system

Abstract

The system according to this embodiment aims to enable the rapid acquisition of necessary information from a vast amount of manuals. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an answering unit, a generation unit, and a provisioning unit. The reception unit receives the manual as a file from the user. The analysis unit analyzes the manual entered by the reception unit. The answering unit provides the necessary knowledge in a dialogue format based on the content of the manual analyzed by the analysis unit. The generation unit automatically generates an FAQ chatbot based on the content analyzed by the analysis unit. The provisioning unit provides the FAQ chatbot generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

[0007] The system according to this embodiment can quickly retrieve necessary information from a vast amount of manuals. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The agent service system according to an embodiment of the present invention is a system for efficiently utilizing manuals. This agent service system allows users to input the manual as a file, and it provides the necessary knowledge in a conversational format. Furthermore, this agent service system automatically generates an FAQ chatbot tailored to the specific site. This eliminates reliance on veteran employees familiar with manuals, creating a world where anyone can freely use equipment from the first day of installation. Moreover, by formatting the manual from the outset with the premise of loading it into an LLM (Large-Scale Language Model) rather than simply reading existing manuals, it reduces unnecessary effort for manual creators, such as creating easy-to-read layouts and arranging detailed diagrams and charts. For example, a user inputs the manual as a file. In this case, the user only needs to upload the manual in a specific format, such as PDF or Word. This information is input into the agent service system. Next, the agent service system analyzes the input manual. The agent service system understands the content of the manual and provides the necessary knowledge in a conversational format. For example, if a user asks about a specific operation method, the agent service system provides information about that operation method. Furthermore, the agent service system automatically generates an FAQ chatbot tailored to the specific site. For example, it can generate FAQs about specific equipment and provide appropriate answers to questions about that equipment. In this way, users can quickly obtain the information they need. This system eliminates reliance on veteran employees who are familiar with manuals. For example, when new equipment is introduced, anyone can freely use it, even without veteran employees. Furthermore, by formatting the manual from the outset with the assumption that it will be read by the LLM, rather than having to read existing manuals, it reduces unnecessary work for manual creators, such as creating an easy-to-read layout and arranging detailed diagrams and charts.For example, manual creators only need to create manuals according to a specific format, eliminating the need to spend time on detailed layouts and the placement of diagrams and charts. This allows agent service systems to enable users to utilize manuals efficiently.

[0029] The agent service system according to this embodiment comprises a reception unit, an analysis unit, a response unit, a generation unit, and a provision unit. The reception unit receives manuals as files from the user. The reception unit can accept manuals in PDF or Word format, for example. The user only needs to upload the manual in a specific format. For example, they can upload a manual in PDF or Word format. This information is input into the agent service system. The analysis unit analyzes the manual input by the reception unit. The analysis unit, for example, understands the content of the input manual and provides the necessary knowledge in a conversational format. The analysis unit can analyze the content of the manual using natural language processing technology. For example, if the user asks about a specific operation method, the analysis unit provides information about that operation method. The response unit provides the necessary knowledge in a conversational format based on the content of the manual analyzed by the analysis unit. For example, if the user asks about a specific operation method, the response unit provides information about that operation method. The response unit can provide appropriate answers to user questions using natural language processing technology. The generation unit automatically generates an FAQ chatbot based on the content analyzed by the analysis unit. The generation unit can, for example, generate FAQs about a specific device and provide appropriate answers to questions about that device. The generation unit can automatically generate an FAQ chatbot using natural language processing technology. The provision unit provides the FAQ chatbot generated by the generation unit. The provision unit can, for example, provide the generated FAQ chatbot to the user. The provision unit can provide the generated FAQ chatbot through a website or application. As a result, the agent service system according to this embodiment can provide an agent service that allows users to efficiently utilize manuals.

