advice provided by the advice unit. The database linkage unit supports decision-making based on the simulation results displayed by the simulation unit, taking into account the latest material prices and delivery periods.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to select members commensurate with the budget and balance cost, strength, and delivery date, and there is room for improvement.

[0005] The system according to the embodiment aims to select members commensurate with the budget and balance cost, strength, and delivery date.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a component selection unit, an advice unit, a simulation unit, and a database linkage unit. The component selection unit selects the optimal components based on the budget provided by the user. The advice unit provides specific advice considering the strength and delivery time of the components selected by the component selection unit. The simulation unit displays the simulation results in real time, corresponding to design changes and budget adjustments based on the advice provided by the advice unit. The database linkage unit supports decision-making based on the simulation results displayed by the simulation unit, taking into account the latest material prices and delivery periods. [Effects of the Invention]

[0007] The system according to this embodiment allows for component selection that fits the budget and balances cost, strength, and delivery time. [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 signed communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 4>

[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 reception 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 reception 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 cost-efficient AI design agent system according to an embodiment of the present invention is a system that streamlines the selection of materials in construction projects and simplifies budget management. This cost-efficient AI design agent system uses AI to select the optimal materials based on a budget provided by the user. Next, the AI ​​considers the strength and delivery time of the selected materials and provides specific advice for realizing an economical and efficient construction project. Furthermore, it displays simulation results in real time in response to design changes and budget adjustments. Finally, it links with an economics database to support decisions that take into account the latest material prices and delivery times. This mechanism simplifies budget management, promotes economical construction design, and achieves optimal quality and efficiency within budget. For example, based on a budget provided by the user, the AI ​​selects the optimal materials. In this process, the AI ​​considers a balanced approach to factors such as material properties, processability, durability, cost, quality, and supply source. For example, in a construction project, high-strength materials are selected for parts requiring strength, while low-cost materials are selected for other parts to reduce costs. In this way, the optimal materials can be selected within budget. Next, the AI ​​considers the strength and delivery time of the selected components and provides specific advice to realize an economical and efficient construction project. For example, if the selected components lack sufficient strength, the AI ​​will suggest reinforcement methods to ensure strength. Also, if the delivery time is long, it will suggest alternative components to shorten the delivery period. In this way, users can efficiently select components while receiving specific advice. Furthermore, it displays simulation results in real time in response to design changes and budget adjustments. For example, if a user makes a design change, the AI ​​immediately performs a simulation corresponding to that change and displays the results. This allows users to check the impact of the design change in real time and respond quickly. Also, if a budget adjustment is necessary, the AI ​​re-selects the optimal components within the budget and displays the results. In this way, users can efficiently proceed with the project while checking the simulation results in real time. Finally, it links with an economics database to support decision-making that takes into account the latest material prices and delivery times.For example, the AI ​​retrieves the latest material prices and delivery time information from an economics database and selects components based on that information. This allows users to always make decisions based on the most up-to-date information. In this way, economical and efficient construction projects can be realized. As a result, the cost-efficient AI design agent system becomes an important tool for architects, project managers, construction companies, and civil engineers. This makes the cost-efficient AI design agent system more efficient in selecting components in construction projects and simplifies budget management.

[0029] The cost-efficient AI design agent system according to this embodiment comprises a component selection unit, an advice unit, a simulation unit, and a database linkage unit. The component selection unit selects the optimal components based on the budget provided by the user. The component selection unit selects components by considering, for example, material properties, processability, durability, cost, quality, and supply source in a balanced manner. For example, in a building project, the component selection unit can select high-strength materials for parts where strength is required and low-cost materials for other parts to reduce costs. The component selection unit can also use AI to select components by considering, in a balanced manner, factors such as material properties, processability, durability, cost, quality, and supply source. The advice unit provides specific advice considering the strength and delivery time of the components selected by the component selection unit. For example, if the strength of the selected components is insufficient, the advice unit proposes reinforcement methods to ensure strength. For example, the advice unit can propose methods such as adding reinforcing materials or changing the arrangement of components to ensure strength. Furthermore, if the delivery time of the selected components is long, the advice unit proposes alternative components to shorten the delivery period. For example, the advice unit can suggest methods for procuring materials from other sources or selecting materials with shorter delivery times to shorten the delivery period. The simulation unit displays simulation results in real time based on the advice provided by the advice unit, corresponding to design changes and budget adjustments. For example, if a user makes a design change, the simulation unit immediately performs a simulation corresponding to that change and displays the results. For example, the simulation unit can simulate changes in strength and cost fluctuations due to design changes and display the results in real time. Also, if budget adjustments are necessary, the simulation unit re-selects the optimal materials within the budget and displays the results. For example, the simulation unit can re-select the optimal materials within the budget and display the results in real time. The database integration unit supports decision-making based on the simulation results displayed by the simulation unit, taking into account the latest material prices and delivery periods.The database integration unit, for example, obtains the latest material prices and delivery time information from the economics database and selects components based on that information. For example, the database integration unit can obtain the latest material prices and delivery time information in real time and select components based on that information. As a result, the cost-efficient AI design agent system according to the embodiment can select the optimal components based on the budget provided by the user, provide specific advice considering strength and delivery time, display simulation results in real time in response to design changes and budget adjustments, and support decision-making that takes into account the latest material prices and delivery times.

[0030] The component selection unit selects the optimal components based on the budget provided by the user. The unit selects components by considering a balanced approach, taking into account factors such as material properties, processability, durability, cost, quality, and supply source. Specifically, in a building project, the component selection unit can select high-strength materials for areas requiring strength and low-cost materials for other areas to reduce costs. For example, high-strength concrete might be used for the building's foundation, while lightweight, cost-effective materials are used for the interior. Furthermore, the component selection unit can utilize AI to select components by considering a balanced approach, taking into account factors such as material properties, processability, durability, cost, quality, and supply source. The AI ​​analyzes past data and market trends to quickly select the optimal components. For example, based on past project data, the AI ​​compares the performance and cost of components used under similar conditions and presents the best option. The AI ​​also monitors market price fluctuations and supply conditions in real time, enabling the procurement of components at the optimal time. This allows the component selection unit to select the optimal components within the user's budget, maximizing the cost efficiency of the project. Furthermore, the component selection unit provides detailed information about the selected components, making it easier for users to understand the reasons for their selection. For example, it provides detailed explanations of the characteristics, advantages, and cost breakdown of the selected components, enabling users to accept the selection with confidence. This allows the component selection unit to select the optimal components to meet the user's needs and contribute to the success of the project.

[0031] The advisory department provides specific advice, taking into account the strength and delivery time of the components selected by the component selection department. For example, if the strength of the selected components is insufficient, the advisory department will propose reinforcement methods to ensure sufficient strength. For instance, the advisory department may propose adding reinforcing materials or changing the arrangement of components to ensure sufficient strength. Specifically, they may propose methods to improve the overall strength by adding reinforcing materials to the columns and beams of the building, or methods to distribute the load by optimizing the arrangement of components. Furthermore, if the delivery time of the selected components is long, the advisory department will propose alternative components to shorten the delivery period. For example, they may propose methods to shorten the delivery period by procuring components from other sources or selecting components with shorter delivery times. Specifically, they may propose methods to obtain quotes from multiple suppliers and select the supplier that can deliver the fastest, or select components with ample stock. In addition, the advisory department also provides advice on the quality and cost of the selected components. For example, they will compare high-quality but high-cost components with slightly lower-quality but lower-cost components and propose the best option for the project requirements. This allows the advisory department to provide specific advice to help users select the optimal components and contribute to the success of their projects. Furthermore, the advisory department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it can revise its advice based on user feedback regarding the performance and delivery status of components actually used, and incorporate this feedback into future projects. In this way, the advisory department can always provide optimal advice based on the latest information and user needs, contributing to the success of projects.

