System

Abstract

To provide a system that provides prompt and accurate responses to questions regarding Internet line installation.SOLUTION: A system according to an embodiment comprises: a reception unit; an analysis unit; and a generation unit. The reception unit receives questions regarding Internet line installation. The analysis unit analyzes an installation specification document on the basis of the questions received by the reception unit. The generation unit generates responses on the basis of the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to quickly and accurately respond to questions about the installation of Internet lines, and there is a demand for improved business efficiency.

[0005] The system according to the embodiment aims to quickly and accurately answer questions about the construction of Internet lines. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives a question about the construction of an Internet line. The analysis unit analyzes a construction specification based on the question received by the reception unit. The generation unit generates a response based on the information analyzed by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can quickly and accurately answer questions about the installation of Internet lines. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The Internet line construction question answering system according to the embodiment of the present invention is a system that automatically provides optimal answers to questions from construction companies using AI. In this system, the reception unit receives questions from construction companies, the analysis unit analyzes construction specifications, and the generation unit generates optimal answers. For example, the reception unit receives questions from construction companies. Next, the analysis unit analyzes the construction specifications based on the questions received by the reception unit. The analysis unit uses AI to analyze the contents of the construction specifications in detail and extracts appropriate information for the questions. Next, the generation unit generates an answer based on the information analyzed by the analysis unit. The generation unit generates optimal answers using AI and provides them to the construction company. This system eliminates the need for SB employees to check and answer questions, improving work efficiency. In addition, construction companies can get answers to questions 24 hours a day, improving convenience. Furthermore, this system can be applied to other examples of use, such as selection of contracts and answering questions, answering questions about the contents of KCS, and answering questions about the contents of inspection. For example, in selecting a contract, AI can analyze the contents of the contract and select the optimal contract. In addition, in answering questions about the contents of the KCS, the AI ​​can analyze the contents of the KCS and provide appropriate answers to the questions. In addition, in answering questions about the contents of the inspection, the AI ​​can analyze the contents of the inspection and provide appropriate answers to the questions. This system has a voice reception unit that accepts voice input, so that the user can input questions by voice. In addition, it has a learning unit that learns past questions and answers, so that the AI ​​can provide more accurate answers by learning past questions and answers. In addition, it has a diagram generation unit that generates answers including figures and tables, so that it can provide visually easy-to-understand answers. In addition, it has a reference unit that refers to related documents or databases, so that the AI ​​can provide more appropriate answers by referring to related information. This system has a function of estimating the user's emotions and adjusting the timing of accepting questions based on the estimated user's emotions. For example, if the user is in a hurry, the question can be accepted quickly. In addition, it has a function of analyzing the user's past question history and selecting an appropriate acceptance method when accepting a question.Furthermore, when accepting questions, the system has a function for filtering based on the user's current project or area of ​​interest, allowing the system to provide the most suitable question acceptance method for the user. This allows the Internet line construction question answering system to automatically provide the most suitable answer to questions from construction companies using AI.

[0029] The Internet line construction question answering system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives a question from a construction company. The question from the construction company includes, for example, a text format, a voice format, and the like, but is not limited to such examples. The reception unit receives, for example, a question in text format. The reception unit can also receive a question in voice format. Furthermore, the reception unit can analyze the content of the question and convert it into an appropriate format. For example, the reception unit converts a question in voice format into text format. The analysis unit analyzes the construction specification based on the question received by the reception unit. The analysis unit analyzes the content of the construction specification using, for example, natural language processing technology. Furthermore, the analysis unit can also analyze the content of the construction specification using a machine learning algorithm. Furthermore, the analysis unit analyzes the content of the construction specification in detail and extracts appropriate information for the question. For example, the analysis unit extracts a part related to the question from the construction specification. The generation unit generates an answer based on the information analyzed by the analysis unit. The generation unit generates an answer using, for example, a text generation AI (for example, LLM). The generation unit can also generate answers using a multimodal generation AI. Furthermore, the generation unit provides the generated answers to the construction company. For example, the generation unit generates answers in text format and provides them to the construction company. The generation unit can also generate answers in audio format and provide them to the construction company. This allows the Internet line construction question answering system to automatically provide optimal answers to questions from construction companies using AI.

[0030] The reception unit receives questions from the construction company. The questions from the construction company include, for example, text format, voice format, and the like, but are not limited to such examples. The reception unit receives, for example, questions in text format. The reception unit can also receive questions in voice format. Furthermore, the reception unit can analyze the content of the question and convert it into an appropriate format. For example, the reception unit converts the voice format question into text format. Specifically, the reception unit converts the voice format question into text format using voice recognition technology. This voice recognition technology can convert voice into text with high accuracy even in a noisy environment. Furthermore, when receiving a text format question, the reception unit can analyze the intention of the question using natural language processing technology and classify it into an appropriate category. For example, if the question is "Please tell me how to connect optical fiber," the reception unit classifies this question into a category related to "connection method." Furthermore, the reception unit can evaluate the urgency and importance of the question when analyzing the content of the question. For example, the entire system can be adjusted so that questions with high urgency are given priority. This enables the reception unit to efficiently receive questions in a variety of formats from construction companies, convert them into an appropriate format, and prepare to hand them over to the analysis unit.

[0031] The analysis unit analyzes the construction specification based on the question received by the reception unit. The analysis unit analyzes the contents of the construction specification using, for example, natural language processing technology. The analysis unit can also analyze the contents of the construction specification using a machine learning algorithm. Furthermore, the analysis unit analyzes the contents of the construction specification in detail and extracts appropriate information for the question. For example, the analysis unit extracts a part related to the question from the construction specification. Specifically, the analysis unit tokenizes the text of the construction specification using natural language processing technology and analyzes the meaning of each token. This allows the contents of the construction specification to be treated as structured data. Furthermore, the machine learning algorithm can be used to learn data on past questions and answers and derive optimal answers to new questions. For example, when extracting information on "optical fiber connection method" from the construction specification, related sections and paragraphs are identified and their contents are analyzed in detail. Furthermore, the analysis unit can understand the context of the question and integrate related information. For example, if the question is "Please tell me how to connect optical fiber," the analysis unit will extract detailed procedures and points to note regarding the connection method from the construction specifications and provide them as basic information for the answer. This allows the analysis unit to perform a sophisticated analysis of the contents of the construction specifications and quickly and accurately extract the appropriate information to answer the question.

[0032] The generation unit generates an answer based on the information analyzed by the analysis unit. The generation unit generates an answer using, for example, a text generation AI (for example, LLM). The generation unit can also generate an answer using a multimodal generation AI. Furthermore, the generation unit provides the generated answer to the construction company. For example, the generation unit generates an answer in text format and provides it to the construction company. The generation unit can also generate an answer in voice format and provide it to the construction company. Specifically, the generation unit generates natural sentences using a text generation AI based on the information provided by the analysis unit. This text generation AI is based on a large-scale language model and can generate grammatically correct and meaningful answers. Furthermore, by using a multimodal generation AI, it is possible to generate answers that include not only text but also images and diagrams. For example, in response to a question about a method of connecting optical fibers, in addition to a text explanation, an answer that includes a diagram showing the connection procedure and a video link can be generated. Furthermore, when providing the generated answer to the construction company, the generation unit can provide it in a format according to the user's preference. For example, in addition to sending a text-format answer by email, it is also possible to provide a voice-format answer by phone or voice message. This enables the generation unit to generate optimal answers based on the information provided by the analysis unit, in accordance with the needs of the construction company, and provide them promptly and appropriately.

[0033] The Internet line construction question answering system includes a voice reception unit that receives voice input. The voice reception unit allows, for example, a user to input a question by voice. The voice reception unit converts the voice into text using, for example, a voice recognition technology. The voice reception unit also includes a function of learning the voice characteristics of a specific speaker in order to improve the accuracy of the voice input. For example, the voice reception unit accumulates user voice data and learns the voice characteristics of each user to improve the accuracy of the voice recognition. As a result, even a user who has difficulty writing by hand or typing can input a question by using the voice input. A part or all of the above-mentioned processing in the voice reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice reception unit can input the voice data to a generation AI and cause the generation AI to convert the voice data into text data.