[0030] The reception desk receives manuals as files from users. The reception desk can accept manuals in formats such as PDF or Word. Users simply upload manuals in the specified format, for example, PDF or Word. This information is entered into the agent service system. The reception desk provides an intuitive interface to make it easy for users to upload manuals. For example, it allows users to easily upload files using drag-and-drop functionality. The reception desk also automatically detects the format of uploaded files and prepares them for corresponding analysis. Furthermore, the reception desk has a function to pre-check the contents of uploaded files to ensure they are readable correctly. This allows users to immediately confirm whether their manual upload was successful. The reception desk also provides a function that allows users to upload multiple files at once. This enables users to input multiple manuals into the system simultaneously, allowing for more efficient work. Additionally, the reception desk automatically retrieves metadata from uploaded files (e.g., file name, creation date, file size, etc.) to facilitate management within the system. This allows users to easily view detailed information about uploaded files.

[0031] The analysis unit analyzes the manual entered by the reception unit. For example, the analysis unit understands the content of the entered manual and provides the necessary knowledge in a conversational format. The analysis unit can analyze the content of the manual using natural language processing technology. Specifically, the analysis unit tokenizes the text data of the manual and analyzes the meaning of each token. Furthermore, the analysis unit analyzes the structure of sentences and contextual information to understand the context and generates appropriate answers to user questions. For example, if a user asks about a specific operation method, it provides information about that operation method. The analysis unit understands the intent of the question and extracts relevant information from the manual. The analysis unit can use machine learning algorithms to learn from past question and answer data and provide more accurate answers. Furthermore, the analysis unit uses a server with high processing power to generate answers to user questions in real time. This allows users to obtain quick and accurate answers. The analysis unit can collect user feedback and continuously improve the accuracy of the analysis algorithm. For example, by having users evaluate the answers provided, the analysis unit evaluates the quality of the answers and adjusts the algorithm as needed. This allows the analysis unit to provide highly accurate analysis based on the latest information at all times, meeting the needs of users.

[0032] The answering unit provides necessary information in an interactive format based on the content of the manual analyzed by the analysis unit. For example, if a user asks about a specific operation method, the answering unit will provide information about that operation method. The answering unit can provide appropriate answers to user questions using natural language processing technology. Specifically, the answering unit analyzes the user's question and generates the optimal answer to that question. For example, if a user asks, "Please tell me how to set up this device," the answering unit will provide detailed instructions on how to set it up based on the content of the manual. The answering unit can provide answers to user questions not only in text format but also using images and videos. This allows users to obtain information in a visually easy-to-understand format. Furthermore, the answering unit can save the user's question history and provide more appropriate answers based on past questions and answers. For example, if a user asks the same question in the past, the answering unit will refer to past answers to that question and provide an answer quickly. The answering unit can collect user feedback and continuously improve the quality of its answers. For example, by having users evaluate the answers provided, the answering unit can evaluate the quality of the answers and adjust the content of the answers as needed. This allows the answering function to always provide high-quality answers based on the latest information, meeting the needs of users.

[0033] The generation unit automatically generates an FAQ chatbot based on the content analyzed by the analysis unit. For example, the generation unit generates FAQs about a specific device and provides appropriate answers to questions about that device. The generation unit can automatically generate FAQ chatbots using natural language processing technology. Specifically, the generation unit automatically generates frequently asked questions and their answers based on the content of the manual analyzed by the analysis unit. For example, it generates questions and answers regarding the setup and troubleshooting of a specific device. The generation unit tests the generated FAQ chatbot and evaluates its accuracy and usefulness. For example, it simulates questions from actual users to the generated FAQ chatbot and evaluates the accuracy of its answers. The generation unit can collect user feedback and continuously improve the accuracy of the FAQ chatbot. For example, by having users evaluate the answers they provide, the generation unit evaluates the quality of the FAQ chatbot's answers and adjusts the answer content as needed. Furthermore, the generation unit has a function to automate the updating of the FAQ chatbot. This allows the generation unit to automatically update the FAQ chatbot when a new manual is entered or an existing manual is updated, providing the latest information. This allows the generation unit to consistently provide high-quality FAQ chatbots based on the latest information, meeting user needs.