[0032] The simulation unit displays simulation results in real time based on the advice provided by the advice unit, corresponding to design changes and budget adjustments. For example, if a user makes a design change, the simulation unit immediately performs a simulation corresponding to that change and displays the results. Specifically, it can simulate changes in strength and cost fluctuations due to design changes and display the results in real time. For example, if the type or arrangement of materials used changes due to a design change in a building, the simulation unit immediately simulates the impact and visualizes the changes in strength and cost. Furthermore, if budget adjustments are necessary, the simulation unit re-selects the optimal materials within the budget and displays the results. For example, it can re-select the optimal materials within the budget and display the results in real time. Specifically, it selects the optimal materials to ensure the necessary strength and quality while keeping costs down, according to budget constraints, and displays the selection results as simulation results. In addition, the simulation unit can simulate multiple scenarios. For example, by creating multiple scenarios with different design changes and budget adjustments and comparing the simulation results based on each scenario, the optimal choice can be found. In this way, the simulation unit can provide support to users in making the optimal choices in response to design changes and budget adjustments. Furthermore, the simulation unit provides tools to display simulation results in an easily understandable visual format. For example, it uses graphs and charts to visually represent fluctuations in strength and cost, allowing users to intuitively understand the results. This enables the simulation unit to support users in making optimal choices quickly and accurately in response to design changes and budget adjustments.

[0033] The database integration unit supports decision-making based on the latest material prices and delivery periods, taking into account the simulation results displayed by the simulation unit. For example, the database integration unit obtains the latest material price and delivery period information from the economics database and selects components based on that information. Specifically, the database integration unit can obtain the latest material price and delivery period information in real time and select components based on that information. For example, in a construction project, if material prices fluctuate rapidly, the database integration unit can immediately obtain the latest price information and re-select the optimal components. Also, if the delivery period changes, the database integration unit can obtain the latest delivery period information and select components that are optimal for the project schedule. Furthermore, the database integration unit can integrate with multiple databases. For example, it can obtain information from multiple databases, such as a database providing material price information, a database providing delivery period information, and a database providing quality information, and integrate that information to select the optimal components. This allows the database integration unit to support users in making optimal decisions based on the latest information. In addition, the database integration unit shares the acquired information with the simulation unit and the advisory unit, strengthening the overall system integration. For example, by providing the latest material price information to the simulation department and reflecting it in the simulation results, more accurate simulation results can be obtained. Furthermore, by providing the latest delivery period information to the advice department, specific advice that takes delivery periods into consideration can be provided. This allows the database integration department to strengthen the overall system integration and provide support for users to make optimal decisions.

[0034] The component selection unit can select components by considering a balanced approach to material properties, processability, durability, cost, quality, and supply source. For example, considering material properties, the component selection unit can select high-strength materials for areas requiring strength. For instance, in a building project, the component selection unit can select high-strength steel for areas requiring strength. The component selection unit can also select easily processable materials by considering processability. For example, it can select easily processable aluminum. Furthermore, the component selection unit can select materials that can be used for a long period by considering durability. For example, it can select highly durable concrete. This enables optimal component selection by considering a balanced approach to material properties, processability, durability, cost, quality, and supply source. Some or all of the above-described processes in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input data such as material properties, processability, durability, cost, quality, and supply source into a generating AI, and have the generating AI perform the optimal component selection.

[0035] The advisory unit can propose reinforcement methods to ensure sufficient strength if the selected member's strength is insufficient. For example, the advisory unit can propose adding reinforcing materials to ensure strength. For example, the advisory unit can ensure strength by adding reinforcing materials to the selected member. The advisory unit can also propose changing the arrangement of the members. For example, the advisory unit can ensure strength by changing the arrangement of the members. The advisory unit can also propose changing the shape of the members. For example, the advisory unit can ensure strength by changing the shape of the members. In this way, if the selected member's strength is insufficient, strength can be ensured by proposing reinforcement methods to ensure strength. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without using AI. For example, the advisory unit can input strength data of the selected member into a generating AI and have the generating AI execute a proposal for a reinforcement method to ensure strength.

[0036] The advisory unit can propose alternative components to shorten the delivery period if the delivery time for selected components is long. For example, the advisory unit can propose a method of procuring components from other sources to shorten the delivery period. For example, the advisory unit can shorten the delivery period by procuring components from sources with shorter delivery periods. The advisory unit can also propose a method of selecting components with shorter delivery periods. For example, the advisory unit can shorten the delivery period by selecting components with shorter delivery periods. The advisory unit can also propose a method of delivering some components in advance to shorten the delivery period. For example, the advisory unit can shorten the delivery period by delivering some components in advance. In this way, if the delivery time for selected components is long, the delivery period can be shortened by proposing alternative components to shorten the delivery period. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without using AI. For example, the advice unit can input delivery time data for selected components into the generating AI and have the AI ​​propose alternative components to shorten the delivery period.

[0037] The simulation unit can perform simulations in response to design changes and display the results in real time. For example, if a user makes a design change, the simulation unit can immediately perform a simulation in response to that change and display the results. For example, the simulation unit can simulate changes in strength and cost fluctuations due to design changes and display the results in real time. The simulation unit can also simulate changes in delivery periods due to design changes and display the results in real time. For example, the simulation unit can simulate changes in delivery periods due to design changes and display the results in real time. This allows for quick confirmation of the impact of design changes by performing simulations in response to design changes and displaying the results in real time. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input design change data into a generating AI and have the generating AI execute a simulation due to the design change.

[0038] The simulation unit can re-select the optimal components within the budget if budget adjustments are necessary, and display the results. For example, the simulation unit can re-select the optimal components within the budget if budget adjustments are necessary, and display the results in real time. For example, the simulation unit can re-select the optimal components within the budget and display the results in real time. The simulation unit can also simulate cost fluctuations due to budget adjustments and display the results in real time. For example, the simulation unit can simulate cost fluctuations due to budget adjustments and display the results in real time. This makes it possible to select the optimal components within the budget by re-selecting the optimal components within the budget and displaying the results when budget adjustments are necessary. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input budget adjustment data into a generating AI and have the generating AI perform the optimal component selection within the budget.

[0039] The database integration unit can obtain the latest material prices and delivery time information from the economics database and select components based on that information. For example, the database integration unit can obtain the latest material prices and delivery time information from the economics database in real time and select components based on that information. For example, the database integration unit can obtain the latest material prices and delivery time information in real time and select components based on that information. The database integration unit can also optimize component selection based on the information obtained from the economics database. For example, the database integration unit can optimize component selection based on the information obtained from the economics database. This makes it possible to select components based on the latest information by obtaining the latest material prices and delivery time information from the economics database and selecting components based on that information. Some or all of the above processing in the database integration unit may be performed using AI, for example, or without using AI. For example, the database integration unit can input the information obtained from the economics database into a generating AI and have the generating AI perform component selection.

[0040] The component selection unit can analyze the user's past project history and select the optimal component selection method during component selection. For example, the component selection unit can prioritize selecting components that have been successful in similar projects based on the user's past component usage history. The component selection unit can also analyze past project failures of the user and select components to avoid the same failures. The component selection unit can also analyze the cost performance of past projects of the user and select the most economical components. In this way, by analyzing the user's past project history, the optimal component selection method can be selected. Some or all of the above processing in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input the user's past project history data into a generating AI and have the AI ​​select the optimal component selection method.

[0041] The component selection unit can select components based on the user's current project progress. For example, in the early stages of a project, the component selection unit can select inexpensive components to keep costs down. In the middle stages of a project, the component selection unit can select mid-priced components to ensure quality. In the final stages of a project, the component selection unit can select components that are readily available and prioritize delivery time. This allows for optimal component selection based on the user's current project progress. Some or all of the above-described processes in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input data on the user's current project progress into the generating AI, and have the generating AI perform the component selection.

[0042] The component selection unit can prioritize the selection of components that are highly relevant, taking into account the user's geographical location information. For example, if the user's project is located in a cold region, the component selection unit can select components with high cold resistance. For example, if the user's project is located in a humid region, the component selection unit can select components with high waterproofing properties. For example, if the user's project is located in a humid region, the component selection unit can select components with high waterproofing properties. For example, if the user's project is located in an urban area, the component selection unit can select components with noise reduction measures. For example, if the user's project is located in an urban area, the component selection unit can select components with noise reduction measures. By prioritizing the selection of components that are highly relevant, taking into account the user's geographical location information, it becomes possible to select the optimal components according to the project's location. Some or all of the above processing in the component selection unit may be performed using AI, for example, or without using AI. For example, the component selection unit can input the user's geographical location data into the generating AI and have the AI ​​select highly relevant components.