[0034] The Internet line construction question answering system includes a learning unit that learns past questions and answers. The learning unit can provide more accurate answers by learning, for example, past questions and answers. The learning unit learns past questions and answers using, for example, a machine learning algorithm. The learning unit can also perform learning based on the type of data set. For example, the learning unit performs learning using a data set of past questions and answers. As a result, the learning unit can provide more accurate answers by learning past questions and answers. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input data of past questions and answers to a generation AI and cause the generation AI to perform learning.

[0035] The Internet line construction question answering system includes a diagram generating unit that generates an answer including a diagram or a table. The diagram generating unit can provide an answer that is visually easy to understand, for example. The diagram generating unit generates an answer in a format such as a graph, a chart, or a table format. The diagram generating unit can also integrate the generated diagram or table with text data and display it. For example, the diagram generating unit integrates the generated diagram or table with text data to provide it in a format that is visually easy to understand. In this way, by generating an answer including a diagram or a table, it is possible to provide an answer that is visually easy to understand. A part or all of the above-mentioned processing in the diagram generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagram generating unit can input data of the diagram or table to the generating AI and cause the generating AI to generate the diagram or table.

[0036] The Internet line construction question answering system includes a reference unit that refers to a related document or database. The reference unit can provide a more appropriate answer by, for example, referring to a related document or database. The reference unit refers to documents such as technical documents and types of databases. The reference unit can also extract appropriate information based on the type of document to be referenced. For example, the reference unit extracts related information from technical documents. As a result, the reference unit can provide a more appropriate answer by referring to a related document or database. Some or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input data of a document or database to a generation AI and cause the generation AI to extract information.

[0037] The reception unit can analyze the user's past question history and select the optimal reception method. The reception unit, for example, automatically displays questions frequently asked by the user in the past as candidates. For example, the reception unit analyzes the past question history and displays appropriate candidates based on the questions asked by the user in the past. The reception unit can also preferentially suggest input methods (such as voice and text) used by the user in the past. For example, the reception unit predicts and suggests questions to be used in a specific time period from the past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past question history data to the generation AI and cause the generation AI to select the optimal reception method.

[0038] The reception unit can perform filtering based on the user's current project or field of interest when receiving a question. The reception unit, for example, preferentially receives questions related to a project currently in progress by the user. For example, the reception unit filters related questions based on the user's project information. The reception unit can also filter and display related questions based on the user's field of interest. For example, the reception unit filters questions based on keywords related to the user's field of interest. Furthermore, the reception unit can also suggest appropriate questions according to the progress of the user's project. For example, the reception unit analyzes the progress of the project and suggests related questions. As a result, questions can be filtered based on the user's current project and field of interest, so that highly relevant questions can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input project information and field of interest data to the generation AI and cause the generation AI to perform filtering.

[0039] When receiving a question, the reception unit can preferentially receive a highly relevant question in consideration of the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially receives questions related to the area. For example, the reception unit filters related questions based on the geographical location information of the user. In addition, when the user is moving, the reception unit can also suggest related questions based on the current location. For example, the reception unit suggests related questions based on the current location of the user. Furthermore, when the user is in a specific facility, the reception unit can also preferentially receive questions related to the facility. For example, the reception unit filters related questions based on information about the facility. As a result, by considering the geographical location information of the user, it is possible to preferentially receive questions with a high degree of relevance. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input geographical location information data to the generation AI and cause the generation AI to execute filtering of related questions.

[0040] The reception unit can analyze the user's social media activity and receive related questions when receiving a question. The reception unit can suggest related questions based on, for example, information shared by the user on social media. For example, the reception unit can analyze the content of the user's social media posts and preferentially receive related questions. The reception unit can also suggest related questions with reference to the activities of the user's social media followers and friends. For example, the reception unit can suggest related questions based on the activities of the followers and friends. In this way, by analyzing the user's social media activities, it is possible to preferentially receive related questions. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input social media activity data to a generation AI and cause the generation AI to execute the suggestion of related questions.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the construction specification during analysis. For example, in the case of an important construction specification, the AI ​​performs a detailed analysis. For example, the analysis unit evaluates the importance of the construction specification and analyzes important parts in detail. In addition, the analysis unit can perform a standard analysis in the case of a general construction specification. For example, the analysis unit performs a standard analysis based on the importance of the construction specification. Furthermore, in the case of a simple construction specification, the analysis unit can perform a simplified analysis. For example, the analysis unit performs a simplified analysis based on the importance of the construction specification. As a result, an appropriate analysis can be performed by adjusting the level of detail of the analysis based on the importance of the construction specification. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the construction specification to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the construction specification during analysis. For example, in the case of a construction specification related to electrical work, the analysis unit applies an analysis algorithm dedicated to electrical work by the AI. For example, the analysis unit applies an algorithm dedicated to electrical work to analyze a construction specification related to electrical work. In addition, in the case of a construction specification related to piping work, the analysis unit can also apply an analysis algorithm dedicated to piping work. For example, the analysis unit applies an algorithm dedicated to piping work to analyze a construction specification related to piping work. In addition, in the case of a construction specification related to architectural work, the analysis unit can also apply an analysis algorithm dedicated to architectural work. For example, the analysis unit applies an algorithm dedicated to architectural work to analyze a construction specification related to architectural work. As a result, the accuracy of the analysis is improved by applying an appropriate analysis algorithm depending on the category of the construction specification. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the construction specification to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] The analysis unit can determine the priority of the analysis based on the submission time of the construction specification during the analysis. In the analysis unit, for example, in the case of an urgent construction specification, the AI ​​performs the analysis preferentially. For example, the analysis unit evaluates the submission time of the construction specification and prioritizes the analysis of the urgent one. In addition, the analysis unit can also perform the analysis with a standard priority in the case of a normal construction specification. For example, the analysis unit performs the analysis with a standard priority based on the submission time of the construction specification. Furthermore, the analysis unit can also perform the analysis with priority in the case of a construction specification whose deadline is approaching. For example, the analysis unit determines the priority based on the submission time of the construction specification and prioritizes the analysis of the construction specification whose deadline is approaching. As a result, by determining the priority of the analysis based on the submission time of the construction specification, it is possible to analyze the urgent construction specification with priority. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the construction specification to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the construction specifications during analysis. In the analysis unit, for example, in the case of important construction specifications, the AI ​​performs analysis preferentially. For example, the analysis unit evaluates the relevance of the construction specifications and prioritizes analysis of important ones. In addition, the analysis unit can also perform analysis preferentially in the case of highly relevant construction specifications. For example, the analysis unit determines the priority order based on the relevance of the construction specifications and prioritizes analysis of highly relevant ones. Furthermore, the analysis unit can also postpone analysis in the case of low-relevance construction specifications. For example, the analysis unit determines the priority order based on the relevance of the construction specifications and prioritizes analysis of low-relevance ones. As a result, by adjusting the order of analysis based on the relevance of the construction specifications, it is possible to prioritize analysis of highly relevant construction specifications. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the construction specifications to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] The generation unit can adjust the level of detail of the answer based on the importance of the question when generating the answer. For example, in the case of an important question, the generation unit generates a detailed answer. For example, the generation unit evaluates the importance of the question and generates a detailed answer for the important question. In addition, the generation unit can also generate a standard answer in the case of a general question. For example, the generation unit generates a standard answer based on the importance of the question. Furthermore, in the case of a simple question, the generation unit can also generate a simplified answer. For example, the generation unit generates a simplified answer based on the importance of the question. In this way, it is possible to provide an appropriate answer by adjusting the level of detail of the answer based on the importance of the question. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the importance data of the question to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0046] The generation unit can apply different generation algorithms depending on the category of the question when generating an answer. For example, in the case of a technical question, the generation unit applies a generation algorithm dedicated to technology. For example, the generation unit applies an algorithm dedicated to technology to a technical question. In addition, in the case of a question regarding a contract, the generation unit can also apply a generation algorithm dedicated to contracts. For example, the generation unit applies an algorithm dedicated to contracts to a question regarding a contract. In addition, in the case of a general question, the generation unit can also apply a generation algorithm dedicated to general questions. For example, the generation unit applies an algorithm dedicated to general questions. This improves the accuracy of the answer by applying an appropriate generation algorithm depending on the category of the question. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0047] The generation unit can determine the priority of the answers based on the time of submission of the question when generating the answer. In the generation unit, for example, in the case of an urgent question, the AI ​​generates the answer with priority. For example, the generation unit evaluates the time of submission of the question and answers the urgent one with priority. In addition, the generation unit can also generate an answer with standard priority in the case of a normal question. For example, the generation unit generates an answer with standard priority based on the time of submission of the question. Furthermore, the generation unit can also generate an answer with priority in the case of a question with an approaching deadline. For example, the generation unit determines the priority based on the time of submission of the question and answers the question with an approaching deadline with priority. In this way, by determining the priority of the answers based on the time of submission of the question, it is possible to answer the urgent question with priority. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the submission time data of the question to the generation AI and cause the generation AI to execute the determination of the priority of the answers.

