system

Abstract

The system according to this embodiment aims to provide specific knowledge and know-how regarding the installation of mobile phone base stations in condominium buildings and to generate the best possible solutions. [Solution] The system according to the embodiment comprises a generation unit, a provision unit, and a posting unit. The generation unit learns from past project data and generates the best solution for a specific problem. The provision unit provides the solution generated by the generation unit to the construction company. The posting unit posts the knowledge acquired by engineers and construction staff participating in the platform, contributing to an integrated knowledge database.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a construction company lacking specific knowledge and know - how regarding the installation of mobile phone base stations in condominiums to find an appropriate solution.

[0005] The system according to the embodiment aims to provide specific knowledge and know - how regarding the installation of mobile phone base stations in condominiums and generate the best solution.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a provision unit, and a posting unit. The generation unit learns from past project data and generates the best solution for a specific problem. The provision unit provides the solution generated by the generation unit to the construction company. The posting unit posts the knowledge gained by engineers and construction staff participating in the platform, contributing to an integrated knowledge database. [Effects of the Invention]

[0007] The system according to this embodiment provides specific knowledge and know-how regarding the installation of mobile phone base stations in condominium buildings, and can generate the best possible solution. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The knowledge provision system according to an embodiment of the present invention is a system that provides knowledge regarding the installation of mobile phone base stations in condominium buildings using a generating AI. This knowledge provision system learns from past project data and generates the best solutions to specific problems. Next, it provides the generated solutions to construction companies and also provides hints for specific scenarios such as negotiations for the installation of mobile phone base stations in condominium buildings. Furthermore, it provides a mechanism that allows engineers and construction staff participating in the platform to post the knowledge they have gained and contribute to an integrated knowledge database. For example, the generating AI learns from past project data. In this process, it collects data including specific problems and solutions regarding the installation of mobile phone base stations in condominium buildings and analyzes it. For example, by learning from problems that occurred in past projects, their solutions, and successful negotiation cases, the generating AI can generate the best solutions to specific problems. Next, it provides the generated solutions to construction companies. For example, in negotiations for the installation of mobile phone base stations in condominium buildings, the generating AI provides specific negotiation methods and hints based on past successful cases. This allows construction companies to proceed with negotiations efficiently. Furthermore, it provides a mechanism that allows engineers and construction staff participating in the platform to post the knowledge they have gained and contribute to an integrated knowledge database. For example, by posting new insights and success stories gained at construction sites to the platform, users can share them with other users and enrich the overall knowledge base. This system centralizes knowledge regarding the installation of mobile phone base stations in condominiums, enabling construction companies to perform their work more efficiently. Furthermore, by utilizing generative AI, the system can provide the best solutions learned from past projects, enabling rapid responses to specific challenges. Adding a crowdsourcing element allows users to directly contribute to the knowledge base, making knowledge sharing more comprehensive and useful. As a result, the knowledge provision system can efficiently provide knowledge regarding the installation of mobile phone base stations in condominiums and support the work of construction companies.

[0029] The knowledge provision system according to this embodiment comprises a generation unit, a provision unit, and a posting unit. The generation unit learns from past project data and generates the best solution to a specific problem. For example, the generation unit collects and analyzes past project data. By learning from problems that occurred in past projects, their solutions, and successful negotiation cases, the generation unit can generate the best solution to a specific problem. The generation unit uses a generation AI to analyze past project data and generate the best solution. The provision unit provides the solution generated by the generation unit to the construction company. For example, the provision unit provides the generated solution to the construction company via email or a dedicated application. The provision unit supports the construction company in efficiently conducting negotiations based on the solution provided by the generation AI. The provision unit uses the generation AI to provide the construction company with the optimal solution. The posting unit posts the knowledge gained by engineers and construction staff participating in the platform, contributing to the integrated knowledge database. For example, the posting unit can share new insights and success stories gained at construction sites with other users by posting them to the platform, thereby enriching the overall knowledge base. The posting section uses a generation AI to analyze the submitted knowledge and integrate it into a knowledge database. This allows the knowledge provision system according to this embodiment to efficiently provide knowledge regarding the installation of mobile phone base stations in condominium buildings, thereby supporting the work of construction companies.

[0030] The generation unit learns from past project data and generates the best solutions to specific challenges. For example, the generation unit collects and analyzes past project data. Specifically, it collects information from a database of past projects, such as project progress, problems encountered, solutions, and successful negotiation examples. Since this data exists in various formats, including text data, image data, and audio data, the generation unit analyzes this data comprehensively. The generation unit uses generational AI to analyze past project data and generate the best solutions. The generational AI analyzes text data using natural language processing technology, analyzes image data using image recognition technology, and analyzes audio data using speech recognition technology. As a result, the generation unit learns from problems that occurred in past projects, their solutions, and successful negotiation examples, enabling it to generate the best solutions to specific challenges. For example, the generation unit identifies problems that occurred in past projects and generates solutions to those problems. It also generates optimal negotiation strategies based on successful negotiation examples from past projects. As a result, the generation unit can quickly and accurately generate the best solutions to specific challenges. Furthermore, the generation unit can continuously evaluate and improve the generated solutions, thereby always providing the latest and best solutions.

[0031] The service provider provides the solutions generated by the generation unit to the construction company. For example, the service provider provides the generated solutions to the construction company via email or a dedicated application. Specifically, the service provider provides a means to quickly communicate the solutions received from the generation unit to the construction company's representative. When using email, the service provider creates a detailed report including the generated solutions and sends it to the construction company's representative. When using a dedicated application, the service provider makes the generated solutions viewable within the application, allowing the construction company's representative to quickly obtain the necessary information. The service provider also assists the construction company in efficiently conducting negotiations based on the solutions provided by the generation AI. Specifically, the service provider not only provides the generated solutions to the construction company's representative but also provides detailed guidelines including the necessary steps and precautions for implementing the solutions. This enables the construction company's representative to efficiently conduct negotiations based on the generated solutions. Furthermore, the service provider collects feedback from the construction company's representative and provides this feedback to the generation unit, continuously improving the accuracy and effectiveness of the generated solutions. This allows the service provider to provide the construction company with the optimal solution and efficiently support their operations.