[0043] The component selection unit can analyze the user's social media activity and select relevant components during component selection. For example, the component selection unit can prioritize selecting components recommended by the user on social media. The component selection unit can also exclude components that the user avoids on social media. The component selection unit can also select components that align with current trends based on the user's social media activity. In this way, relevant components can be selected by analyzing the user's social media activity. Some or all of the above processing in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the selection of relevant components.

[0044] The advice unit can adjust the level of detail of the advice based on the importance of the component when providing advice. For example, the advice unit can provide detailed advice for important components. For example, the advice unit can provide concise advice for components of low importance. For example, the advice unit can provide concise advice for components of low importance. For example, the advice unit can provide advice with an appropriate level of detail for components of medium importance. In this way, by adjusting the level of detail of the advice based on the importance of the component, detailed advice is provided for important components and concise advice is provided for components of low importance. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input component importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0045] The advice unit can apply different advice algorithms depending on the category of the component when providing advice. For example, the advice unit can provide advice on strength and durability for structural components. For example, the advice unit can provide advice on aesthetics and cost for interior components. For example, the advice unit can provide advice on functionality and energy efficiency for equipment components. By applying different advice algorithms depending on the category of the component, the best advice for each category is provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input component category data into a generating AI and have the generating AI apply different advice algorithms.

[0046] The advice unit can determine the priority of advice based on the delivery date of the components when providing advice. For example, the advice unit can prioritize providing advice to components whose delivery date is approaching. The advice unit can also postpone providing advice to components whose delivery date is far off. The advice unit can also provide advice at an appropriate time for components with a medium delivery date. By determining the priority of advice based on the delivery date of the components, advice is prioritized for components whose delivery date is approaching. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input component delivery date data into a generating AI and have the generating AI determine the priority of advice.

[0047] The advice unit can adjust the order of advice based on the relationships between components when providing advice. For example, the advice unit can provide advice first to important components. The advice unit can also provide advice later to components with low relevance. The advice unit can also provide advice in an appropriate order to components with moderate relevance. By adjusting the order of advice based on the relationships between components, advice is provided first to important components. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input component relationship data into a generating AI and have the generating AI adjust the order of advice.

[0048] The simulation unit can optimize the current simulation by referring to past simulation data during the simulation process. For example, the simulation unit can refer to successful simulation results from similar projects based on past simulation data. The simulation unit can also analyze failure examples from past simulation data and perform simulations to avoid the same failures. The simulation unit can also analyze past simulation data and apply the most efficient simulation method. By optimizing the current simulation by referring to past simulation data, the optimal simulation is provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input past simulation data into a generating AI and have the generating AI perform the optimization of the current simulation.

[0049] The simulation unit can improve the accuracy of the simulation by considering the interrelationships of the components. For example, the simulation unit can perform a simulation that optimizes strength and durability by considering the interrelationships of the components. The simulation unit can also perform a simulation that optimizes cost and quality by considering the interrelationships of the components. The simulation unit can also perform a simulation that optimizes delivery time and efficiency by considering the interrelationships of the components. By improving the accuracy of the simulation by considering the interrelationships of the components, an optimal simulation is provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input component interrelationship data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0050] The simulation unit can perform simulations while considering the geographical distribution of components. For example, the simulation unit can perform simulations to optimize transportation costs while considering the geographical distribution of components. The simulation unit can also perform simulations to optimize delivery times while considering the geographical distribution of components. The simulation unit can also perform simulations to minimize supply risks while considering the geographical distribution of components. By performing simulations while considering the geographical distribution of components, an optimal simulation is provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input geographical distribution data of components into a generating AI and have the generating AI execute the simulation.

[0051] The simulation unit can improve the accuracy of the simulation by referring to relevant literature on the components during the simulation. For example, the simulation unit can perform a simulation to optimize strength and durability by referring to relevant literature on the components. The simulation unit can also perform a simulation to optimize cost and quality by referring to relevant literature on the components. The simulation unit can also perform a simulation to optimize delivery time and efficiency by referring to relevant literature on the components. By improving the accuracy of the simulation by referring to relevant literature on the components, an optimal simulation is provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input relevant literature data on the components into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0052] The database integration unit can select the optimal information retrieval method by referring to past database usage history when integrating with a database. For example, the database integration unit can prioritize retrieving information that the user has frequently used in the past. The database integration unit can also retrieve information required for similar projects from the user's past usage history. The database integration unit can also analyze the user's past usage history and select the most efficient information retrieval method. This enables efficient information retrieval by selecting the optimal information retrieval method by referring to past database usage history. Some or all of the above processing in the database integration unit may be performed using AI, for example, or without AI. For example, the database integration unit can input past database usage history data into a generating AI and have the generating AI select the optimal information retrieval method.

[0053] The database integration unit can acquire the latest information on materials in real time during database integration and reflect it in the selection process. For example, the database integration unit can acquire the latest price information on materials in real time and reflect it in the selection process. The database integration unit can also acquire the latest delivery period information on materials in real time and reflect it in the selection process. The database integration unit can also acquire the latest supply status of materials in real time and reflect it in the selection process. By acquiring the latest information on materials in real time and reflecting it in the selection process, it becomes possible to select the optimal materials based on the latest information. Some or all of the above-described processes in the database integration unit may be performed using AI, for example, or without AI. For example, the database integration unit can input the latest information on materials into a generating AI and have the generating AI execute the process of reflecting it in the selection process.

[0054] The database integration unit can acquire information while considering the geographical distribution of components when integrating with the database. For example, the database integration unit can acquire information to optimize transportation costs while considering the geographical distribution of components. For example, the database integration unit can acquire information to optimize delivery times while considering the geographical distribution of components. For example, the database integration unit can acquire information to optimize delivery times while considering the geographical distribution of components. For example, the database integration unit can acquire information to minimize supply risks while considering the geographical distribution of components. For example, the database integration unit can acquire information to minimize supply risks while considering the geographical distribution of components. In this way, optimal information is provided by acquiring information while considering the geographical distribution of components. Some or all of the above processing in the database integration unit may be performed using AI, for example, or without using AI. For example, the database integration unit can input geographical distribution data of components into a generating AI and have the generating AI acquire the information.

[0055] The database integration unit can improve the accuracy of information by referring to relevant literature for components during database integration. For example, the database integration unit can refer to relevant literature for components to obtain information that optimizes strength and durability. The database integration unit can also refer to relevant literature for components to obtain information that optimizes cost and quality. The database integration unit can also refer to relevant literature for components to obtain information that optimizes delivery time and efficiency. By improving the accuracy of information by referring to relevant literature for components, optimal information is provided. Some or all of the above processing in the database integration unit may be performed using AI, for example, or without AI. For example, the database integration unit can input relevant literature data for components into a generating AI and have the generating AI perform information accuracy improvement.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The component selection unit can analyze the user's past project history and select the optimal component selection method. For example, it can prioritize selecting components that have been successful in similar projects based on the user's past component usage history. It can also analyze past project failures and select components to avoid the same mistakes. Furthermore, it can analyze the cost-effectiveness of past projects and select the most economical components. In this way, the optimal component selection method can be selected by analyzing the user's past project history. Some or all of the above processing in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input the user's past project history data into a generating AI and have the generating AI perform the selection of the optimal component selection method.

[0058] The advice unit can adjust the level of detail of the advice based on the importance of the component when providing advice. For example, it can provide detailed advice for important components, concise advice for less important components, and advice of moderate importance for components. By adjusting the level of detail of the advice based on the importance of the components, detailed advice is provided for important components, and concise advice is provided for less important components. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input component importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0059] The simulation unit can optimize the current simulation by referring to past simulation data during the simulation process. For example, it can refer to successful simulation results from similar projects based on past simulation data. It can also analyze failure examples from past simulation data and perform simulations to avoid the same failures. Furthermore, it can analyze past simulation data and apply the most efficient simulation method. In this way, an optimal simulation is provided by optimizing the current simulation by referring to past simulation data. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input past simulation data into a generating AI and have the generating AI perform the optimization of the current simulation.