[0048] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. In the generation unit, for example, in the case of an important question, the AI ​​generates answers preferentially. For example, the generation unit evaluates the relevance of the questions and answers the important questions preferentially. In addition, the generation unit can also generate answers preferentially in the case of a highly relevant question. For example, the generation unit determines the priority order based on the relevance of the questions and answers the highly relevant questions preferentially. Furthermore, in the case of a question with low relevance, the generation unit can also generate answers later. For example, the generation unit determines the priority order based on the relevance of the questions and answers the less relevant questions later. In this way, by adjusting the order of answers based on the relevance of the questions, it is possible to answer questions with high relevance preferentially. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the relevance data of the questions to the generation AI and cause the generation AI to adjust the order of the answers.

[0049] The voice reception unit can analyze the user's past voice input history at the time of voice input and select the optimal reception method. For example, the voice reception unit preferentially suggests voice commands that the user has frequently used in the past. For example, the voice reception unit analyzes the past voice input history and suggests appropriate candidates based on the voice commands that the user has used in the past. The voice reception unit can also predict and suggest a voice command to be used in a specific time period from the user's past voice input history. For example, the voice reception unit predicts a voice command to be used in a specific time period based on the past voice input history. Furthermore, the voice reception unit can preferentially suggest a voice input method (such as abbreviated commands or detailed commands) that the user has used in the past. For example, the voice reception unit suggests an appropriate voice input method based on the past voice input history. In this way, the optimal reception method can be selected by analyzing the user's past voice input history. A part or all of the above-mentioned processing in the voice reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice reception unit can input past voice input history data to a generation AI and cause the generation AI to select the optimal reception method.

[0050] The voice reception unit can preferentially receive a highly relevant voice input in consideration of the geographical location information of the user when inputting voice. For example, when the user is in a specific area, the voice reception unit preferentially receives a voice input related to the area. For example, the voice reception unit filters the relevant voice input based on the geographical location information of the user. In addition, when the user is moving, the voice reception unit can also suggest a related voice input based on the current location. For example, the voice reception unit suggests a related voice input based on the current location of the user. Furthermore, when the user is in a specific facility, the voice reception unit can also preferentially receive a voice input related to the facility. For example, the voice reception unit filters the related voice input based on the information of the facility. As a result, by considering the geographical location information of the user, it is possible to preferentially receive a highly relevant voice input. A part or all of the above-mentioned processing in the voice reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice reception unit can input geographical location information data to the generation AI and cause the generation AI to execute filtering of the related voice input.

[0051] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit, for example, selects an optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and selects an optimal algorithm. The learning unit can also apply an algorithm that improves the efficiency of learning from the past learning data. For example, the learning unit applies an efficient algorithm based on past learning data. Furthermore, the learning unit can analyze past learning data and apply an algorithm that improves the accuracy of learning. For example, the learning unit applies a highly accurate algorithm based on past learning data. Thereby, the learning algorithm can be optimized by referring to the past learning data. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to execute optimization of the learning algorithm.

[0052] The learning unit can weight the learning data based on the time of question submission during learning. For example, in the case of an urgent question, the learning unit weights the learning data higher. For example, the learning unit evaluates the time of question submission and weights the urgent question higher. The learning unit can also weight normal questions. For example, the learning unit weights standard questions based on the time of question submission. Furthermore, the learning unit can weight questions with a deadline approaching higher. For example, the learning unit determines weights based on the time of question submission and weights questions with a deadline approaching higher. In this way, weighting of the learning data based on the time of question submission can be performed with priority for learning urgent questions. Part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input question submission time data to the generation AI and cause the generation AI to perform weighting of the learning data.

[0053] The diagram generating unit can adjust the level of detail of the diagram based on the importance of the question when generating the diagram. For example, in the case of an important question, the diagram generating unit generates a detailed diagram. For example, the diagram generating unit evaluates the importance of the question and generates a detailed diagram for the important question. In addition, the diagram generating unit can also generate a standard diagram in the case of a general question. For example, the diagram generating unit generates a standard diagram based on the importance of the question. Furthermore, in the case of a simple question, the diagram generating unit can also generate a simplified diagram. For example, the diagram generating unit generates a simplified diagram based on the importance of the question. As a result, an appropriate diagram can be generated by adjusting the level of detail of the diagram based on the importance of the question. A part or all of the above-mentioned processing in the diagram generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagram generating unit can input the importance data of the question to the generating AI and cause the generating AI to adjust the level of detail of the diagram.

[0054] The diagram generating unit can determine the priority of the diagram based on the time of question submission when generating the diagram. In the diagram generating unit, for example, in the case of an urgent question, the AI ​​generates the diagram preferentially. For example, the diagram generating unit evaluates the time of question submission and generates a diagram preferentially for an urgent question. In addition, the diagram generating unit can also generate a diagram with a standard priority in the case of a normal question. For example, the diagram generating unit generates a diagram with a standard priority based on the time of question submission. Furthermore, the diagram generating unit can also generate a diagram preferentially in the case of a question with an approaching deadline. For example, the diagram generating unit determines the priority based on the time of question submission and generates a diagram preferentially for a question with an approaching deadline. In this way, by determining the priority of the diagram based on the time of question submission, it is possible to generate a diagram for an urgent question with priority. Part or all of the above-mentioned processing in the diagram generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagram generating unit can input question submission time data to the generating AI and cause the generating AI to execute the determination of the priority of the diagram.

[0055] The reference unit can select the optimal document by referring to the past reference history at the time of reference. For example, the reference unit preferentially displays documents that the user frequently referred to in the past. For example, the reference unit analyzes the past reference history and displays appropriate candidates based on the documents that the user frequently referred to in the past. The reference unit can also predict and suggest documents to be used in a specific time period from the user's past reference history. For example, the reference unit predicts documents to be used in a specific time period based on the past reference history. Furthermore, the reference unit can preferentially suggest reference methods (keyword search, category search, etc.) that the user used in the past. For example, the reference unit suggests an appropriate reference method based on the past reference history. In this way, the optimal document can be selected by referring to the past reference history. A part or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input past reference history data to the generation AI and cause the generation AI to select the optimal document.

[0056] The reference unit can weight the reference document based on the time of submission of the question at the time of reference. For example, in the case of an urgent question, the reference unit increases the weighting of the reference document. For example, the reference unit evaluates the time of submission of the question and increases the weighting for urgent questions. The reference unit can also perform standard weighting for normal questions. For example, the reference unit performs standard weighting based on the time of submission of the question. Furthermore, the reference unit can also increase the weighting for questions with a deadline approaching. For example, the reference unit determines the weighting based on the time of submission of the question and increases the weighting for questions with a deadline approaching. In this way, by weighting the reference document based on the time of submission of the question, documents for urgent questions can be preferentially referenced. Part or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input question submission time data to the generation AI and cause the generation AI to execute weighting of the reference document.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates based on the past question history. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be used in a specific time period based on the past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history.

[0059] The reception unit can preferentially receive highly relevant questions by taking into account the geographical location information of the user. For example, when the user is in a specific area, the reception unit can preferentially receive questions related to the area. In addition, when the user is moving, the reception unit can also suggest related questions based on the current location. Furthermore, when the user is in a specific facility, the reception unit can also preferentially receive questions related to the facility. In this way, by taking into account the geographical location information of the user, highly relevant questions can be preferentially received.