[0032] The posting section allows engineers and construction staff participating in the platform to post the knowledge they have gained, contributing to a unified knowledge database. For example, by posting new insights and success stories gained at construction sites, the posting section can share them with other users and enrich the overall knowledge base. Specifically, the posting section provides an interface that allows engineers and construction staff to easily post insights and success stories gained on-site. Posted knowledge is saved in various formats such as text, images, and videos, making it easily accessible to other users. The posting section uses generative AI to analyze the posted knowledge and integrate it into the knowledge database. The generative AI analyzes the posted text data using natural language processing technology to extract important information. It also analyzes image and video data to extract relevant information. This allows the posting section to efficiently analyze the posted knowledge and integrate it into the knowledge database. Furthermore, the posting section has a function to evaluate the quality of posted knowledge and prioritize the display of reliable information. This allows users to quickly obtain reliable information. The posting section can collect feedback from users and use it to improve the posting interface and analysis algorithms. This allows the contributing section to consistently provide the latest and most reliable knowledge, enriching the overall knowledge base.

[0033] The generation unit can collect and analyze past project data. For example, the generation unit can extract and analyze past project data from a database. The generation unit can also manually input past project data. The generation unit analyzes past project data using statistical analysis and machine learning algorithms. For example, the generation unit can generate the best solution to a specific problem by collecting past project data and performing statistical analysis. The generation unit can also analyze past project data using machine learning algorithms and generate the best solution. This allows the generation unit to generate more accurate solutions by collecting and analyzing past project data. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past project data into a generation AI and have the generation AI perform the analysis.

[0034] The service provider can provide the generated solutions to the construction company. For example, the service provider can provide the generated solutions to the construction company via email. The service provider can also provide the generated solutions to the construction company through a dedicated application. The service provider assists the construction company in efficiently conducting negotiations based on the solutions provided by the generation AI. For example, the service provider can provide the construction company with specific negotiation methods and hints based on the solutions provided by the generation AI. The service provider can also use the generation AI to provide the construction company with the optimal solution. In this way, by providing the generated solutions to the construction company, the service provider can enable the construction company to proceed with its work efficiently. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide the construction company with specific negotiation methods and hints based on the solutions provided by the generation AI.

[0035] The posting section allows engineers and construction staff to post knowledge they have gained and integrate it into the knowledge database. For example, the posting section can post new insights and success stories gained at construction sites to the platform. The posting section can also analyze the posted knowledge and integrate it into the knowledge database. The posting section uses generative AI to analyze the posted knowledge and integrate it into the knowledge database. For example, the posting section enriches the overall knowledge base by having generative AI analyze the posted knowledge and integrate it into the knowledge database. This promotes knowledge sharing by allowing engineers and construction staff to post knowledge they have gained and integrate it into the knowledge database. Some or all of the above processes in the posting section may be performed using AI, for example, or without AI. For example, the posting section can have generative AI analyze the posted knowledge and integrate it into the knowledge database.

[0036] The generation unit can provide specific negotiation methods and hints for negotiating the installation of mobile phone base stations in condominium buildings. For example, the generation unit can provide specific negotiation methods and hints based on past successful cases. The generation unit uses generation AI to provide specific negotiation methods and hints for negotiating the installation of mobile phone base stations in condominium buildings. For example, the generation unit enables construction companies to proceed with negotiations efficiently by having the generation AI provide specific negotiation methods and hints based on past successful cases. As a result, the generation unit improves the success rate of negotiations by providing specific negotiation methods and hints. Some or all of the above processing in the generation unit may be performed using generation AI, for example, or without generation AI. For example, the generation unit can have the generation AI provide specific negotiation methods and hints based on past successful cases.

[0037] The service provider can support construction companies in efficiently conducting negotiations based on solutions provided by the generation AI. For example, the service provider can provide construction companies with specific negotiation methods and hints based on solutions provided by the generation AI. The service provider can also use the generation AI to provide construction companies with the optimal solution. The service provider supports construction companies in efficiently conducting negotiations based on solutions provided by the generation AI. For example, by providing construction companies with specific negotiation methods and hints based on solutions provided by the generation AI, the service provider can enable construction companies to conduct negotiations efficiently. In this way, the service provider improves the success rate of negotiations by supporting construction companies in conducting negotiations efficiently. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide construction companies with specific negotiation methods and hints based on solutions provided by the generation AI.

[0038] The generation unit can prioritize the analysis of the most relevant data from past project data based on specific conditions. For example, the generation unit can prioritize the analysis of past success stories of a construction company. The generation unit can also prioritize the analysis of data related to a specific area of ​​condominium development. The generation unit can also prioritize the analysis of past project data of a specific construction company. In this way, the generation unit can generate more accurate solutions by prioritizing the analysis of relevant data based on specific conditions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input relevant data based on specific conditions into a generation AI and have the generation AI perform the analysis.

[0039] The generation unit can generate more realistic solutions by including environmental and weather data of the construction site in the data to be analyzed. For example, the generation unit can include noise level and vibration data of the construction site in the analysis. The generation unit can also propose construction methods for rainy weather based on weather data. The generation unit can also include geological data of the construction site in the analysis and propose the optimal construction method. In this way, the generation unit can generate more realistic solutions by including environmental and weather data of the construction site. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input environmental and weather data of the construction site into a generation AI and have the generation AI perform the analysis.

[0040] The generation unit can include installation examples of other condominiums and region-specific regulatory information in the data it analyzes. For example, the generation unit can include successful examples from other condominiums in its analysis. The generation unit can also include region-specific building regulations in its analysis. The generation unit can also include failure examples from other condominiums in its analysis. As a result, the generation unit can generate more appropriate solutions by including installation examples from other condominiums and region-specific regulatory information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input installation examples from other condominiums and region-specific regulatory information into a generation AI and have the generation AI perform the analysis.

[0041] The generation unit can generate solutions optimized for each company by including the construction company's past performance data in the data to be analyzed. For example, the generation unit can include the construction company's past project success rate in its analysis. The generation unit can also include the construction company's past project completion time in its analysis. The generation unit can also include the construction company's past project cost data in its analysis. This allows the generation unit to generate solutions optimized for each company by including the construction company's past performance data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input the construction company's past performance data into a generation AI and have the generation AI perform the analysis.