[0060] The simulation unit can improve the accuracy of simulations by considering the interrelationships of components. For example, it can perform simulations that optimize strength and durability by considering the interrelationships of components. It can also perform simulations that optimize cost and quality by considering the interrelationships of components. Furthermore, it can perform simulations that optimize delivery time and efficiency by considering the interrelationships of components. In this way, by improving the accuracy of simulations by considering the interrelationships of components, an optimal simulation is provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input component interrelationship data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0061] The database integration unit can select the optimal information retrieval method by referring to past database usage history during database integration. For example, it can prioritize the retrieval of information that the user has frequently used in the past. It can also retrieve information that was needed in similar projects based on the user's past usage history. Furthermore, it can analyze the user's past usage history and select the most efficient information retrieval method. This enables efficient information retrieval by selecting the optimal information retrieval method by referring to past database usage history. Some or all of the above processing in the database integration unit may be performed using AI, for example, or without AI. For example, the database integration unit can input past database usage history data into a generating AI and have the generating AI select the optimal information retrieval method.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The component selection unit selects the optimal components based on the budget provided by the user. The component selection unit selects components by considering a balanced approach to material properties, processability, durability, cost, quality, and supply source. For example, in a building project, high-strength materials can be selected for parts requiring strength, while low-cost materials can be selected for other parts to reduce costs. AI can also be used to select components by considering these factors in a balanced way. Step 2: The advice department provides specific advice, taking into account the strength and delivery time of the components selected by the component selection department. For example, if the strength of the selected components is insufficient, they will propose reinforcement methods to ensure sufficient strength. If the delivery time is long, they will propose alternative components to shorten the delivery period. Step 3: The simulation unit displays simulation results in real time, based on the advice provided by the advice unit, in response to design changes and budget adjustments. For example, if a user makes a design change, the simulation immediately performs a simulation corresponding to that change and displays the results. If a budget adjustment is necessary, the unit re-selects the optimal components within the budget and displays the results. Step 4: The database integration unit supports decision-making based on the simulation results displayed by the simulation unit, taking into account the latest material prices and delivery times. For example, it retrieves the latest material price and delivery time information from the economics database and selects components based on that information.

[0064] (Example of form 2) The cost-efficient AI design agent system according to an embodiment of the present invention is a system that streamlines the selection of materials in construction projects and simplifies budget management. This cost-efficient AI design agent system uses AI to select the optimal materials based on a budget provided by the user. Next, the AI ​​considers the strength and delivery time of the selected materials and provides specific advice for realizing an economical and efficient construction project. Furthermore, it displays simulation results in real time in response to design changes and budget adjustments. Finally, it links with an economics database to support decisions that take into account the latest material prices and delivery times. This mechanism simplifies budget management, promotes economical construction design, and achieves optimal quality and efficiency within budget. For example, based on a budget provided by the user, the AI ​​selects the optimal materials. In this process, the AI ​​considers a balanced approach to factors such as material properties, processability, durability, cost, quality, and supply source. For example, in a construction project, high-strength materials are selected for parts requiring strength, while low-cost materials are selected for other parts to reduce costs. In this way, the optimal materials can be selected within budget. Next, the AI ​​considers the strength and delivery time of the selected components and provides specific advice to realize an economical and efficient construction project. For example, if the selected components lack sufficient strength, the AI ​​will suggest reinforcement methods to ensure strength. Also, if the delivery time is long, it will suggest alternative components to shorten the delivery period. In this way, users can efficiently select components while receiving specific advice. Furthermore, it displays simulation results in real time in response to design changes and budget adjustments. For example, if a user makes a design change, the AI ​​immediately performs a simulation corresponding to that change and displays the results. This allows users to check the impact of the design change in real time and respond quickly. Also, if a budget adjustment is necessary, the AI ​​re-selects the optimal components within the budget and displays the results. In this way, users can efficiently proceed with the project while checking the simulation results in real time. Finally, it links with an economics database to support decision-making that takes into account the latest material prices and delivery times.For example, the AI ​​retrieves the latest material prices and delivery time information from an economics database and selects components based on that information. This allows users to always make decisions based on the most up-to-date information. In this way, economical and efficient construction projects can be realized. As a result, the cost-efficient AI design agent system becomes an important tool for architects, project managers, construction companies, and civil engineers. This makes the cost-efficient AI design agent system more efficient in selecting components in construction projects and simplifies budget management.

[0065] The cost-efficient AI design agent system according to this embodiment comprises a component selection unit, an advice unit, a simulation unit, and a database linkage unit. The component selection unit selects the optimal components based on the budget provided by the user. The component selection unit selects components by considering, for example, material properties, processability, durability, cost, quality, and supply source in a balanced manner. For example, in a building project, the component selection unit can select high-strength materials for parts where strength is required and low-cost materials for other parts to reduce costs. The component selection unit can also use AI to select components by considering, in a balanced manner, factors such as material properties, processability, durability, cost, quality, and supply source. The advice unit provides specific advice considering the strength and delivery time of the components selected by the component selection unit. For example, if the strength of the selected components is insufficient, the advice unit proposes reinforcement methods to ensure strength. For example, the advice unit can propose methods such as adding reinforcing materials or changing the arrangement of components to ensure strength. Furthermore, if the delivery time of the selected components is long, the advice unit proposes alternative components to shorten the delivery period. For example, the advice unit can suggest methods for procuring materials from other sources or selecting materials with shorter delivery times to shorten the delivery period. The simulation unit displays simulation results in real time based on the advice provided by the advice unit, corresponding to design changes and budget adjustments. For example, if a user makes a design change, the simulation unit immediately performs a simulation corresponding to that change and displays the results. For example, the simulation unit can simulate changes in strength and cost fluctuations due to design changes and display the results in real time. Also, if budget adjustments are necessary, the simulation unit re-selects the optimal materials within the budget and displays the results. For example, the simulation unit can re-select the optimal materials within the budget and display the results in real time. The database integration unit supports decision-making based on the simulation results displayed by the simulation unit, taking into account the latest material prices and delivery periods.The database integration unit, for example, obtains the latest material prices and delivery time information from the economics database and selects components based on that information. For example, the database integration unit can obtain the latest material prices and delivery time information in real time and select components based on that information. As a result, the cost-efficient AI design agent system according to the embodiment can select the optimal components based on the budget provided by the user, provide specific advice considering strength and delivery time, display simulation results in real time in response to design changes and budget adjustments, and support decision-making that takes into account the latest material prices and delivery times.

[0066] The component selection unit selects the optimal components based on the budget provided by the user. The unit selects components by considering a balanced approach, taking into account factors such as material properties, processability, durability, cost, quality, and supply source. Specifically, in a building project, the component selection unit can select high-strength materials for areas requiring strength and low-cost materials for other areas to reduce costs. For example, high-strength concrete might be used for the building's foundation, while lightweight, cost-effective materials are used for the interior. Furthermore, the component selection unit can utilize AI to select components by considering a balanced approach, taking into account factors such as material properties, processability, durability, cost, quality, and supply source. The AI ​​analyzes past data and market trends to quickly select the optimal components. For example, based on past project data, the AI ​​compares the performance and cost of components used under similar conditions and presents the best option. The AI ​​also monitors market price fluctuations and supply conditions in real time, enabling the procurement of components at the optimal time. This allows the component selection unit to select the optimal components within the user's budget, maximizing the cost efficiency of the project. Furthermore, the component selection unit provides detailed information about the selected components, making it easier for users to understand the reasons for their selection. For example, it provides detailed explanations of the characteristics, advantages, and cost breakdown of the selected components, enabling users to accept the selection with confidence. This allows the component selection unit to select the optimal components to meet the user's needs and contribute to the success of the project.