[0060] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the construction specifications. For example, the analysis unit can perform a detailed analysis for important construction specifications. Also, the analysis unit can perform a standard analysis for general construction specifications. Furthermore, the analysis unit can perform a simplified analysis for simple construction specifications. In this way, by adjusting the level of detail of the analysis based on the importance of the construction specifications, an appropriate analysis can be performed.

[0061] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit can generate a detailed answer for an important question. Also, the generation unit can generate a standard answer for a general question. Furthermore, the generation unit can generate a simplified answer for a simple question. In this way, by adjusting the level of detail of the answer based on the importance of the question, it is possible to provide an appropriate answer.

[0062] The voice reception unit can analyze the user's past voice input history at the time of voice input and select the optimal reception method. For example, the voice reception unit can preferentially suggest voice commands that the user has frequently used in the past. The voice reception unit can also predict and suggest voice commands to be used in a specific time period based on the past voice input history. Furthermore, the voice reception unit can preferentially suggest voice input methods (abbreviated commands, detailed commands, etc.) that the user has used in the past. This makes it possible to select the optimal reception method by analyzing the user's past voice input history.

[0063] At the time of reference, the reference unit can select the most suitable document by referring to the past reference history. For example, the reference unit can preferentially display documents that the user has frequently referenced in the past. The reference unit can also predict and suggest documents to be used in a specific time period based on the past reference history. Furthermore, the reference unit can preferentially suggest reference methods (keyword search, category search, etc.) that the user has used in the past. This makes it possible to select the most suitable document by referring to the past reference history.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The reception unit receives questions from the construction company. Questions can be in text or voice format. The reception unit can also analyze the content of the questions and convert them into an appropriate format, such as converting voice questions into text format. Step 2: The analysis unit analyzes the construction specifications based on the questions received by the reception unit. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the contents of the construction specifications in detail and extract information appropriate to the questions. Step 3: The generation unit generates a response based on the information analyzed by the analysis unit. The generation unit generates a response using a text generation AI or a multimodal generation AI and provides it to the construction company. The response format includes text and audio formats.

[0066] (Example 2) The Internet line construction question answering system according to the embodiment of the present invention is a system that automatically provides optimal answers to questions from construction companies using AI. In this system, the reception unit receives questions from construction companies, the analysis unit analyzes construction specifications, and the generation unit generates optimal answers. For example, the reception unit receives questions from construction companies. Next, the analysis unit analyzes the construction specifications based on the questions received by the reception unit. The analysis unit uses AI to analyze the contents of the construction specifications in detail and extracts appropriate information for the questions. Next, the generation unit generates an answer based on the information analyzed by the analysis unit. The generation unit generates optimal answers using AI and provides them to the construction company. This system eliminates the need for SB employees to check and answer questions, improving work efficiency. In addition, construction companies can get answers to questions 24 hours a day, improving convenience. Furthermore, this system can be applied to other examples of use, such as selection of contracts and answering questions, answering questions about the contents of KCS, and answering questions about the contents of inspection. For example, in selecting a contract, AI can analyze the contents of the contract and select the optimal contract. In addition, in answering questions about the contents of the KCS, the AI ​​can analyze the contents of the KCS and provide appropriate answers to the questions. In addition, in answering questions about the contents of the inspection, the AI ​​can analyze the contents of the inspection and provide appropriate answers to the questions. This system has a voice reception unit that accepts voice input, so that the user can input questions by voice. In addition, it has a learning unit that learns past questions and answers, so that the AI ​​can provide more accurate answers by learning past questions and answers. In addition, it has a diagram generation unit that generates answers including figures and tables, so that it can provide visually easy-to-understand answers. In addition, it has a reference unit that refers to related documents or databases, so that the AI ​​can provide more appropriate answers by referring to related information. This system has a function of estimating the user's emotions and adjusting the timing of accepting questions based on the estimated user's emotions. For example, if the user is in a hurry, the question can be accepted quickly. In addition, it has a function of analyzing the user's past question history and selecting an appropriate acceptance method when accepting a question.Furthermore, when accepting questions, the system has a function for filtering based on the user's current project or area of ​​interest, allowing the system to provide the most suitable question acceptance method for the user. This allows the Internet line construction question answering system to automatically provide the most suitable answer to questions from construction companies using AI.

[0067] The Internet line construction question answering system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives a question from a construction company. The question from the construction company includes, for example, a text format, a voice format, and the like, but is not limited to such examples. The reception unit receives, for example, a question in text format. The reception unit can also receive a question in voice format. Furthermore, the reception unit can analyze the content of the question and convert it into an appropriate format. For example, the reception unit converts a question in voice format into text format. The analysis unit analyzes the construction specification based on the question received by the reception unit. The analysis unit analyzes the content of the construction specification using, for example, natural language processing technology. Furthermore, the analysis unit can also analyze the content of the construction specification using a machine learning algorithm. Furthermore, the analysis unit analyzes the content of the construction specification in detail and extracts appropriate information for the question. For example, the analysis unit extracts a part related to the question from the construction specification. The generation unit generates an answer based on the information analyzed by the analysis unit. The generation unit generates an answer using, for example, a text generation AI (for example, LLM). The generation unit can also generate answers using a multimodal generation AI. Furthermore, the generation unit provides the generated answers to the construction company. For example, the generation unit generates answers in text format and provides them to the construction company. The generation unit can also generate answers in audio format and provide them to the construction company. This allows the Internet line construction question answering system to automatically provide optimal answers to questions from construction companies using AI.

[0068] The reception unit receives questions from the construction company. The questions from the construction company include, for example, text format, voice format, and the like, but are not limited to such examples. The reception unit receives, for example, questions in text format. The reception unit can also receive questions in voice format. Furthermore, the reception unit can analyze the content of the question and convert it into an appropriate format. For example, the reception unit converts the voice format question into text format. Specifically, the reception unit converts the voice format question into text format using voice recognition technology. This voice recognition technology can convert voice into text with high accuracy even in a noisy environment. Furthermore, when receiving a text format question, the reception unit can analyze the intention of the question using natural language processing technology and classify it into an appropriate category. For example, if the question is "Please tell me how to connect optical fiber," the reception unit classifies this question into a category related to "connection method." Furthermore, the reception unit can evaluate the urgency and importance of the question when analyzing the content of the question. For example, the entire system can be adjusted so that questions with high urgency are given priority. This enables the reception unit to efficiently receive questions in a variety of formats from construction companies, convert them into an appropriate format, and prepare to hand them over to the analysis unit.

[0069] The analysis unit analyzes the construction specification based on the question received by the reception unit. The analysis unit analyzes the contents of the construction specification using, for example, natural language processing technology. The analysis unit can also analyze the contents of the construction specification using a machine learning algorithm. Furthermore, the analysis unit analyzes the contents of the construction specification in detail and extracts appropriate information for the question. For example, the analysis unit extracts a part related to the question from the construction specification. Specifically, the analysis unit tokenizes the text of the construction specification using natural language processing technology and analyzes the meaning of each token. This allows the contents of the construction specification to be treated as structured data. Furthermore, the machine learning algorithm can be used to learn data on past questions and answers and derive optimal answers to new questions. For example, when extracting information on "optical fiber connection method" from the construction specification, related sections and paragraphs are identified and their contents are analyzed in detail. Furthermore, the analysis unit can understand the context of the question and integrate related information. For example, if the question is "Please tell me how to connect optical fiber," the analysis unit will extract detailed procedures and points to note regarding the connection method from the construction specifications and provide them as basic information for the answer. This allows the analysis unit to perform a sophisticated analysis of the contents of the construction specifications and quickly and accurately extract the appropriate information to answer the question.

[0070] The generation unit generates an answer based on the information analyzed by the analysis unit. The generation unit generates an answer using, for example, a text generation AI (for example, LLM). The generation unit can also generate an answer using a multimodal generation AI. Furthermore, the generation unit provides the generated answer to the construction company. For example, the generation unit generates an answer in text format and provides it to the construction company. The generation unit can also generate an answer in voice format and provide it to the construction company. Specifically, the generation unit generates natural sentences using a text generation AI based on the information provided by the analysis unit. This text generation AI is based on a large-scale language model and can generate grammatically correct and meaningful answers. Furthermore, by using a multimodal generation AI, it is possible to generate answers that include not only text but also images and diagrams. For example, in response to a question about a method of connecting optical fibers, in addition to a text explanation, an answer that includes a diagram showing the connection procedure and a video link can be generated. Furthermore, when providing the generated answer to the construction company, the generation unit can provide it in a format according to the user's preference. For example, in addition to sending a text-format answer by email, it is also possible to provide a voice-format answer by phone or voice message. This enables the generation unit to generate optimal answers based on the information provided by the analysis unit, in accordance with the needs of the construction company, and provide them promptly and appropriately.