[0042] The service provider can provide solutions at the optimal time, taking into account the construction company's current project progress. For example, if the construction company is in the early stages of the project, the service provider can provide basic solutions. If the construction company is in the middle stages of the project, the service provider can also provide detailed solutions. If the construction company is in the final stages of the project, the service provider can also provide solutions for final adjustments. This allows the service provider to provide more effective support by providing solutions while considering the construction company's current project progress. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input project progress data of the construction company into a generating AI and have the generating AI provide solutions at the optimal time.

[0043] The service provider can customize the content of its solutions by reflecting past feedback from the construction company. For example, the service provider can reflect feedback that the construction company has given to solutions previously provided. The service provider can also evaluate the effectiveness of solutions previously provided by the construction company and reflect areas for improvement. The service provider can also provide the optimal solution based on the results of implementing solutions previously provided by the construction company. In this way, the service provider can provide more appropriate solutions by reflecting past feedback from the construction company. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past feedback data from the construction company into a generating AI and have the generating AI perform the customization of the content of its solutions.

[0044] The service provider can provide region-specific solutions by considering the geographical location information of the construction company when providing solutions. For example, if the construction company is located in a specific region, the service provider can provide solutions that include building regulations for that region. If the construction company is located in a specific region, the service provider can also provide solutions that consider the climate conditions of that region. If the construction company is located in a specific region, the service provider can also provide solutions that consider the geological data of that region. In this way, the service provider can provide region-specific solutions by considering the geographical location information of the construction company. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the geographical location information of the construction company into a generating AI and have the generating AI perform the task of providing region-specific solutions.

[0045] The service provider can provide feasible solutions by considering the resource situation of the construction company when providing solutions. For example, the service provider can provide solutions considering the current personnel situation of the construction company. The service provider can also provide solutions considering the current budget situation of the construction company. The service provider can also provide solutions considering the current equipment situation of the construction company. In this way, the service provider can provide feasible solutions by considering the resource situation of the construction company. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input resource situation data of the construction company into a generating AI and have the generating AI perform the task of providing feasible solutions.

[0046] The posting function can suggest the optimal posting format when a user submits knowledge by referring to their past posting history. For example, the posting function may prioritize suggesting formats previously used by the user. The posting function can also analyze the user's past postings and suggest the optimal format. The posting function can also suggest the most effective format based on the user's past posting history. In this way, the posting function can suggest the optimal posting format by referring to the user's past posting history. Some or all of the above processes in the posting function may be performed using AI, for example, or not. For example, the posting function can input the user's past posting history data into a generating AI and have the generating AI suggest the optimal posting format.

[0047] The posting section can evaluate the reliability of submitted knowledge and prioritize the display of highly reliable submissions. For example, the posting section may refer to past success stories to evaluate the reliability of submitted content. The posting section may also refer to the past performance of the submitter to evaluate the reliability of submitted content. The posting section may also refer to feedback from other users to evaluate the reliability of submitted content. In this way, the posting section can prioritize the display of highly reliable submissions by evaluating the reliability of submitted content. Some or all of the above processing in the posting section may be performed using AI, for example, or not using AI. For example, the posting section may input data for evaluating the reliability of submitted content into a generating AI and have the generating AI perform the reliability evaluation.

[0048] The posting function can prioritize displaying region-specific knowledge by considering the poster's geographical location when a user posts knowledge. For example, if the poster is in a specific region, the posting function can prioritize displaying knowledge that includes building regulations for that region. The posting function can also prioritize displaying knowledge that considers the climate conditions of a specific region if the poster is in that region. The posting function can also prioritize displaying knowledge that considers the geological data of a specific region if the poster is in that region. In this way, the posting function can prioritize displaying region-specific knowledge by considering the poster's geographical location. Some or all of the above processing in the posting function may be performed using AI, for example, or without AI. For example, the posting function can input the poster's geographical location information into a generating AI and have the generating AI perform the display of region-specific knowledge.

[0049] The posting section can evaluate the relevance of posts when submitting knowledge and prioritize displaying posts with high relevance. For example, the posting section may refer to past success stories to evaluate the relevance of posts. The posting section may also refer to the poster's past performance to evaluate the relevance of posts. The posting section may also refer to feedback from other users to evaluate the relevance of posts. In this way, the posting section can prioritize displaying posts with high relevance by evaluating the relevance of posts. Some or all of the above processing in the posting section may be performed using AI, for example, or not using AI. For example, the posting section may input data for evaluating the relevance of posts into a generating AI and have the generating AI perform the relevance evaluation.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The service provider can provide solutions at the optimal time, taking into account the construction company's current project progress. For example, if the construction company is in the early stages of the project, the service provider can provide basic solutions. If the construction company is in the middle stages of the project, the service provider can also provide detailed solutions. If the construction company is in the final stages of the project, the service provider can also provide solutions for final adjustments. This allows the service provider to provide more effective support by providing solutions while considering the construction company's current project progress. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input project progress data of the construction company into a generating AI and have the generating AI provide solutions at the optimal time.

[0052] The generation unit can generate more realistic solutions by including environmental and weather data of the construction site in the data to be analyzed. For example, the generation unit can include noise level and vibration data of the construction site in the analysis. The generation unit can also propose construction methods for rainy weather based on weather data. The generation unit can also include geological data of the construction site in the analysis and propose the optimal construction method. In this way, the generation unit can generate more realistic solutions by including environmental and weather data of the construction site. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input environmental and weather data of the construction site into a generation AI and have the generation AI perform the analysis.

[0053] The posting function can suggest the optimal posting format when a user submits knowledge by referring to their past posting history. For example, the posting function may prioritize suggesting formats previously used by the user. The posting function can also analyze the user's past postings and suggest the optimal format. The posting function can also suggest the most effective format based on the user's past posting history. In this way, the posting function can suggest the optimal posting format by referring to the user's past posting history. Some or all of the above processes in the posting function may be performed using AI, for example, or not. For example, the posting function can input the user's past posting history data into a generating AI and have the generating AI suggest the optimal posting format.