[0034] The service provider provides the FAQ chatbot generated by the generation service provider. For example, the service provider provides the generated FAQ chatbot to users. The service provider can provide the generated FAQ chatbot through websites and applications. Specifically, the service provider integrates the FAQ chatbot into the FAQ section or customer support page of a website. This allows users to use the FAQ chatbot directly on the website and quickly obtain the information they need. The service provider also integrates the FAQ chatbot into mobile applications, allowing users to access it from smartphones and tablets. This enables users to use the FAQ chatbot and obtain the information they need even when on the go. Furthermore, the service provider has the capability to support multiple platforms. For example, it can provide the FAQ chatbot through social media and messaging applications, allowing users to access it from their preferred platforms. The service provider can monitor the usage of the FAQ chatbot and improve its functionality according to user needs. For example, it can analyze the frequency of FAQ chatbot usage and user feedback, and improve the answers and interface as needed. This allows the service provider to provide users with a high-quality FAQ chatbot and improve user satisfaction.

[0035] The reception desk can input manuals in PDF or Word format. For example, the reception desk inputs manuals in PDF or Word format. The reception desk only needs users to upload manuals in a specific format. For example, users upload manuals in PDF or Word format. This information is entered into the agent service system. This allows users to input manuals in various formats. PDF and Word formats include, but are not limited to, PDF versions and Word file formats. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can use AI to convert file formats when inputting PDF or Word manuals.

[0036] The analysis unit can understand the content of the input manual and provide the necessary knowledge in a conversational format. For example, the analysis unit can understand the content of the input manual and provide the necessary knowledge in a conversational format. The analysis unit can analyze the content of the manual using natural language processing technology. For example, the analysis unit can understand the content of the manual and provide the necessary knowledge in a conversational format. For example, if a user asks about a specific operation method, the analysis unit can provide information about that operation method. This allows the user to obtain the necessary knowledge in a conversational format. Understanding the content includes, but is not limited to, natural language processing technology and machine learning algorithms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit inputs the content of the input manual into the AI, and the AI ​​understands the content and provides the necessary knowledge in a conversational format.

[0037] The generation unit can generate FAQs about specific devices. For example, the generation unit generates FAQs about specific devices. The generation unit can automatically generate an FAQ chatbot using natural language processing technology. For example, the generation unit generates FAQs about specific devices and provides appropriate answers to questions about those devices. This enables the automatic generation of FAQs about specific devices. Specific devices include, but are not limited to, home appliances and industrial equipment. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs information about specific devices into the AI, and the AI ​​generates the FAQs.

[0038] The service provider can provide the generated FAQ chatbot. The service provider can, for example, provide the generated FAQ chatbot. The service provider can provide the generated FAQ chatbot through a website or application. For example, the service provider provides the generated FAQ chatbot to a user, thereby enabling the user to use the generated FAQ chatbot. Provision includes, but is not limited to, provision on a website or provision in an application. Some or all of the processing described above in the service provider may be performed, for example, using AI or not using AI. For example, the service provider provides the generated FAQ chatbot to a user using AI.

[0039] The reception desk can analyze the user's past manual input history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods that the user has frequently used in the past. For example, the reception desk may select the most efficient input method from the user's past input history. For example, the reception desk may analyze the user's input history and suggest input methods based on specific patterns. This allows the reception desk to select the optimal input method based on the user's past input history. Analyzing input history includes, but is not limited to, methods for analyzing past input data. Selecting the optimal input method includes, but is not limited to, methods based on the user's operation history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk may input the user's past input history into AI, and the AI ​​may select the optimal input method.

[0040] The reception unit can filter manuals based on the user's current projects and areas of interest when they are entered. For example, the reception unit prioritizes manuals related to the project the user is currently working on. For example, the reception unit filters and enters relevant manuals based on the user's areas of interest. For example, the reception unit selects and enters necessary manuals according to the user's project progress. This allows manuals to be filtered based on the user's projects and areas of interest. Projects and areas of interest include, but are not limited to, the user's project information and area of ​​interest data. Filtering includes, but is not limited to, methods for extracting highly relevant information. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit enters the user's project information into AI, and the AI ​​filters the relevant manuals.