[0071] The Internet line construction question answering system includes a voice reception unit that receives voice input. The voice reception unit allows, for example, a user to input a question by voice. The voice reception unit converts the voice into text using, for example, a voice recognition technology. The voice reception unit also includes a function of learning the voice characteristics of a specific speaker in order to improve the accuracy of the voice input. For example, the voice reception unit accumulates user voice data and learns the voice characteristics of each user to improve the accuracy of the voice recognition. As a result, even a user who has difficulty writing by hand or typing can input a question by using the voice input. A part or all of the above-mentioned processing in the voice reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice reception unit can input the voice data to a generation AI and cause the generation AI to convert the voice data into text data.

[0072] The Internet line construction question answering system includes a learning unit that learns past questions and answers. The learning unit can provide more accurate answers by learning, for example, past questions and answers. The learning unit learns past questions and answers using, for example, a machine learning algorithm. The learning unit can also perform learning based on the type of data set. For example, the learning unit performs learning using a data set of past questions and answers. As a result, the learning unit can provide more accurate answers by learning past questions and answers. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input data of past questions and answers to a generation AI and cause the generation AI to perform learning.

[0073] The Internet line construction question answering system includes a diagram generating unit that generates an answer including a diagram or a table. The diagram generating unit can provide an answer that is visually easy to understand, for example. The diagram generating unit generates an answer in a format such as a graph, a chart, or a table format. The diagram generating unit can also integrate the generated diagram or table with text data and display it. For example, the diagram generating unit integrates the generated diagram or table with text data to provide it in a format that is visually easy to understand. In this way, by generating an answer including a diagram or a table, it is possible to provide an answer that is visually easy to understand. A part or all of the above-mentioned processing in the diagram generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagram generating unit can input data of the diagram or table to the generating AI and cause the generating AI to generate the diagram or table.

[0074] The Internet line construction question answering system includes a reference unit that refers to a related document or database. The reference unit can provide a more appropriate answer by, for example, referring to a related document or database. The reference unit refers to documents such as technical documents and types of databases. The reference unit can also extract appropriate information based on the type of document to be referenced. For example, the reference unit extracts related information from technical documents. As a result, the reference unit can provide a more appropriate answer by referring to a related document or database. Some or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input data of a document or database to a generation AI and cause the generation AI to extract information.

[0075] The reception unit can estimate the user's emotion and adjust the timing of receiving the question based on the estimated user's emotion. For example, when the user is in a hurry, the reception unit adjusts the timing so that the AI ​​can quickly receive the question. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the fluctuation of the heart rate. As a result, the timing of receiving the question can be adjusted according to the user's emotion, so that the question can be received at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0076] The reception unit can analyze the user's past question history and select the optimal reception method. The reception unit, for example, automatically displays questions frequently asked by the user in the past as candidates. For example, the reception unit analyzes the past question history and displays appropriate candidates based on the questions asked by the user in the past. The reception unit can also preferentially suggest input methods (such as voice and text) used by the user in the past. For example, the reception unit predicts and suggests questions to be used in a specific time period from the past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past question history data to the generation AI and cause the generation AI to select the optimal reception method.

[0077] The reception unit can perform filtering based on the user's current project or field of interest when receiving a question. The reception unit, for example, preferentially receives questions related to a project currently in progress by the user. For example, the reception unit filters related questions based on the user's project information. The reception unit can also filter and display related questions based on the user's field of interest. For example, the reception unit filters questions based on keywords related to the user's field of interest. Furthermore, the reception unit can also suggest appropriate questions according to the progress of the user's project. For example, the reception unit analyzes the progress of the project and suggests related questions. As a result, questions can be filtered based on the user's current project and field of interest, so that highly relevant questions can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input project information and field of interest data to the generation AI and cause the generation AI to perform filtering.

[0078] The reception unit can estimate the user's emotion and determine the priority of questions to be received based on the estimated user's emotion. For example, when the user is in a hurry, the reception unit preferentially receives important questions from the AI. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the fluctuation of the heart rate. As a result, the priority of questions can be determined according to the user's emotion, so that important questions can be preferentially received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0079] When receiving a question, the reception unit can preferentially receive a highly relevant question in consideration of the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially receives questions related to the area. For example, the reception unit filters related questions based on the geographical location information of the user. In addition, when the user is moving, the reception unit can also suggest related questions based on the current location. For example, the reception unit suggests related questions based on the current location of the user. Furthermore, when the user is in a specific facility, the reception unit can also preferentially receive questions related to the facility. For example, the reception unit filters related questions based on information about the facility. As a result, by considering the geographical location information of the user, it is possible to preferentially receive questions with a high degree of relevance. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input geographical location information data to the generation AI and cause the generation AI to execute filtering of related questions.

[0080] The reception unit can analyze the user's social media activity and receive related questions when receiving a question. The reception unit can suggest related questions based on, for example, information shared by the user on social media. For example, the reception unit can analyze the content of the user's social media posts and preferentially receive related questions. The reception unit can also suggest related questions with reference to the activities of the user's social media followers and friends. For example, the reception unit can suggest related questions based on the activities of the followers and friends. In this way, by analyzing the user's social media activities, it is possible to preferentially receive related questions. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input social media activity data to a generation AI and cause the generation AI to execute the suggestion of related questions.

[0081] The analysis unit can estimate the user's emotion and adjust the priority of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit uses AI to analyze important parts with priority. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. As a result, important parts can be analyzed with priority by adjusting the priority of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the construction specification during analysis. For example, in the case of an important construction specification, the AI ​​performs a detailed analysis. For example, the analysis unit evaluates the importance of the construction specification and analyzes important parts in detail. In addition, the analysis unit can perform a standard analysis in the case of a general construction specification. For example, the analysis unit performs a standard analysis based on the importance of the construction specification. Furthermore, in the case of a simple construction specification, the analysis unit can perform a simplified analysis. For example, the analysis unit performs a simplified analysis based on the importance of the construction specification. As a result, an appropriate analysis can be performed by adjusting the level of detail of the analysis based on the importance of the construction specification. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the construction specification to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the construction specification during analysis. For example, in the case of a construction specification related to electrical work, the analysis unit applies an analysis algorithm dedicated to electrical work by the AI. For example, the analysis unit applies an algorithm dedicated to electrical work to analyze a construction specification related to electrical work. In addition, in the case of a construction specification related to piping work, the analysis unit can also apply an analysis algorithm dedicated to piping work. For example, the analysis unit applies an algorithm dedicated to piping work to analyze a construction specification related to piping work. In addition, in the case of a construction specification related to architectural work, the analysis unit can also apply an analysis algorithm dedicated to architectural work. For example, the analysis unit applies an algorithm dedicated to architectural work to analyze a construction specification related to architectural work. As a result, the accuracy of the analysis is improved by applying an appropriate analysis algorithm depending on the category of the construction specification. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the construction specification to the generation AI and cause the generation AI to apply the analysis algorithm.