[0067] The advisory department provides specific advice, taking into account the strength and delivery time of the components selected by the component selection department. For example, if the strength of the selected components is insufficient, the advisory department will propose reinforcement methods to ensure sufficient strength. For instance, the advisory department may propose adding reinforcing materials or changing the arrangement of components to ensure sufficient strength. Specifically, they may propose methods to improve the overall strength by adding reinforcing materials to the columns and beams of the building, or methods to distribute the load by optimizing the arrangement of components. Furthermore, if the delivery time of the selected components is long, the advisory department will propose alternative components to shorten the delivery period. For example, they may propose methods to shorten the delivery period by procuring components from other sources or selecting components with shorter delivery times. Specifically, they may propose methods to obtain quotes from multiple suppliers and select the supplier that can deliver the fastest, or select components with ample stock. In addition, the advisory department also provides advice on the quality and cost of the selected components. For example, they will compare high-quality but high-cost components with slightly lower-quality but lower-cost components and propose the best option for the project requirements. This allows the advisory department to provide specific advice to help users select the optimal components and contribute to the success of their projects. Furthermore, the advisory department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it can revise its advice based on user feedback regarding the performance and delivery status of components actually used, and incorporate this feedback into future projects. In this way, the advisory department can always provide optimal advice based on the latest information and user needs, contributing to the success of projects.

[0068] The simulation unit displays simulation results in real time based on the advice provided by the advice unit, corresponding to design changes and budget adjustments. For example, if a user makes a design change, the simulation unit immediately performs a simulation corresponding to that change and displays the results. Specifically, it can simulate changes in strength and cost fluctuations due to design changes and display the results in real time. For example, if the type or arrangement of materials used changes due to a design change in a building, the simulation unit immediately simulates the impact and visualizes the changes in strength and cost. Furthermore, if budget adjustments are necessary, the simulation unit re-selects the optimal materials within the budget and displays the results. For example, it can re-select the optimal materials within the budget and display the results in real time. Specifically, it selects the optimal materials to ensure the necessary strength and quality while keeping costs down, according to budget constraints, and displays the selection results as simulation results. In addition, the simulation unit can simulate multiple scenarios. For example, by creating multiple scenarios with different design changes and budget adjustments and comparing the simulation results based on each scenario, the optimal choice can be found. In this way, the simulation unit can provide support to users in making the optimal choices in response to design changes and budget adjustments. Furthermore, the simulation unit provides tools to display simulation results in an easily understandable visual format. For example, it uses graphs and charts to visually represent fluctuations in strength and cost, allowing users to intuitively understand the results. This enables the simulation unit to support users in making optimal choices quickly and accurately in response to design changes and budget adjustments.

[0069] The database integration unit supports decision-making based on the latest material prices and delivery periods, taking into account the simulation results displayed by the simulation unit. For example, the database integration unit obtains the latest material price and delivery period information from the economics database and selects components based on that information. Specifically, the database integration unit can obtain the latest material price and delivery period information in real time and select components based on that information. For example, in a construction project, if material prices fluctuate rapidly, the database integration unit can immediately obtain the latest price information and re-select the optimal components. Also, if the delivery period changes, the database integration unit can obtain the latest delivery period information and select components that are optimal for the project schedule. Furthermore, the database integration unit can integrate with multiple databases. For example, it can obtain information from multiple databases, such as a database providing material price information, a database providing delivery period information, and a database providing quality information, and integrate that information to select the optimal components. This allows the database integration unit to support users in making optimal decisions based on the latest information. In addition, the database integration unit shares the acquired information with the simulation unit and the advisory unit, strengthening the overall system integration. For example, by providing the latest material price information to the simulation department and reflecting it in the simulation results, more accurate simulation results can be obtained. Furthermore, by providing the latest delivery period information to the advice department, specific advice that takes delivery periods into consideration can be provided. This allows the database integration department to strengthen the overall system integration and provide support for users to make optimal decisions.

[0070] The component selection unit can select components by considering a balanced approach to material properties, processability, durability, cost, quality, and supply source. For example, considering material properties, the component selection unit can select high-strength materials for areas requiring strength. For instance, in a building project, the component selection unit can select high-strength steel for areas requiring strength. The component selection unit can also select easily processable materials by considering processability. For example, it can select easily processable aluminum. Furthermore, the component selection unit can select materials that can be used for a long period by considering durability. For example, it can select highly durable concrete. This enables optimal component selection by considering a balanced approach to material properties, processability, durability, cost, quality, and supply source. Some or all of the above-described processes in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input data such as material properties, processability, durability, cost, quality, and supply source into a generating AI, and have the generating AI perform the optimal component selection.

[0071] The advisory unit can propose reinforcement methods to ensure sufficient strength if the selected member's strength is insufficient. For example, the advisory unit can propose adding reinforcing materials to ensure strength. For example, the advisory unit can ensure strength by adding reinforcing materials to the selected member. The advisory unit can also propose changing the arrangement of the members. For example, the advisory unit can ensure strength by changing the arrangement of the members. The advisory unit can also propose changing the shape of the members. For example, the advisory unit can ensure strength by changing the shape of the members. In this way, if the selected member's strength is insufficient, strength can be ensured by proposing reinforcement methods to ensure strength. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without using AI. For example, the advisory unit can input strength data of the selected member into a generating AI and have the generating AI execute a proposal for a reinforcement method to ensure strength.

[0072] The advisory unit can propose alternative components to shorten the delivery period if the delivery time for selected components is long. For example, the advisory unit can propose a method of procuring components from other sources to shorten the delivery period. For example, the advisory unit can shorten the delivery period by procuring components from sources with shorter delivery periods. The advisory unit can also propose a method of selecting components with shorter delivery periods. For example, the advisory unit can shorten the delivery period by selecting components with shorter delivery periods. The advisory unit can also propose a method of delivering some components in advance to shorten the delivery period. For example, the advisory unit can shorten the delivery period by delivering some components in advance. In this way, if the delivery time for selected components is long, the delivery period can be shortened by proposing alternative components to shorten the delivery period. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without using AI. For example, the advice unit can input delivery time data for selected components into the generating AI and have the AI ​​propose alternative components to shorten the delivery period.

[0073] The simulation unit can perform simulations in response to design changes and display the results in real time. For example, if a user makes a design change, the simulation unit can immediately perform a simulation in response to that change and display the results. For example, the simulation unit can simulate changes in strength and cost fluctuations due to design changes and display the results in real time. The simulation unit can also simulate changes in delivery periods due to design changes and display the results in real time. For example, the simulation unit can simulate changes in delivery periods due to design changes and display the results in real time. This allows for quick confirmation of the impact of design changes by performing simulations in response to design changes and displaying the results in real time. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input design change data into a generating AI and have the generating AI execute a simulation due to the design change.

[0074] The simulation unit can re-select the optimal components within the budget if budget adjustments are necessary, and display the results. For example, the simulation unit can re-select the optimal components within the budget if budget adjustments are necessary, and display the results in real time. For example, the simulation unit can re-select the optimal components within the budget and display the results in real time. The simulation unit can also simulate cost fluctuations due to budget adjustments and display the results in real time. For example, the simulation unit can simulate cost fluctuations due to budget adjustments and display the results in real time. This makes it possible to select the optimal components within the budget by re-selecting the optimal components within the budget and displaying the results when budget adjustments are necessary. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input budget adjustment data into a generating AI and have the generating AI perform the optimal component selection within the budget.

[0075] The database integration unit can obtain the latest material prices and delivery time information from the economics database and select components based on that information. For example, the database integration unit can obtain the latest material prices and delivery time information from the economics database in real time and select components based on that information. For example, the database integration unit can obtain the latest material prices and delivery time information in real time and select components based on that information. The database integration unit can also optimize component selection based on the information obtained from the economics database. For example, the database integration unit can optimize component selection based on the information obtained from the economics database. This makes it possible to select components based on the latest information by obtaining the latest material prices and delivery time information from the economics database and selecting components based on that information. Some or all of the above processing in the database integration unit may be performed using AI, for example, or without using AI. For example, the database integration unit can input the information obtained from the economics database into a generating AI and have the generating AI perform component selection.