[0054] The service provider can customize the content of its solutions by reflecting past feedback from the construction company. For example, the service provider can reflect feedback that the construction company has given to solutions previously provided. The service provider can also evaluate the effectiveness of solutions previously provided by the construction company and reflect areas for improvement. The service provider can also provide the optimal solution based on the results of implementing solutions previously provided by the construction company. In this way, the service provider can provide more appropriate solutions by reflecting past feedback from the construction company. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past feedback data from the construction company into a generating AI and have the generating AI perform the customization of the content of its solutions.

[0055] The generation unit can include installation examples of other condominiums and region-specific regulatory information in the data it analyzes. For example, the generation unit can include successful examples from other condominiums in its analysis. The generation unit can also include region-specific building regulations in its analysis. The generation unit can also include failure examples from other condominiums in its analysis. As a result, the generation unit can generate more appropriate solutions by including installation examples from other condominiums and region-specific regulatory information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input installation examples from other condominiums and region-specific regulatory information into a generation AI and have the generation AI perform the analysis.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The generation unit learns from past project data and generates the best solutions to specific problems. The generation unit collects and analyzes past project data. By learning from problems that occurred in past projects, their solutions, and successful negotiation examples, it generates the best solutions to specific problems. Using the generation AI, it analyzes past project data and generates the best solutions. Step 2: The provisioning department provides the solutions generated by the generation department to the construction company. The provisioning department provides the generated solutions to the construction company via email or a dedicated application. It supports the construction company in efficiently conducting negotiations based on the solutions provided by the generation AI. It provides the construction company with the optimal solution using the generation AI. Step 3: The Contributors contribute to the unified knowledge database by posting knowledge gained by engineers and construction staff participating in the platform. The Contributors share new insights and success stories obtained at construction sites with other users by posting them on the platform, enriching the overall knowledge base. Generative AI is used to analyze the posted knowledge and integrate it into the knowledge database.

[0058] (Example of form 2) The knowledge provision system according to an embodiment of the present invention is a system that provides knowledge regarding the installation of mobile phone base stations in condominium buildings using a generating AI. This knowledge provision system learns from past project data and generates the best solutions to specific problems. Next, it provides the generated solutions to construction companies and also provides hints for specific scenarios such as negotiations for the installation of mobile phone base stations in condominium buildings. Furthermore, it provides a mechanism that allows engineers and construction staff participating in the platform to post the knowledge they have gained and contribute to an integrated knowledge database. For example, the generating AI learns from past project data. In this process, it collects data including specific problems and solutions regarding the installation of mobile phone base stations in condominium buildings and analyzes it. For example, by learning from problems that occurred in past projects, their solutions, and successful negotiation cases, the generating AI can generate the best solutions to specific problems. Next, it provides the generated solutions to construction companies. For example, in negotiations for the installation of mobile phone base stations in condominium buildings, the generating AI provides specific negotiation methods and hints based on past successful cases. This allows construction companies to proceed with negotiations efficiently. Furthermore, it provides a mechanism that allows engineers and construction staff participating in the platform to post the knowledge they have gained and contribute to an integrated knowledge database. For example, by posting new insights and success stories gained at construction sites to the platform, users can share them with other users and enrich the overall knowledge base. This system centralizes knowledge regarding the installation of mobile phone base stations in condominiums, enabling construction companies to perform their work more efficiently. Furthermore, by utilizing generative AI, the system can provide the best solutions learned from past projects, enabling rapid responses to specific challenges. Adding a crowdsourcing element allows users to directly contribute to the knowledge base, making knowledge sharing more comprehensive and useful. As a result, the knowledge provision system can efficiently provide knowledge regarding the installation of mobile phone base stations in condominiums and support the work of construction companies.

[0059] The knowledge provision system according to this embodiment comprises a generation unit, a provision unit, and a posting unit. The generation unit learns from past project data and generates the best solution to a specific problem. For example, the generation unit collects and analyzes past project data. By learning from problems that occurred in past projects, their solutions, and successful negotiation cases, the generation unit can generate the best solution to a specific problem. The generation unit uses a generation AI to analyze past project data and generate the best solution. The provision unit provides the solution generated by the generation unit to the construction company. For example, the provision unit provides the generated solution to the construction company via email or a dedicated application. The provision unit supports the construction company in efficiently conducting negotiations based on the solution provided by the generation AI. The provision unit uses the generation AI to provide the construction company with the optimal solution. The posting unit posts the knowledge gained by engineers and construction staff participating in the platform, contributing to the integrated knowledge database. For example, the posting unit can share new insights and success stories gained at construction sites with other users by posting them to the platform, thereby enriching the overall knowledge base. The posting section uses a generation AI to analyze the submitted knowledge and integrate it into a knowledge database. This allows the knowledge provision system according to this embodiment to efficiently provide knowledge regarding the installation of mobile phone base stations in condominium buildings, thereby supporting the work of construction companies.

[0060] The generation unit learns from past project data and generates the best solutions to specific challenges. For example, the generation unit collects and analyzes past project data. Specifically, it collects information from a database of past projects, such as project progress, problems encountered, solutions, and successful negotiation examples. Since this data exists in various formats, including text data, image data, and audio data, the generation unit analyzes this data comprehensively. The generation unit uses generational AI to analyze past project data and generate the best solutions. The generational AI analyzes text data using natural language processing technology, analyzes image data using image recognition technology, and analyzes audio data using speech recognition technology. As a result, the generation unit learns from problems that occurred in past projects, their solutions, and successful negotiation examples, enabling it to generate the best solutions to specific challenges. For example, the generation unit identifies problems that occurred in past projects and generates solutions to those problems. It also generates optimal negotiation strategies based on successful negotiation examples from past projects. As a result, the generation unit can quickly and accurately generate the best solutions to specific challenges. Furthermore, the generation unit can continuously evaluate and improve the generated solutions, thereby always providing the latest and best solutions.