[0084] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. This allows the display method of the analysis result to be adjusted according to the user's emotion, thereby providing a highly visible display method. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0085] The analysis unit can determine the priority of the analysis based on the submission time of the construction specification during the analysis. For example, in the case of an urgent construction specification, the analysis unit allows the AI ​​to perform the analysis preferentially. For example, the analysis unit evaluates the submission time of the construction specification and prioritizes the analysis of the urgent one. In addition, the analysis unit can also perform the analysis with a standard priority in the case of a normal construction specification. For example, the analysis unit performs the analysis with a standard priority based on the submission time of the construction specification. Furthermore, the analysis unit can also perform the analysis with priority in the case of a construction specification whose deadline is approaching. For example, the analysis unit determines the priority based on the submission time of the construction specification and prioritizes the analysis of the construction specification whose deadline is approaching. As a result, by determining the priority of the analysis based on the submission time of the construction specification, it is possible to analyze the urgent construction specification with priority. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the submission time data of the construction specification to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the construction specifications during analysis. In the analysis unit, for example, in the case of important construction specifications, the AI ​​performs analysis preferentially. For example, the analysis unit evaluates the relevance of the construction specifications and prioritizes analysis of important ones. In addition, the analysis unit can also perform analysis preferentially in the case of highly relevant construction specifications. For example, the analysis unit determines the priority order based on the relevance of the construction specifications and prioritizes analysis of highly relevant ones. Furthermore, the analysis unit can also postpone analysis in the case of low-relevance construction specifications. For example, the analysis unit determines the priority order based on the relevance of the construction specifications and prioritizes analysis of low-relevance ones. As a result, by adjusting the order of analysis based on the relevance of the construction specifications, it is possible to prioritize analysis of highly relevant construction specifications. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the construction specifications to the generation AI and cause the generation AI to adjust the order of analysis.

[0087] The generation unit can estimate the user's emotion and adjust the way of expressing the answer based on the estimated emotion of the user. For example, when the user is relaxed, the generation unit generates an answer including a detailed explanation. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to provide a more appropriate answer by adjusting the way of expressing the answer according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0088] The generation unit can adjust the level of detail of the answer based on the importance of the question when generating the answer. For example, in the case of an important question, the generation unit generates a detailed answer. For example, the generation unit evaluates the importance of the question and generates a detailed answer for the important question. In addition, the generation unit can also generate a standard answer in the case of a general question. For example, the generation unit generates a standard answer based on the importance of the question. Furthermore, in the case of a simple question, the generation unit can also generate a simplified answer. For example, the generation unit generates a simplified answer based on the importance of the question. In this way, it is possible to provide an appropriate answer by adjusting the level of detail of the answer based on the importance of the question. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the importance data of the question to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0089] The generation unit can apply different generation algorithms depending on the category of the question when generating an answer. For example, in the case of a technical question, the generation unit applies a generation algorithm dedicated to technology. For example, the generation unit applies an algorithm dedicated to technology to a technical question. In addition, in the case of a question regarding a contract, the generation unit can also apply a generation algorithm dedicated to contracts. For example, the generation unit applies an algorithm dedicated to contracts to a question regarding a contract. In addition, in the case of a general question, the generation unit can also apply a generation algorithm dedicated to general questions. For example, the generation unit applies an algorithm dedicated to general questions. This improves the accuracy of the answer by applying an appropriate generation algorithm depending on the category of the question. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0090] The generation unit can estimate the user's emotion and adjust the length of the answer based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates a short answer that is to the point. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to provide a more appropriate answer by adjusting the length of the answer according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0091] The generation unit can determine the priority of the answers based on the time of submission of the question when generating the answer. In the generation unit, for example, in the case of an urgent question, the AI ​​generates the answer with priority. For example, the generation unit evaluates the time of submission of the question and answers the urgent one with priority. In addition, the generation unit can also generate an answer with standard priority in the case of a normal question. For example, the generation unit generates an answer with standard priority based on the time of submission of the question. Furthermore, the generation unit can also generate an answer with priority in the case of a question with an approaching deadline. For example, the generation unit determines the priority based on the time of submission of the question and answers the question with an approaching deadline with priority. In this way, by determining the priority of the answers based on the time of submission of the question, it is possible to answer the urgent question with priority. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the submission time data of the question to the generation AI and cause the generation AI to execute the determination of the priority of the answers.

[0092] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. In the generation unit, for example, in the case of an important question, the AI ​​generates answers preferentially. For example, the generation unit evaluates the relevance of the questions and answers the important questions preferentially. In addition, the generation unit can also generate answers preferentially in the case of a highly relevant question. For example, the generation unit determines the priority order based on the relevance of the questions and answers the highly relevant questions preferentially. Furthermore, in the case of a question with low relevance, the generation unit can also generate answers later. For example, the generation unit determines the priority order based on the relevance of the questions and answers the less relevant questions later. In this way, by adjusting the order of answers based on the relevance of the questions, it is possible to answer questions with high relevance preferentially. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the relevance data of the questions to the generation AI and cause the generation AI to adjust the order of the answers.

[0093] The voice reception unit can estimate the user's emotion and adjust the timing of receiving the voice input based on the estimated user's emotion. For example, when the user is in a hurry, the voice reception unit adjusts the timing so that the AI ​​can quickly receive the voice input. For example, the voice reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The voice reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the voice reception unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the voice reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the voice reception unit calculates an emotion score based on the fluctuation of the heart rate. As a result, the voice input can be received at a more appropriate timing by adjusting the timing of receiving the voice input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice reception unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0094] The voice reception unit can analyze the user's past voice input history at the time of voice input and select the optimal reception method. For example, the voice reception unit preferentially suggests voice commands that the user has frequently used in the past. For example, the voice reception unit analyzes the past voice input history and suggests appropriate candidates based on the voice commands that the user has used in the past. The voice reception unit can also predict and suggest a voice command to be used in a specific time period from the user's past voice input history. For example, the voice reception unit predicts a voice command to be used in a specific time period based on the past voice input history. Furthermore, the voice reception unit can preferentially suggest a voice input method (such as abbreviated commands or detailed commands) that the user has used in the past. For example, the voice reception unit suggests an appropriate voice input method based on the past voice input history. In this way, the optimal reception method can be selected by analyzing the user's past voice input history. A part or all of the above-mentioned processing in the voice reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice reception unit can input past voice input history data to a generation AI and cause the generation AI to select the optimal reception method.

[0095] The voice reception unit can estimate the user's emotion and determine the priority of the voice input based on the estimated user's emotion. For example, when the user is in a hurry, the voice reception unit preferentially receives important voice input by the AI. For example, the voice reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The voice reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the voice reception unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the voice reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the voice reception unit calculates an emotion score based on the fluctuation of the heart rate. As a result, the priority of the voice input can be determined according to the user's emotion, so that important voice input can be preferentially received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice reception unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0096] The voice reception unit can preferentially receive a highly relevant voice input in consideration of the geographical location information of the user when inputting voice. For example, when the user is in a specific area, the voice reception unit preferentially receives a voice input related to the area. For example, the voice reception unit filters the relevant voice input based on the geographical location information of the user. In addition, when the user is moving, the voice reception unit can also suggest a related voice input based on the current location. For example, the voice reception unit suggests a related voice input based on the current location of the user. Furthermore, when the user is in a specific facility, the voice reception unit can also preferentially receive a voice input related to the facility. For example, the voice reception unit filters the related voice input based on the information of the facility. As a result, by considering the geographical location information of the user, it is possible to preferentially receive a highly relevant voice input. A part or all of the above-mentioned processing in the voice reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice reception unit can input geographical location information data to the generation AI and cause the generation AI to execute filtering of the related voice input.

[0097] The learning unit can estimate the user's emotion and select learning data based on the estimated user's emotion. For example, when the user is relaxed, the learning unit selects detailed learning data. For example, the learning unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The learning unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the learning unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the learning unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the learning unit calculates an emotion score based on the fluctuation of the heart rate. As a result, more appropriate learning data can be selected by selecting learning data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0098] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit, for example, selects an optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and selects an optimal algorithm. The learning unit can also apply an algorithm that improves the efficiency of learning from the past learning data. For example, the learning unit applies an efficient algorithm based on past learning data. Furthermore, the learning unit can analyze past learning data and apply an algorithm that improves the accuracy of learning. For example, the learning unit applies a highly accurate algorithm based on past learning data. Thereby, the learning algorithm can be optimized by referring to the past learning data. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to execute optimization of the learning algorithm.

[0099] The learning unit can estimate the user's emotion and adjust the frequency of learning based on the estimated user's emotion. For example, when the user is relaxed, the learning unit performs learning frequently. For example, the learning unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The learning unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the learning unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the learning unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the learning unit calculates an emotion score based on the fluctuation of the heart rate. This allows learning to be performed at a more appropriate frequency by adjusting the frequency of learning according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0100] The learning unit can weight the learning data based on the time of question submission during learning. For example, in the case of an urgent question, the learning unit weights the learning data higher. For example, the learning unit evaluates the time of question submission and weights the urgent question higher. The learning unit can also weight normal questions. For example, the learning unit weights standard questions based on the time of question submission. Furthermore, the learning unit can weight questions with a deadline approaching higher. For example, the learning unit determines weights based on the time of question submission and weights questions with a deadline approaching higher. In this way, weighting of the learning data based on the time of question submission can be performed with priority for learning urgent questions. Part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input question submission time data to the generation AI and cause the generation AI to perform weighting of the learning data.