[0076] The component selection unit estimates the user's emotions and adjusts the priority of component selection based on those estimated emotions. For example, if the user is stressed, the AI ​​will prioritize cost when selecting components and emphasize selection within budget. If the user is relaxed, the AI ​​will prioritize quality when selecting components and consider long-term durability. If the user is in a hurry, the AI ​​will prioritize delivery time when selecting components and emphasize rapid delivery. By adjusting the priority of component selection based on the user's emotions, it becomes possible to select the optimal components according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the priority of component selection.

[0077] The component selection unit can analyze the user's past project history and select the optimal component selection method during component selection. For example, the component selection unit can prioritize selecting components that have been successful in similar projects based on the user's past component usage history. The component selection unit can also analyze past project failures of the user and select components to avoid the same failures. The component selection unit can also analyze the cost performance of past projects of the user and select the most economical components. In this way, by analyzing the user's past project history, the optimal component selection method can be selected. Some or all of the above processing in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input the user's past project history data into a generating AI and have the AI ​​select the optimal component selection method.

[0078] The component selection unit can select components based on the user's current project progress. For example, in the early stages of a project, the component selection unit can select inexpensive components to keep costs down. In the middle stages of a project, the component selection unit can select mid-priced components to ensure quality. In the final stages of a project, the component selection unit can select components that are readily available and prioritize delivery time. This allows for optimal component selection based on the user's current project progress. Some or all of the above-described processes in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input data on the user's current project progress into the generating AI, and have the generating AI perform the component selection.

[0079] The component selection unit estimates the user's emotions and adjusts the timing of component selection based on the estimated emotions. For example, if the user is stressed, the AI ​​in the component selection unit quickly selects components to reduce the user's burden. Furthermore, if the user is relaxed, the AI ​​in the component selection unit carefully selects components to make the optimal selection. Also, if the user is in a hurry, the AI ​​in the component selection unit immediately selects components to ensure smooth project progress. This allows for component selection at the optimal time according to the user's situation by adjusting the timing of component selection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the component selection unit may be performed using AI, or not using AI. For example, the component selection unit can input user emotion data into the generating AI and have the generating AI adjust the timing of component selection.

[0080] The component selection unit can prioritize the selection of components that are highly relevant, taking into account the user's geographical location information. For example, if the user's project is located in a cold region, the component selection unit can select components with high cold resistance. For example, if the user's project is located in a humid region, the component selection unit can select components with high waterproofing properties. For example, if the user's project is located in a humid region, the component selection unit can select components with high waterproofing properties. For example, if the user's project is located in an urban area, the component selection unit can select components with noise reduction measures. For example, if the user's project is located in an urban area, the component selection unit can select components with noise reduction measures. By prioritizing the selection of components that are highly relevant, taking into account the user's geographical location information, it becomes possible to select the optimal components according to the project's location. Some or all of the above processing in the component selection unit may be performed using AI, for example, or without using AI. For example, the component selection unit can input the user's geographical location data into the generating AI and have the AI ​​select highly relevant components.

[0081] The component selection unit can analyze the user's social media activity and select relevant components during component selection. For example, the component selection unit can prioritize selecting components recommended by the user on social media. The component selection unit can also exclude components that the user avoids on social media. The component selection unit can also select components that align with current trends based on the user's social media activity. In this way, relevant components can be selected by analyzing the user's social media activity. Some or all of the above processing in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the selection of relevant components.

[0082] The advice unit estimates the user's emotions and adjusts the way it expresses the advice based on those emotions. For example, if the user is stressed, the advice unit can provide concise and clear advice. Furthermore, if the user is relaxed, the advice unit can provide detailed and thoughtful advice. Also, if the user is in a hurry, the advice unit can provide quick and to-the-point advice. By adjusting the way the advice is expressed based on the user's emotions, the system provides optimal advice tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into a generating AI and have the generating AI adjust the way the advice is expressed.

[0083] The advice unit can adjust the level of detail of the advice based on the importance of the component when providing advice. For example, the advice unit can provide detailed advice for important components. For example, the advice unit can provide concise advice for components of low importance. For example, the advice unit can provide concise advice for components of low importance. For example, the advice unit can provide advice with an appropriate level of detail for components of medium importance. In this way, by adjusting the level of detail of the advice based on the importance of the component, detailed advice is provided for important components and concise advice is provided for components of low importance. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input component importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0084] The advice unit can apply different advice algorithms depending on the category of the component when providing advice. For example, the advice unit can provide advice on strength and durability for structural components. For example, the advice unit can provide advice on aesthetics and cost for interior components. For example, the advice unit can provide advice on functionality and energy efficiency for equipment components. By applying different advice algorithms depending on the category of the component, the best advice for each category is provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input component category data into a generating AI and have the generating AI apply different advice algorithms.

[0085] The advice unit estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the advice unit can provide short, to-the-point advice. The advice unit can also provide detailed, longer advice if the user is relaxed. Furthermore, if the user is in a hurry, the advice unit can provide quick, concise advice. By adjusting the length of the advice based on the user's emotions, the system provides optimal advice tailored to the user's situation. 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-described processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into a generating AI and have the generating AI adjust the length of the advice.

[0086] The advice unit can determine the priority of advice based on the delivery date of the components when providing advice. For example, the advice unit can prioritize providing advice to components whose delivery date is approaching. The advice unit can also postpone providing advice to components whose delivery date is far off. The advice unit can also provide advice at an appropriate time for components with a medium delivery date. By determining the priority of advice based on the delivery date of the components, advice is prioritized for components whose delivery date is approaching. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input component delivery date data into a generating AI and have the generating AI determine the priority of advice.

[0087] The advice unit can adjust the order of advice based on the relationships between components when providing advice. For example, the advice unit can provide advice first to important components. The advice unit can also provide advice later to components with low relevance. The advice unit can also provide advice in an appropriate order to components with moderate relevance. By adjusting the order of advice based on the relationships between components, advice is provided first to important components. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input component relationship data into a generating AI and have the generating AI adjust the order of advice.

[0088] The simulation unit estimates the user's emotions and adjusts the display method of the simulation results based on the estimated emotions. The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated emotions. For example, if the user is stressed, the simulation unit can provide a simple and highly visible display method. The simulation unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the simulation unit can provide a display method that focuses on the essentials. By adjusting the display method of the simulation results based on the user's emotions, the optimal display method is provided according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generating AI and have the generating AI adjust how the simulation results are displayed.

[0089] The simulation unit can optimize the current simulation by referring to past simulation data during the simulation process. For example, the simulation unit can refer to successful simulation results from similar projects based on past simulation data. The simulation unit can also analyze failure examples from past simulation data and perform simulations to avoid the same failures. The simulation unit can also analyze past simulation data and apply the most efficient simulation method. By optimizing the current simulation by referring to past simulation data, the optimal simulation is provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input past simulation data into a generating AI and have the generating AI perform the optimization of the current simulation.

[0090] The simulation unit can improve the accuracy of the simulation by considering the interrelationships of the components. For example, the simulation unit can perform a simulation that optimizes strength and durability by considering the interrelationships of the components. The simulation unit can also perform a simulation that optimizes cost and quality by considering the interrelationships of the components. The simulation unit can also perform a simulation that optimizes delivery time and efficiency by considering the interrelationships of the components. By improving the accuracy of the simulation by considering the interrelationships of the components, an optimal simulation is provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input component interrelationship data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0091] The simulation unit estimates the user's emotions and adjusts the display order of the simulation results based on the estimated emotions. For example, if the user is stressed, the simulation unit can display important results first. The simulation unit can also display detailed results sequentially if the user is relaxed. Furthermore, if the user is in a hurry, the simulation unit can display concise results first. By adjusting the display order of the simulation results based on the user's emotions, the system provides an optimal display order tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generating AI and have the generating AI adjust the display order of the simulation results.