[0061] The service provider provides the solutions generated by the generation unit to the construction company. For example, the service provider provides the generated solutions to the construction company via email or a dedicated application. Specifically, the service provider provides a means to quickly communicate the solutions received from the generation unit to the construction company's representative. When using email, the service provider creates a detailed report including the generated solutions and sends it to the construction company's representative. When using a dedicated application, the service provider makes the generated solutions viewable within the application, allowing the construction company's representative to quickly obtain the necessary information. The service provider also assists the construction company in efficiently conducting negotiations based on the solutions provided by the generation AI. Specifically, the service provider not only provides the generated solutions to the construction company's representative but also provides detailed guidelines including the necessary steps and precautions for implementing the solutions. This enables the construction company's representative to efficiently conduct negotiations based on the generated solutions. Furthermore, the service provider collects feedback from the construction company's representative and provides this feedback to the generation unit, continuously improving the accuracy and effectiveness of the generated solutions. This allows the service provider to provide the construction company with the optimal solution and efficiently support their operations.

[0062] The posting section allows engineers and construction staff participating in the platform to post the knowledge they have gained, contributing to a unified knowledge database. For example, by posting new insights and success stories gained at construction sites, the posting section can share them with other users and enrich the overall knowledge base. Specifically, the posting section provides an interface that allows engineers and construction staff to easily post insights and success stories gained on-site. Posted knowledge is saved in various formats such as text, images, and videos, making it easily accessible to other users. The posting section uses generative AI to analyze the posted knowledge and integrate it into the knowledge database. The generative AI analyzes the posted text data using natural language processing technology to extract important information. It also analyzes image and video data to extract relevant information. This allows the posting section to efficiently analyze the posted knowledge and integrate it into the knowledge database. Furthermore, the posting section has a function to evaluate the quality of posted knowledge and prioritize the display of reliable information. This allows users to quickly obtain reliable information. The posting section can collect feedback from users and use it to improve the posting interface and analysis algorithms. This allows the contributing section to consistently provide the latest and most reliable knowledge, enriching the overall knowledge base.

[0063] The generation unit can collect and analyze past project data. For example, the generation unit can extract and analyze past project data from a database. The generation unit can also manually input past project data. The generation unit analyzes past project data using statistical analysis and machine learning algorithms. For example, the generation unit can generate the best solution to a specific problem by collecting past project data and performing statistical analysis. The generation unit can also analyze past project data using machine learning algorithms and generate the best solution. This allows the generation unit to generate more accurate solutions by collecting and analyzing past project data. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past project data into a generation AI and have the generation AI perform the analysis.

[0064] The service provider can provide the generated solutions to the construction company. For example, the service provider can provide the generated solutions to the construction company via email. The service provider can also provide the generated solutions to the construction company through a dedicated application. The service provider assists the construction company in efficiently conducting negotiations based on the solutions provided by the generation AI. For example, the service provider can provide the construction company with specific negotiation methods and hints based on the solutions provided by the generation AI. The service provider can also use the generation AI to provide the construction company with the optimal solution. In this way, by providing the generated solutions to the construction company, the service provider can enable the construction company to proceed with its work efficiently. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide the construction company with specific negotiation methods and hints based on the solutions provided by the generation AI.

[0065] The posting section allows engineers and construction staff to post knowledge they have gained and integrate it into the knowledge database. For example, the posting section can post new insights and success stories gained at construction sites to the platform. The posting section can also analyze the posted knowledge and integrate it into the knowledge database. The posting section uses generative AI to analyze the posted knowledge and integrate it into the knowledge database. For example, the posting section enriches the overall knowledge base by having generative AI analyze the posted knowledge and integrate it into the knowledge database. This promotes knowledge sharing by allowing engineers and construction staff to post knowledge they have gained and integrate it into the knowledge database. Some or all of the above processes in the posting section may be performed using AI, for example, or without AI. For example, the posting section can have generative AI analyze the posted knowledge and integrate it into the knowledge database.

[0066] The generation unit can provide specific negotiation methods and hints for negotiating the installation of mobile phone base stations in condominium buildings. For example, the generation unit can provide specific negotiation methods and hints based on past successful cases. The generation unit uses generation AI to provide specific negotiation methods and hints for negotiating the installation of mobile phone base stations in condominium buildings. For example, the generation unit enables construction companies to proceed with negotiations efficiently by having the generation AI provide specific negotiation methods and hints based on past successful cases. As a result, the generation unit improves the success rate of negotiations by providing specific negotiation methods and hints. Some or all of the above processing in the generation unit may be performed using generation AI, for example, or without generation AI. For example, the generation unit can have the generation AI provide specific negotiation methods and hints based on past successful cases.

[0067] The service provider can support construction companies in efficiently conducting negotiations based on solutions provided by the generation AI. For example, the service provider can provide construction companies with specific negotiation methods and hints based on solutions provided by the generation AI. The service provider can also use the generation AI to provide construction companies with the optimal solution. The service provider supports construction companies in efficiently conducting negotiations based on solutions provided by the generation AI. For example, by providing construction companies with specific negotiation methods and hints based on solutions provided by the generation AI, the service provider can enable construction companies to conduct negotiations efficiently. In this way, the service provider improves the success rate of negotiations by supporting construction companies in conducting negotiations efficiently. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide construction companies with specific negotiation methods and hints based on solutions provided by the generation AI.

[0068] The generation unit can estimate the user's emotions and adjust the way the generated solutions are presented based on the estimated emotions. For example, if the user is stressed, the generation unit will present the solution in a simple and intuitive way. If the user is relaxed, the generation unit may also provide a solution with detailed explanations. If the user is in a hurry, the generation unit may also provide a concise solution that gets straight to the point. In this way, the generation unit can provide solutions that are easy for the user to understand by adjusting the way the solutions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the way the solutions are presented based on the emotions.

[0069] The generation unit can prioritize the analysis of the most relevant data from past project data based on specific conditions. For example, the generation unit can prioritize the analysis of past success stories of a construction company. The generation unit can also prioritize the analysis of data related to a specific area of ​​condominium development. The generation unit can also prioritize the analysis of past project data of a specific construction company. In this way, the generation unit can generate more accurate solutions by prioritizing the analysis of relevant data based on specific conditions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input relevant data based on specific conditions into a generation AI and have the generation AI perform the analysis.