[0101] The diagram generating unit can estimate the user's emotion and adjust the diagram generation method based on the estimated user's emotion. For example, when the user is relaxed, the diagram generating unit generates a detailed diagram. For example, the diagram generating unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The diagram generating unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the diagram generating unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the diagram generating unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the diagram generating unit calculates an emotion score based on the fluctuation of the heart rate. This allows a more appropriate diagram to be generated by adjusting the diagram generation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the diagram generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagram generating unit may input image data of a user taken by a camera to the generating AI, and cause the generating AI to estimate the user's emotions.

[0102] The diagram generating unit can adjust the level of detail of the diagram based on the importance of the question when generating the diagram. For example, in the case of an important question, the diagram generating unit generates a detailed diagram. For example, the diagram generating unit evaluates the importance of the question and generates a detailed diagram for the important question. In addition, the diagram generating unit can also generate a standard diagram in the case of a general question. For example, the diagram generating unit generates a standard diagram based on the importance of the question. Furthermore, in the case of a simple question, the diagram generating unit can also generate a simplified diagram. For example, the diagram generating unit generates a simplified diagram based on the importance of the question. As a result, an appropriate diagram can be generated by adjusting the level of detail of the diagram based on the importance of the question. A part or all of the above-mentioned processing in the diagram generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagram generating unit can input the importance data of the question to the generating AI and cause the generating AI to adjust the level of detail of the diagram.

[0103] The diagram generating unit can estimate the user's emotion and adjust the display method of the diagram based on the estimated user's emotion. For example, when the user is nervous, the diagram generating unit provides a simple and highly visible display method. For example, the diagram generating unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The diagram generating unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the diagram generating unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the diagram generating unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the diagram generating unit calculates an emotion score based on the fluctuation of the heart rate. As a result, a highly visible display method can be provided by adjusting the display method of the diagram according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the diagram generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagram generating unit may input image data of a user taken by a camera to the generating AI, and cause the generating AI to estimate the user's emotions.

[0104] The diagram generating unit can determine the priority of the diagram based on the time of question submission when generating the diagram. In the diagram generating unit, for example, in the case of an urgent question, the AI ​​generates the diagram preferentially. For example, the diagram generating unit evaluates the time of question submission and generates a diagram preferentially for an urgent question. In addition, the diagram generating unit can also generate a diagram with a standard priority in the case of a normal question. For example, the diagram generating unit generates a diagram with a standard priority based on the time of question submission. Furthermore, the diagram generating unit can also generate a diagram preferentially in the case of a question with an approaching deadline. For example, the diagram generating unit determines the priority based on the time of question submission and generates a diagram preferentially for a question with an approaching deadline. In this way, by determining the priority of the diagram based on the time of question submission, it is possible to generate a diagram for an urgent question with priority. Part or all of the above-mentioned processing in the diagram generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagram generating unit can input question submission time data to the generating AI and cause the generating AI to execute the determination of the priority of the diagram.

[0105] The reference unit can estimate the user's emotion and select a document to be referenced based on the estimated user's emotion. For example, when the user is relaxed, the reference unit selects a detailed document. For example, the reference unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reference unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reference unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the reference unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reference unit calculates an emotion score based on the fluctuation of the heart rate. This allows the selection of a document to be referenced according to the user's emotion, thereby making it possible to select a more appropriate document. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit may input image data of a user taken by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0106] The reference unit can select the optimal document by referring to the past reference history at the time of reference. For example, the reference unit preferentially displays documents that the user frequently referred to in the past. For example, the reference unit analyzes the past reference history and displays appropriate candidates based on the documents that the user frequently referred to in the past. The reference unit can also predict and suggest documents to be used in a specific time period from the user's past reference history. For example, the reference unit predicts documents to be used in a specific time period based on the past reference history. Furthermore, the reference unit can preferentially suggest reference methods (keyword search, category search, etc.) that the user used in the past. For example, the reference unit suggests an appropriate reference method based on the past reference history. In this way, the optimal document can be selected by referring to the past reference history. A part or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input past reference history data to the generation AI and cause the generation AI to select the optimal document.

[0107] The reference unit can estimate the user's emotion and determine the priority of reference based on the estimated user's emotion. For example, when the user is in a hurry, the AI ​​preferentially refers to important documents. For example, the reference unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reference unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the reference unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the reference unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reference unit calculates an emotion score based on the fluctuation of the heart rate. As a result, important documents can be preferentially referenced by determining the priority of reference according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit may input image data of a user taken by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0108] The reference unit can weight the reference document based on the time of submission of the question at the time of reference. For example, in the case of an urgent question, the reference unit increases the weighting of the reference document. For example, the reference unit evaluates the time of submission of the question and increases the weighting for urgent questions. The reference unit can also perform standard weighting for normal questions. For example, the reference unit performs standard weighting based on the time of submission of the question. Furthermore, the reference unit can also increase the weighting for questions with a deadline approaching. For example, the reference unit determines the weighting based on the time of submission of the question and increases the weighting for questions with a deadline approaching. In this way, by weighting the reference document based on the time of submission of the question, documents for urgent questions can be preferentially referenced. Part or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input question submission time data to the generation AI and cause the generation AI to execute weighting of the reference document.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates based on the past question history. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be used in a specific time period based on the past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history.

[0111] The voice receiving unit can estimate the user's emotion and adjust the timing of receiving the voice input based on the estimated user's emotion. For example, when the user is in a hurry, the voice receiving unit can adjust the timing to quickly receive the voice input. Also, the voice receiving unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the voice receiving unit can record the user's voice and estimate the emotion using a voice analysis technology. In this way, the voice input can be received at a more appropriate timing by adjusting the timing of receiving the voice input according to the user's emotion.

[0112] The learning unit can estimate the user's emotion and select learning data based on the estimated user's emotion. For example, the learning unit can select detailed learning data when the user is relaxed. The learning unit can also capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the learning unit can also record the user's voice and estimate the emotion using voice analysis technology. This allows more appropriate learning data to be selected by selecting learning data according to the user's emotion.

[0113] The diagram generating unit can estimate the user's emotion and adjust the diagram generation method based on the estimated user's emotion. For example, the diagram generating unit can generate a detailed diagram when the user is relaxed. The diagram generating unit can also capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the diagram generating unit can record the user's voice and estimate the emotion using a voice analysis technology. This allows a more appropriate diagram to be generated by adjusting the diagram generation method according to the user's emotion.

[0114] The reference unit can estimate the user's emotion and select a document to be referenced based on the estimated user's emotion. For example, the reference unit can select a detailed document when the user is relaxed. The reference unit can also capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the reference unit can record the user's voice and estimate the emotion using voice analysis technology. This makes it possible to select a more appropriate document by selecting a document to be referenced based on the user's emotion.

[0115] The reception unit can preferentially receive highly relevant questions by taking into account the geographical location information of the user. For example, when the user is in a specific area, the reception unit can preferentially receive questions related to the area. In addition, when the user is moving, the reception unit can also suggest related questions based on the current location. Furthermore, when the user is in a specific facility, the reception unit can also preferentially receive questions related to the facility. In this way, by taking into account the geographical location information of the user, highly relevant questions can be preferentially received.

[0116] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the construction specifications. For example, the analysis unit can perform a detailed analysis for important construction specifications. Also, the analysis unit can perform a standard analysis for general construction specifications. Furthermore, the analysis unit can perform a simplified analysis for simple construction specifications. In this way, by adjusting the level of detail of the analysis based on the importance of the construction specifications, an appropriate analysis can be performed.

[0117] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit can generate a detailed answer for an important question. Also, the generation unit can generate a standard answer for a general question. Furthermore, the generation unit can generate a simplified answer for a simple question. In this way, by adjusting the level of detail of the answer based on the importance of the question, it is possible to provide an appropriate answer.