[0092] The simulation unit can perform simulations while considering the geographical distribution of components. For example, the simulation unit can perform simulations to optimize transportation costs while considering the geographical distribution of components. The simulation unit can also perform simulations to optimize delivery times while considering the geographical distribution of components. The simulation unit can also perform simulations to minimize supply risks while considering the geographical distribution of components. By performing simulations while considering the geographical distribution of components, an optimal simulation is provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input geographical distribution data of components into a generating AI and have the generating AI execute the simulation.

[0093] The simulation unit can improve the accuracy of the simulation by referring to relevant literature on the components during the simulation. For example, the simulation unit can perform a simulation to optimize strength and durability by referring to relevant literature on the components. The simulation unit can also perform a simulation to optimize cost and quality by referring to relevant literature on the components. The simulation unit can also perform a simulation to optimize delivery time and efficiency by referring to relevant literature on the components. By improving the accuracy of the simulation by referring to relevant literature on the components, an optimal simulation is provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input relevant literature data on the components into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0094] The database integration unit estimates the user's emotions and determines the priority of information to retrieve from the database based on the estimated emotions. For example, if the user is stressed, the database integration unit can prioritize retrieving important information. Furthermore, if the user is relaxed, the database integration unit can sequentially retrieve detailed information. Also, if the user is in a hurry, the database integration unit can prioritize retrieving concise information. This ensures that optimal information is provided based on the user's situation by prioritizing information retrieved from the database based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the database integration unit may be performed using AI, for example, or without AI. For example, the database integration unit can input user emotion data into a generating AI and have the generating AI determine the priority of the information.

[0095] The database integration unit can select the optimal information retrieval method by referring to past database usage history when integrating with a database. For example, the database integration unit can prioritize retrieving information that the user has frequently used in the past. The database integration unit can also retrieve information required for similar projects from the user's past usage history. The database integration unit can also analyze the user's past usage history and select the most efficient information retrieval method. This enables efficient information retrieval by selecting the optimal information retrieval method by referring to past database usage history. Some or all of the above processing in the database integration unit may be performed using AI, for example, or without AI. For example, the database integration unit can input past database usage history data into a generating AI and have the generating AI select the optimal information retrieval method.

[0096] The database integration unit can acquire the latest information on materials in real time during database integration and reflect it in the selection process. For example, the database integration unit can acquire the latest price information on materials in real time and reflect it in the selection process. The database integration unit can also acquire the latest delivery period information on materials in real time and reflect it in the selection process. The database integration unit can also acquire the latest supply status of materials in real time and reflect it in the selection process. By acquiring the latest information on materials in real time and reflecting it in the selection process, it becomes possible to select the optimal materials based on the latest information. Some or all of the above-described processes in the database integration unit may be performed using AI, for example, or without AI. For example, the database integration unit can input the latest information on materials into a generating AI and have the generating AI execute the process of reflecting it in the selection process.

[0097] The database integration unit estimates the user's emotions and adjusts the timing of information retrieved from the database based on the estimated emotions. For example, if the user is stressed, the database integration unit can quickly retrieve information. Furthermore, if the user is relaxed, the database integration unit can sequentially retrieve detailed information. Also, if the user is in a hurry, the database integration unit can quickly retrieve concise information. By adjusting the timing of information retrieved from the database based on the user's emotions, information is provided at the optimal timing according to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the database integration unit may be performed using AI, for example, or without AI. For example, the database integration unit can input user emotion data into a generating AI and have the generating AI adjust the timing of the information.

[0098] The database integration unit can acquire information while considering the geographical distribution of components when integrating with the database. For example, the database integration unit can acquire information to optimize transportation costs while considering the geographical distribution of components. For example, the database integration unit can acquire information to optimize delivery times while considering the geographical distribution of components. For example, the database integration unit can acquire information to optimize delivery times while considering the geographical distribution of components. For example, the database integration unit can acquire information to minimize supply risks while considering the geographical distribution of components. For example, the database integration unit can acquire information to minimize supply risks while considering the geographical distribution of components. In this way, optimal information is provided by acquiring information while considering the geographical distribution of components. Some or all of the above processing in the database integration unit may be performed using AI, for example, or without using AI. For example, the database integration unit can input geographical distribution data of components into a generating AI and have the generating AI acquire the information.

[0099] The database integration unit can improve the accuracy of information by referring to relevant literature for components during database integration. For example, the database integration unit can refer to relevant literature for components to obtain information that optimizes strength and durability. The database integration unit can also refer to relevant literature for components to obtain information that optimizes cost and quality. The database integration unit can also refer to relevant literature for components to obtain information that optimizes delivery time and efficiency. By improving the accuracy of information by referring to relevant literature for components, optimal information is provided. Some or all of the above processing in the database integration unit may be performed using AI, for example, or without AI. For example, the database integration unit can input relevant literature data for components into a generating AI and have the generating AI perform information accuracy improvement.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The component selection unit can estimate the user's emotions and adjust the priority of component selection based on the estimated user emotions. For example, if the user is stressed, the AI ​​can prioritize cost when selecting components and emphasize selection within budget. If the user is relaxed, the AI ​​can prioritize quality when selecting components and consider long-term durability. Furthermore, if the user is in a hurry, the AI ​​can prioritize delivery time when selecting components and emphasize rapid delivery. By adjusting the priority of component selection based on the user's emotions, it becomes possible to select the optimal components according to the user's situation. 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 component selection unit may be performed using AI or not using AI. For example, the component selection unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of component selection priorities.

[0102] The component selection unit can analyze the user's past project history and select the optimal component selection method. For example, it can prioritize selecting components that have been successful in similar projects based on the user's past component usage history. It can also analyze past project failures and select components to avoid the same mistakes. Furthermore, it can analyze the cost-effectiveness of past projects and select the most economical components. In this way, the optimal component selection method can be selected by analyzing the user's past project history. Some or all of the above processing in the component selection unit may be performed using AI, for example, or without AI. For example, the component selection unit can input the user's past project history data into a generating AI and have the generating AI perform the selection of the optimal component selection method.

[0103] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, it can provide concise and clear advice. If the user is relaxed, it can provide detailed and thoughtful advice. Furthermore, if the user is in a hurry, it can provide quick and to-the-point advice. By adjusting the way it expresses advice based on the user's emotions, it can provide optimal advice tailored to the user's situation. 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 advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the way it expresses the advice.

[0104] The advice unit can adjust the level of detail of the advice based on the importance of the component when providing advice. For example, it can provide detailed advice for important components, concise advice for less important components, and advice of moderate importance for components. By adjusting the level of detail of the advice based on the importance of the components, detailed advice is provided for important components, and concise advice is provided for less important components. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input component importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0105] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the simulation results based on the user's emotions, the optimal display method according to the user's situation is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the simulation results.

[0106] The simulation unit can optimize the current simulation by referring to past simulation data during the simulation process. For example, it can refer to successful simulation results from similar projects based on past simulation data. It can also analyze failure examples from past simulation data and perform simulations to avoid the same failures. Furthermore, it can analyze past simulation data and apply the most efficient simulation method. In this way, an optimal simulation is provided by optimizing the current simulation by referring to past simulation data. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input past simulation data into a generating AI and have the generating AI perform the optimization of the current simulation.

[0107] The simulation unit can improve the accuracy of simulations by considering the interrelationships of components. For example, it can perform simulations that optimize strength and durability by considering the interrelationships of components. It can also perform simulations that optimize cost and quality by considering the interrelationships of components. Furthermore, it can perform simulations that optimize delivery time and efficiency by considering the interrelationships of components. In this way, by improving the accuracy of simulations by considering the interrelationships of components, an optimal simulation is provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input component interrelationship data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0108] The simulation unit can estimate the user's emotions and adjust the display order of the simulation results based on the estimated emotions. For example, if the user is stressed, important results can be displayed first. If the user is relaxed, detailed results can be displayed sequentially. Furthermore, if the user is in a hurry, key results can be displayed first. By adjusting the display order of the simulation results based on the user's emotions, an optimal display order tailored to the user's situation is provided. 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 simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the simulation results.