[0070] The generation unit can generate more realistic solutions by including environmental and weather data of the construction site in the data to be analyzed. For example, the generation unit can include noise level and vibration data of the construction site in the analysis. The generation unit can also propose construction methods for rainy weather based on weather data. The generation unit can also include geological data of the construction site in the analysis and propose the optimal construction method. In this way, the generation unit can generate more realistic solutions by including environmental and weather data of the construction site. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input environmental and weather data of the construction site into a generation AI and have the generation AI perform the analysis.

[0071] The generation unit can estimate the user's emotions and determine the priority of solutions to generate based on the estimated user emotions. For example, if the user is stressed, the generation unit will prioritize the simplest and quickest solution. If the user is relaxed, the generation unit may also prioritize a more detailed solution. If the user is in a hurry, the generation unit may also prioritize the quickest and most actionable solution. In this way, the generation unit can provide the user with the best possible solution by prioritizing solutions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of solutions based on emotions.

[0072] The generation unit can include installation examples of other condominiums and region-specific regulatory information in the data it analyzes. For example, the generation unit can include successful examples from other condominiums in its analysis. The generation unit can also include region-specific building regulations in its analysis. The generation unit can also include failure examples from other condominiums in its analysis. As a result, the generation unit can generate more appropriate solutions by including installation examples from other condominiums and region-specific regulatory information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input installation examples from other condominiums and region-specific regulatory information into a generation AI and have the generation AI perform the analysis.

[0073] The generation unit can generate solutions optimized for each company by including the construction company's past performance data in the data to be analyzed. For example, the generation unit can include the construction company's past project success rate in its analysis. The generation unit can also include the construction company's past project completion time in its analysis. The generation unit can also include the construction company's past project cost data in its analysis. This allows the generation unit to generate solutions optimized for each company by including the construction company's past performance data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input the construction company's past performance data into a generation AI and have the generation AI perform the analysis.

[0074] The service provider can estimate the user's emotions and adjust the way it presents solutions based on those emotions. For example, if the user is stressed, the service provider can provide a simple and intuitive presentation. If the user is relaxed, the service provider can also provide a presentation that includes detailed explanations. If the user is in a hurry, the service provider can provide a concise and to-the-point presentation. This allows the service provider to provide solutions that are easy for the user to understand by adjusting the presentation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the presentation method of solutions based on emotions.

[0075] The service provider can provide solutions at the optimal time, taking into account the construction company's current project progress. For example, if the construction company is in the early stages of the project, the service provider can provide basic solutions. If the construction company is in the middle stages of the project, the service provider can also provide detailed solutions. If the construction company is in the final stages of the project, the service provider can also provide solutions for final adjustments. This allows the service provider to provide more effective support by providing solutions while considering the construction company's current project progress. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input project progress data of the construction company into a generating AI and have the generating AI provide solutions at the optimal time.

[0076] The service provider can customize the content of its solutions by reflecting past feedback from the construction company. For example, the service provider can reflect feedback that the construction company has given to solutions previously provided. The service provider can also evaluate the effectiveness of solutions previously provided by the construction company and reflect areas for improvement. The service provider can also provide the optimal solution based on the results of implementing solutions previously provided by the construction company. In this way, the service provider can provide more appropriate solutions by reflecting past feedback from the construction company. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past feedback data from the construction company into a generating AI and have the generating AI perform the customization of the content of its solutions.

[0077] The service provider can estimate the user's emotions and adjust the level of detail of the solutions it provides based on the estimated emotions. For example, if the user is stressed, the service provider can provide a concise and to-the-point solution. If the user is relaxed, the service provider can also provide a solution with detailed explanations. If the user is in a hurry, the service provider can also provide a solution that can be quickly implemented. In this way, the service provider can provide the optimal solution for the user by adjusting the level of detail of the solution based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the level of detail of the solution based on the emotions.

[0078] The service provider can provide region-specific solutions by considering the geographical location information of the construction company when providing solutions. For example, if the construction company is located in a specific region, the service provider can provide solutions that include building regulations for that region. If the construction company is located in a specific region, the service provider can also provide solutions that consider the climate conditions of that region. If the construction company is located in a specific region, the service provider can also provide solutions that consider the geological data of that region. In this way, the service provider can provide region-specific solutions by considering the geographical location information of the construction company. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the geographical location information of the construction company into a generating AI and have the generating AI perform the task of providing region-specific solutions.

[0079] The service provider can provide feasible solutions by considering the resource situation of the construction company when providing solutions. For example, the service provider can provide solutions considering the current personnel situation of the construction company. The service provider can also provide solutions considering the current budget situation of the construction company. The service provider can also provide solutions considering the current equipment situation of the construction company. In this way, the service provider can provide feasible solutions by considering the resource situation of the construction company. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input resource situation data of the construction company into a generating AI and have the generating AI perform the task of providing feasible solutions.

[0080] The posting function can estimate the user's emotions and adjust how the post content is displayed based on those emotions. For example, if the user is stressed, the posting function can provide a simple and highly visible display. If the user is relaxed, the posting function can also provide a display that includes detailed information. If the user is in a hurry, the posting function can provide a concise display. In this way, the posting function can provide a highly visible display for the user by adjusting how the post content is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the posting function may be performed using AI or not using AI. For example, the posting function can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the display.

[0081] The posting function can suggest the optimal posting format when a user submits knowledge by referring to their past posting history. For example, the posting function may prioritize suggesting formats previously used by the user. The posting function can also analyze the user's past postings and suggest the optimal format. The posting function can also suggest the most effective format based on the user's past posting history. In this way, the posting function can suggest the optimal posting format by referring to the user's past posting history. Some or all of the above processes in the posting function may be performed using AI, for example, or not. For example, the posting function can input the user's past posting history data into a generating AI and have the generating AI suggest the optimal posting format.