[0118] The voice reception unit can analyze the user's past voice input history at the time of voice input and select the optimal reception method. For example, the voice reception unit can preferentially suggest voice commands that the user has frequently used in the past. The voice reception unit can also predict and suggest voice commands to be used in a specific time period based on the past voice input history. Furthermore, the voice reception unit can preferentially suggest voice input methods (abbreviated commands, detailed commands, etc.) that the user has used in the past. This makes it possible to select the optimal reception method by analyzing the user's past voice input history.

[0119] At the time of reference, the reference unit can select the most suitable document by referring to the past reference history. For example, the reference unit can preferentially display documents that the user has frequently referenced in the past. The reference unit can also predict and suggest documents to be used in a specific time period based on the past reference history. Furthermore, the reference unit can preferentially suggest reference methods (keyword search, category search, etc.) that the user has used in the past. This makes it possible to select the most suitable document by referring to the past reference history.

[0120] The process flow of the second embodiment will be briefly described below.

[0121] Step 1: The reception unit receives questions from the construction company. Questions can be in text or voice format. The reception unit can also analyze the content of the questions and convert them into an appropriate format, such as converting voice questions into text format. Step 2: The analysis unit analyzes the construction specifications based on the questions received by the reception unit. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the contents of the construction specifications in detail and extract information appropriate to the questions. Step 3: The generation unit generates a response based on the information analyzed by the analysis unit. The generation unit generates a response using a text generation AI or a multimodal generation AI and provides it to the construction company. The response format includes text and audio formats.

[0122] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0124] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0125] For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives questions from the construction company. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the construction specification. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates an optimal answer. The voice reception unit is realized by, for example, the microphone 38B of the smart device 14 and receives voice input. The learning unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and learns past questions and answers. The diagram generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates answers including diagrams and tables. The reference unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and refers to related documents and databases. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0133] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0136] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0138] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0140] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0141] For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives questions from the construction company. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the construction specification. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates an optimal answer. The voice reception unit is realized by, for example, the microphone 238 of the smart glasses 214 and receives voice input. The learning unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and learns past questions and answers. The diagram generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates answers including diagrams and tables. The reference unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and refers to related documents and databases. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0152] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0156] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0157] For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives questions from the construction company. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the construction specification. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates an optimal answer. The voice reception unit is realized by, for example, the microphone 238 of the headset type terminal 314 and receives voice input. The learning unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and learns past questions and answers. The diagram generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates answers including diagrams and tables. The reference unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and refers to related documents and databases. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a 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.

[0160] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0165] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0166] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0169] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0171] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0172] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0173] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] For example, the reception unit is realized by the microphone 238 of the robot 414 and receives questions from the construction company. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the construction specification. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates an optimal answer. The voice reception unit is realized by, for example, the microphone 238 of the robot 414 and receives voice input. The learning unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and learns past questions and answers. The diagram generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates answers including diagrams and tables. The reference unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and refers to related documents and databases. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0176] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0180] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0183] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0184] In addition, 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 upon request from the data processing device 12.

[0185] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0186] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0187] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0191] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0193] (Appendix 1) a reception section for receiving inquiries regarding the installation of Internet lines; An analysis unit that analyzes the construction specification based on the question received by the reception unit; A generation unit that generates an answer based on the information analyzed by the analysis unit; Equipped A system characterized by: (Appendix 2) Equipped with a voice reception unit that receives voice input 2. The system of claim 1, (Appendix 3) Equipped with a learning section that learns past questions and answers 2. The system of claim 1, (Appendix 4) Equipped with a chart generator that generates answers including figures and tables 2. The system of claim 1, (Appendix 5) Includes references to related documents or databases 2. The system of claim 1, (Appendix 6) The reception unit is The system estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past question history and select the optimal reception method 2. The system of claim 1, (Appendix 8) The reception unit is Filter questions based on your current projects and interests 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and prioritize questions to be accepted based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When accepting questions, the system takes into account the user's geographic location information to prioritize questions that are more relevant to the user. 2. The system of claim 1, (Appendix 11) The reception unit is When a question is submitted, we analyze the user's social media activity and submit related questions. 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user sentiment and adjust analysis priorities based on the inferred user sentiment 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail based on the importance of the construction specifications 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, apply different analysis algorithms depending on the category of the construction specification 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when construction specifications are submitted 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the analysis order based on construction specification relevance 2. The system of claim 1, (Appendix 18) The generation unit is Infer user sentiment and adjust answer expression based on the inferred sentiment 2. The system of claim 1, (Appendix 19) The generation unit is When generating answers, adjust the detail of the answer based on the importance of the question 2. The system of claim 1, (Appendix 20) The generation unit is When generating answers, different generation algorithms are applied depending on the question category. 2. The system of claim 1, (Appendix 21) The generation unit is Infer user sentiment and adjust the length of answers based on the estimated user sentiment 2. The system of claim 1, (Appendix 22) The generation unit is When generating answers, prioritize answers based on when the question was submitted 2. The system of claim 1, (Appendix 23) The generation unit is During answer generation, adjust the order of answers based on the relevance of the question 2. The system of claim 1, (Appendix 24) The voice receiving unit is To estimate a user's emotion and adjust the timing of accepting voice input based on the estimated user's emotion. 3. The system of claim 2, (Appendix 25) The voice receiving unit is When entering voice input, the system analyzes the user's past voice input history and selects the optimal reception method. 3. The system of claim 2, (Appendix 26) The voice receiving unit is Estimate user emotions and prioritize voice inputs based on the estimated user emotions 3. The system of claim 2, (Appendix 27) The voice receiving unit is When inputting voice, the system takes into account the user's geographic location information and prioritizes relevant voice input. 3. The system of claim 2, (Appendix 28) The learning unit is Estimate the user's emotions and select learning data based on the estimated user emotions. 4. The system of claim 3. (Appendix 29) The learning unit is During training, the learning algorithm is optimized by referencing past training data. 4. The system of claim 3. (Appendix 30) The learning unit is Infer user emotions and adjust the frequency of learning based on the estimated user emotions. 4. The system of claim 3. (Appendix 31) The learning unit is During training, the training data is weighted based on when the question was submitted 4. The system of claim 3. (Appendix 32) The diagram generating unit is Inferring a user's emotion and adjusting the chart generation method based on the estimated user's emotion 5. The system of claim 4. (Appendix 33) The diagram generating unit is When generating a chart, adjust the chart detail based on the importance of the question 5. The system of claim 4. (Appendix 34) The diagram generating unit is Inferring a user's emotion and adjusting the display method of the chart based on the estimated user's emotion 5. The system of claim 4. (Appendix 35) The diagram generating unit is During chart generation, charts are prioritized based on when the question was submitted 5. The system of claim 4. (Appendix 36) The reference portion is Infer user sentiment and select documents to refer to based on the estimated user sentiment 6. The system of claim 5, (Appendix 37) The reference portion is When referencing, select the most appropriate document by referring to past reference history 6. The system of claim 5, (Appendix 38) The reference portion is Inferring user sentiment and determining referral priorities based on the estimated user sentiment 6. The system of claim 5, (Appendix 39) The reference portion is When referencing, weighting reference documents based on when the question was submitted 6. The system of claim 5, [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for receiving inquiries regarding the installation of Internet lines; An analysis unit that analyzes the construction specification based on the question received by the reception unit; A generation unit that generates an answer based on the information analyzed by the analysis unit; Equipped A system characterized by:

2. Equipped with a voice reception unit that receives voice input 2. The system of claim 1 .

3. Equipped with a learning section that learns past questions and answers 2. The system of claim 1 .

4. Equipped with a chart generator that generates answers including figures and tables 2. The system of claim 1 .

5. Includes references to related documents or databases 2. The system of claim 1 .

6. The reception unit is The system estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1 .

7. The reception unit is Analyze the user's past question history and select the optimal reception method 2. The system of claim 1 .

8. The reception unit is Filter questions based on your current projects and interests 2. The system of claim 1 .

9. The reception unit is Estimate the user's emotions and prioritize questions to be accepted based on the estimated user emotions.

2. The system of claim 1 .

10. The reception unit is When accepting questions, the system takes into account the user's geographic location information to prioritize questions that are more relevant to the user.

2. The system of claim 1 .

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