[0109] The database integration unit can estimate the user's emotions and determine the priority of information to retrieve from the database based on the estimated emotions. For example, if the user is stressed, important information can be prioritized. If the user is relaxed, detailed information can be retrieved sequentially. Furthermore, if the user is in a hurry, concise information can be prioritized. In this way, by determining the priority of information to retrieve from the database based on the user's emotions, optimal information tailored to the user's situation is provided. 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 database integration unit may be performed using AI, or not using AI. For example, the database integration unit can input user emotion data into the generative AI and have the generative AI determine the priority of information.

[0110] The database integration unit can select the optimal information retrieval method by referring to past database usage history during database integration. For example, it can prioritize the retrieval of information that the user has frequently used in the past. It can also retrieve information that was needed in similar projects based on the user's past usage history. Furthermore, it can analyze the user's past usage history and select the most efficient information retrieval method. This enables efficient information retrieval by selecting the optimal information retrieval method by referring to past database usage history. Some or all of the above processing in the database integration unit may be performed using AI, for example, or without AI. For example, the database integration unit can input past database usage history data into a generating AI and have the generating AI select the optimal information retrieval method.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The component selection unit selects the optimal components based on the budget provided by the user. The component selection unit selects components by considering a balanced approach to material properties, processability, durability, cost, quality, and supply source. For example, in a building project, high-strength materials can be selected for parts requiring strength, while low-cost materials can be selected for other parts to reduce costs. AI can also be used to select components by considering these factors in a balanced way. Step 2: The advice department provides specific advice, taking into account the strength and delivery time of the components selected by the component selection department. For example, if the strength of the selected components is insufficient, they will propose reinforcement methods to ensure sufficient strength. If the delivery time is long, they will propose alternative components to shorten the delivery period. Step 3: The simulation unit displays simulation results in real time, based on the advice provided by the advice unit, in response to design changes and budget adjustments. For example, if a user makes a design change, the simulation immediately performs a simulation corresponding to that change and displays the results. If a budget adjustment is necessary, the unit re-selects the optimal components within the budget and displays the results. Step 4: The database integration unit supports decision-making based on the simulation results displayed by the simulation unit, taking into account the latest material prices and delivery times. For example, it retrieves the latest material price and delivery time information from the economics database and selects components based on that information.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the component selection unit, advice unit, simulation unit, and database linkage unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the component selection unit is implemented by the control unit 46A of the smart device 14 and selects the optimal component based on the budget provided by the user. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific advice considering the strength and delivery time of the selected component. The simulation unit is implemented by the control unit 46A of the smart device 14 and displays the simulation results in real time in response to design changes and budget adjustments. The database linkage unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports decision-making considering the latest material prices and delivery periods. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the component selection unit, advice unit, simulation unit, and database linkage unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the component selection unit is implemented by the control unit 46A of the smart glasses 214 and selects the optimal component based on the budget provided by the user. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific advice considering the strength and delivery time of the selected component. The simulation unit is implemented by the control unit 46A of the smart glasses 214 and displays the simulation results in real time in response to design changes and budget adjustments. The database linkage unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports decision-making considering the latest material prices and delivery periods. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the component selection unit, advice unit, simulation unit, and database linkage unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the component selection unit is implemented by the control unit 46A of the headset terminal 314 and selects the optimal component based on the budget provided by the user. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific advice considering the strength and delivery time of the selected component. The simulation unit is implemented by the control unit 46A of the headset terminal 314 and displays the simulation results in real time in response to design changes and budget adjustments. The database linkage unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports decision-making considering the latest material prices and delivery periods. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

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

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

[0160] In 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.

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

[0162] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0164] The data processing system 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.

[0165] Each of the multiple elements described above, including the component selection unit, advice unit, simulation unit, and database linkage unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the component selection unit is implemented by the control unit 46A of the robot 414 and selects the optimal components based on the budget provided by the user. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific advice considering the strength and delivery time of the selected components. The simulation unit is implemented by the control unit 46A of the robot 414 and displays the simulation results in real time in response to design changes and budget adjustments. The database linkage unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports decision-making that takes into account the latest material prices and delivery periods. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A component selection unit that selects the most suitable components based on the budget provided by the user, An advice unit provides specific advice considering the strength and delivery time of the components selected by the component selection unit, A simulation unit that displays simulation results in real time in response to design changes and budget adjustments based on the advice provided by the aforementioned advisory unit, The system includes a database linkage unit that supports decision-making based on the simulation results displayed by the aforementioned simulation unit, taking into account the latest material prices and delivery periods. A system characterized by the following features. (Note 2) The aforementioned member selection unit is Components are selected by carefully considering a balanced approach to material properties, processability, durability, cost, quality, and supply source. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice section, If the selected components lack sufficient strength, we will propose reinforcement methods to ensure adequate strength. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, If the delivery time for the selected components is long, we will propose alternative components to shorten the delivery period. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, The system performs simulations based on design changes and displays the results in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned simulation unit, If budget adjustments are necessary, the optimal components will be re-selected within the budget, and the results will be displayed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned database linkage unit is The system retrieves the latest material prices and delivery time information from an economics database and selects components based on that information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned member selection unit is The system estimates the user's emotions and adjusts the priority of component selection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned member selection unit is When selecting components, we analyze the user's past project history to determine the optimal component selection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned member selection unit is When selecting components, the selection process is based on the user's current project progress. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned member selection unit is The system estimates the user's emotions and adjusts the timing of component selection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned member selection unit is When selecting components, the system prioritizes selecting components that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned member selection unit is When selecting components, we analyze the user's social media activity and select relevant components. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the components. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of the component. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, we prioritize the advice based on the delivery dates of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, adjust the order of advice based on the relationships between the components. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During the simulation, past simulation data is referenced to optimize the current simulation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, During simulation, consider the interrelationships between components to improve the accuracy of the simulation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, It estimates the user's emotions and adjusts the display order of the simulation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, During the simulation, the geographical distribution of the components is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, During simulations, we improve the accuracy of the simulations by referring to relevant literature on the components. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned database linkage unit is It estimates the user's emotions and determines the priority of information to retrieve from the database based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned database linkage unit is When integrating with a database, the system selects the optimal method for retrieving information by referring to past database usage history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned database linkage unit is When integrating with the database, the latest information on components is obtained in real time and reflected in the selection process. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned database linkage unit is It estimates the user's emotions and adjusts the timing of information retrieved from the database based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned database linkage unit is When integrating with the database, information is retrieved while considering the geographical distribution of the components. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned database linkage unit is When integrating with databases, we improve the accuracy of information by referring to related literature for the components. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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. A component selection unit that selects the most suitable components based on the budget provided by the user, An advice unit provides specific advice considering the strength and delivery time of the components selected by the component selection unit, A simulation unit that displays simulation results in real time in response to design changes and budget adjustments based on the advice provided by the aforementioned advisory unit, The system includes a database linkage unit that supports decision-making based on the simulation results displayed by the aforementioned simulation unit, taking into account the latest material prices and delivery periods. A system characterized by the following features.

2. The aforementioned member selection unit is Components are selected by carefully considering a balanced approach to material properties, processability, durability, cost, quality, and supply source. The system according to feature 1.

3. The aforementioned advice section, If the selected components lack sufficient strength, we will propose reinforcement methods to ensure adequate strength. The system according to feature 1.

4. The aforementioned advice section, If the delivery time for the selected components is long, we will propose alternative components to shorten the delivery period. The system according to feature 1.

5. The aforementioned simulation unit, The system performs simulations based on design changes and displays the results in real time. The system according to feature 1.

6. The aforementioned simulation unit, If budget adjustments are necessary, the optimal components will be re-selected within the budget, and the results will be displayed. The system according to feature 1.

7. The aforementioned database linkage unit is The system retrieves the latest material prices and delivery time information from an economics database and selects components based on that information. The system according to feature 1.

8. The aforementioned member selection unit is The system estimates the user's emotions and adjusts the priority of component selection based on those estimated emotions. The system according to feature 1.

9. The aforementioned member selection unit is When selecting components, we analyze the user's past project history to determine the optimal component selection method. The system according to feature 1.

10. The aforementioned member selection unit is When selecting components, the selection process is based on the user's current project progress. The system according to feature 1.

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