[0082] The posting section can evaluate the reliability of submitted knowledge and prioritize the display of highly reliable submissions. For example, the posting section may refer to past success stories to evaluate the reliability of submitted content. The posting section may also refer to the past performance of the submitter to evaluate the reliability of submitted content. The posting section may also refer to feedback from other users to evaluate the reliability of submitted content. In this way, the posting section can prioritize the display of highly reliable submissions by evaluating the reliability of submitted content. Some or all of the above processing in the posting section may be performed using AI, for example, or not using AI. For example, the posting section may input data for evaluating the reliability of submitted content into a generating AI and have the generating AI perform the reliability evaluation.

[0083] The posting function can estimate the user's emotions and prioritize the content of posts based on those emotions. For example, if the user is stressed, the posting function will prioritize the simplest and quickest solutions. If the user is relaxed, the posting function may also prioritize detailed solutions. If the user is in a hurry, the posting function may also prioritize the quickest and most actionable solutions. In this way, the posting function can provide the user with the most suitable content by prioritizing posts based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 posting function may be performed using AI or not. For example, the posting function can input user emotion data into a generative AI and have the generative AI perform the emotion-based prioritization of posts.

[0084] The posting function can prioritize displaying region-specific knowledge by considering the poster's geographical location when a user posts knowledge. For example, if the poster is in a specific region, the posting function can prioritize displaying knowledge that includes building regulations for that region. The posting function can also prioritize displaying knowledge that considers the climate conditions of a specific region if the poster is in that region. The posting function can also prioritize displaying knowledge that considers the geological data of a specific region if the poster is in that region. In this way, the posting function can prioritize displaying region-specific knowledge by considering the poster's geographical location. Some or all of the above processing in the posting function may be performed using AI, for example, or without AI. For example, the posting function can input the poster's geographical location information into a generating AI and have the generating AI perform the display of region-specific knowledge.

[0085] The posting section can evaluate the relevance of posts when submitting knowledge and prioritize displaying posts with high relevance. For example, the posting section may refer to past success stories to evaluate the relevance of posts. The posting section may also refer to the poster's past performance to evaluate the relevance of posts. The posting section may also refer to feedback from other users to evaluate the relevance of posts. In this way, the posting section can prioritize displaying posts with high relevance by evaluating the relevance of posts. Some or all of the above processing in the posting section may be performed using AI, for example, or not using AI. For example, the posting section may input data for evaluating the relevance of posts into a generating AI and have the generating AI perform the relevance evaluation.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The generation unit can estimate the user's emotions and adjust the way the generated solutions are presented based on the estimated emotions. For example, if the user is stressed, the generation unit will present the solution in a simple and intuitive way. If the user is relaxed, the generation unit may also provide a solution with detailed explanations. If the user is in a hurry, the generation unit may also provide a concise solution that gets straight to the point. In this way, the generation unit can provide solutions that are easy for the user to understand by adjusting the way the solutions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the way the solutions are presented based on the emotions.

[0088] The service provider can provide solutions at the optimal time, taking into account the construction company's current project progress. For example, if the construction company is in the early stages of the project, the service provider can provide basic solutions. If the construction company is in the middle stages of the project, the service provider can also provide detailed solutions. If the construction company is in the final stages of the project, the service provider can also provide solutions for final adjustments. This allows the service provider to provide more effective support by providing solutions while considering the construction company's current project progress. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input project progress data of the construction company into a generating AI and have the generating AI provide solutions at the optimal time.

[0089] The generation unit can generate more realistic solutions by including environmental and weather data of the construction site in the data to be analyzed. For example, the generation unit can include noise level and vibration data of the construction site in the analysis. The generation unit can also propose construction methods for rainy weather based on weather data. The generation unit can also include geological data of the construction site in the analysis and propose the optimal construction method. In this way, the generation unit can generate more realistic solutions by including environmental and weather data of the construction site. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input environmental and weather data of the construction site into a generation AI and have the generation AI perform the analysis.

[0090] The service provider can estimate the user's emotions and adjust the way it presents solutions based on those emotions. For example, if the user is stressed, the service provider can provide a simple and intuitive presentation. If the user is relaxed, the service provider can also provide a presentation that includes detailed explanations. If the user is in a hurry, the service provider can provide a concise and to-the-point presentation. This allows the service provider to provide solutions that are easy for the user to understand by adjusting the presentation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the presentation method of solutions based on emotions.

[0091] The posting function can suggest the optimal posting format when a user submits knowledge by referring to their past posting history. For example, the posting function may prioritize suggesting formats previously used by the user. The posting function can also analyze the user's past postings and suggest the optimal format. The posting function can also suggest the most effective format based on the user's past posting history. In this way, the posting function can suggest the optimal posting format by referring to the user's past posting history. Some or all of the above processes in the posting function may be performed using AI, for example, or not. For example, the posting function can input the user's past posting history data into a generating AI and have the generating AI suggest the optimal posting format.

[0092] The generation unit can estimate the user's emotions and determine the priority of solutions to generate based on the estimated user emotions. For example, if the user is stressed, the generation unit will prioritize the simplest and quickest solution. If the user is relaxed, the generation unit may also prioritize a more detailed solution. If the user is in a hurry, the generation unit may also prioritize the quickest and most actionable solution. In this way, the generation unit can provide the user with the best possible solution by prioritizing solutions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of solutions based on emotions.

[0093] The service provider can customize the content of its solutions by reflecting past feedback from the construction company. For example, the service provider can reflect feedback that the construction company has given to solutions previously provided. The service provider can also evaluate the effectiveness of solutions previously provided by the construction company and reflect areas for improvement. The service provider can also provide the optimal solution based on the results of implementing solutions previously provided by the construction company. In this way, the service provider can provide more appropriate solutions by reflecting past feedback from the construction company. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past feedback data from the construction company into a generating AI and have the generating AI perform the customization of the content of its solutions.

[0094] The posting function can estimate the user's emotions and adjust how the post content is displayed based on those emotions. For example, if the user is stressed, the posting function can provide a simple and highly visible display. If the user is relaxed, the posting function can also provide a display that includes detailed information. If the user is in a hurry, the posting function can provide a concise display. In this way, the posting function can provide a highly visible display for the user by adjusting how the post content is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the posting function may be performed using AI or not using AI. For example, the posting function can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the display.

[0095] The generation unit can include installation examples of other condominiums and region-specific regulatory information in the data it analyzes. For example, the generation unit can include successful examples from other condominiums in its analysis. The generation unit can also include region-specific building regulations in its analysis. The generation unit can also include failure examples from other condominiums in its analysis. As a result, the generation unit can generate more appropriate solutions by including installation examples from other condominiums and region-specific regulatory information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input installation examples from other condominiums and region-specific regulatory information into a generation AI and have the generation AI perform the analysis.

[0096] The service provider can estimate the user's emotions and adjust the level of detail of the solutions it provides based on the estimated emotions. For example, if the user is stressed, the service provider can provide a concise and to-the-point solution. If the user is relaxed, the service provider can also provide a solution with detailed explanations. If the user is in a hurry, the service provider can also provide a solution that can be quickly implemented. In this way, the service provider can provide the optimal solution for the user by adjusting the level of detail of the solution based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the level of detail of the solution based on the emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The generation unit learns from past project data and generates the best solutions to specific problems. The generation unit collects and analyzes past project data. By learning from problems that occurred in past projects, their solutions, and successful negotiation examples, it generates the best solutions to specific problems. Using the generation AI, it analyzes past project data and generates the best solutions. Step 2: The provisioning department provides the solutions generated by the generation department to the construction company. The provisioning department provides the generated solutions to the construction company via email or a dedicated application. It supports the construction company in efficiently conducting negotiations based on the solutions provided by the generation AI. It provides the construction company with the optimal solution using the generation AI. Step 3: The Contributors contribute to the unified knowledge database by posting knowledge gained by engineers and construction staff participating in the platform. The Contributors share new insights and success stories obtained at construction sites with other users by posting them on the platform, enriching the overall knowledge base. Generative AI is used to analyze the posted knowledge and integrate it into the knowledge database.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the generation unit, provision unit, and posting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects and analyzes past project data. The provision unit is implemented by the control unit 46A of the smart device 14, which provides the generated solutions to the construction company. The posting unit is implemented by the control unit 46A of the smart device 14, which posts the knowledge gained by engineers and construction staff and integrates it into a knowledge database. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements, including the generation unit, provision unit, and posting unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects and analyzes past project data. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides the generated solutions to the construction company. The posting unit is implemented by the control unit 46A of the smart glasses 214, which posts the knowledge gained by engineers and construction staff and integrates it into a knowledge database. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the generation unit, provision unit, and posting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects and analyzes past project data. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides the generated solutions to the construction company. The posting unit is implemented by the control unit 46A of the headset terminal 314, which posts the knowledge acquired by engineers and construction staff and integrates it into the knowledge database. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the generation unit, provision unit, and posting unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects and analyzes past project data. The provision unit is implemented by the control unit 46A of the robot 414, which provides the generated solutions to the construction company. The posting unit is implemented by the control unit 46A of the robot 414, which posts the knowledge gained by engineers and construction staff and integrates it into the knowledge database. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A generation unit that learns from past project data and generates the best solution for specific problems, A supply unit that provides the solution generated by the generation unit to the construction company, It includes a posting section where engineers and construction staff participating in the platform can post the knowledge they have gained, contributing to an integrated knowledge database. A system characterized by the following features. (Note 2) The generating unit is Collect and analyze past project data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide the generated solution to the construction company. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned submission section, Engineers and construction staff can post the knowledge they have gained and integrate it into a knowledge database. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is This provides specific negotiation methods and tips for negotiating the installation of mobile phone base stations in condominium buildings. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Based on solutions provided by the AI, we support construction companies in efficiently conducting negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It estimates the user's emotions and adjusts how the solutions generated are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is From past project data, prioritize the analysis of the most relevant data based on specific criteria. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is By including environmental and weather data from the construction site in the data to be analyzed, more realistic solutions can be generated. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and determines the priority of solutions to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The data to be analyzed will include installation examples from other condominium developments and region-specific regulatory information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is By including the construction company's historical performance data in the data to be analyzed, we can generate solutions optimized for each company. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, We estimate the user's emotions and adjust how we present solutions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing solutions, we consider the current project progress of the construction company and provide them at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing solutions, we customize the offerings by incorporating past feedback from the construction company. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts the level of detail of the solutions provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing solutions, we take into account the geographical location of the construction company to provide region-specific solutions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing solutions, we will consider the resources available to the construction company and provide feasible solutions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned submission section, It estimates the user's sentiment and adjusts how the post content is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned submission section, When submitting knowledge, we refer to the submitter's past submission history to suggest the most suitable submission format. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned submission section, When submitting knowledge, the reliability of the submitted content is evaluated, and highly reliable submissions are displayed preferentially. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned submission section, It estimates user sentiment and prioritizes post content based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned submission section, When submitting knowledge, the system prioritizes displaying region-specific knowledge based on the submitter's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned submission section, When you submit a knowledge base article, the system evaluates the relevance of the content and prioritizes displaying the most relevant articles. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 generation unit that learns from past project data and generates the best solution for specific problems, A supply unit that provides the solution generated by the generation unit to the construction company, It includes a posting section where engineers and construction staff participating in the platform can post the knowledge they have gained, contributing to an integrated knowledge database. A system characterized by the following features.

2. The generating unit is Collect and analyze past project data. The system according to feature 1.

3. The aforementioned supply unit is, Provide the generated solution to the construction company. The system according to feature 1.

4. The aforementioned submission section, Engineers and construction staff can post the knowledge they have gained and integrate it into a knowledge database. The system according to feature 1.

5. The generating unit is This provides specific negotiation methods and tips for negotiating the installation of mobile phone base stations in condominium buildings. The system according to feature 1.

6. The aforementioned supply unit is, Based on solutions provided by the AI, we support construction companies in efficiently conducting negotiations. The system according to feature 1.

7. The generating unit is It estimates the user's emotions and adjusts how the solutions generated are presented based on those estimated emotions. The system according to feature 1.

8. The generating unit is From past project data, prioritize the analysis of the most relevant data based on specific criteria. The system according to feature 1.

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