[0041] The reception desk can prioritize inputting manuals that are highly relevant to the user, taking into account the user's geographical location information when inputting manuals. For example, if the user is in a specific region, the reception desk will prioritize inputting manuals related to that region. For example, the reception desk will filter and input relevant manuals based on the user's location information. For example, if the user is on the move, the reception desk will prioritize inputting manuals related to the user's current location. This allows for the priority input of highly relevant manuals based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Highly relevant manuals include, but are not limited to, methods for selecting manuals based on geographical relevance. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk inputs the user's geographical location information into the AI, and the AI ​​prioritizes inputting relevant manuals.

[0042] The reception desk can analyze a user's social media activity and input relevant manuals when entering manuals. For example, the reception desk can input relevant manuals based on the content of the user's social media posts. For example, the reception desk can suggest relevant manuals based on the activity of the user's followers and friends. For example, the reception desk can prioritize inputting relevant manuals based on the user's social media interests. This allows for the input of relevant manuals based on the user's social media activity. Social media activity includes, but is not limited to, analysis of post content and use of activity history. Relevant manuals include, but are not limited to, methods for selecting manuals based on social media content. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk inputs the user's social media activity into AI, and the AI ​​inputs relevant manuals.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the manual during the analysis. For example, the analysis unit performs a detailed analysis for manuals of high importance. For example, the analysis unit performs a simplified analysis for manuals of low importance. The analysis unit adjusts the depth of the analysis according to the importance of the manual. This allows the level of detail of the analysis to be adjusted according to the importance of the manual. Importance includes, but is not limited to, an evaluation of the importance of the manual's content. Adjusting the level of detail includes, but is not limited to, changing the level of detail of the analysis according to the importance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit inputs the importance of the manual into the AI, and the AI ​​adjusts the level of detail of the analysis based on the importance.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the manual during analysis. For example, the analysis unit applies a technical analysis algorithm to a technical manual. For example, the analysis unit applies an analysis algorithm specialized in operating procedures to an operation manual. For example, the analysis unit applies an analysis algorithm specialized in problem solving to a troubleshooting manual. This allows the application of an appropriate analysis algorithm depending on the category of the manual. Categories include, but are not limited to, categorization according to the type of manual. Analysis algorithms include, but are not limited to, text analysis algorithms and machine learning algorithms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit inputs the manual category into the AI, and the AI ​​applies a different analysis algorithm depending on the category.

[0045] The analysis unit can determine the priority of analysis based on the creation date of the manuals during the analysis. For example, the analysis unit will prioritize the analysis of the most recent manuals. For example, the analysis unit will perform analysis on older manuals as needed. The analysis unit will adjust the priority of analysis according to the creation date of the manuals. This allows the priority of analysis to be determined according to the creation date of the manuals. The creation date includes, but is not limited to, the manual's creation date and update history. Determining the priority includes, but is not limited to, a method for determining priority based on the creation date. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit inputs the manual's creation date into the AI, and the AI ​​determines the priority of analysis based on the creation date.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the manuals during the analysis. For example, the analysis unit prioritizes analyzing the manuals most relevant to the user's question. For example, the analysis unit postpones analyzing less relevant manuals. The analysis unit adjusts the order of analysis according to the relevance of the manuals. This allows the order of analysis to be adjusted according to the relevance of the manuals. Relevance includes, but is not limited to, content relevance and usage relevance. Adjusting the order includes, but is not limited to, changing the order of analysis according to relevance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit inputs the relevance of the manuals into the AI, and the AI ​​adjusts the order of analysis based on the relevance.

[0047] The answering unit can adjust the level of detail in its response based on the importance of the question. For example, the answering unit provides a detailed response to a high-importance question. For example, the answering unit provides a simplified response to a low-importance question. The answering unit adjusts the depth of its response according to the importance of the question. This allows the level of detail in the response to be adjusted according to the importance of the question. Importance includes, but is not limited to, an evaluation of the importance of the question's content. Adjusting the level of detail includes, but is not limited to, changing the level of detail in the response according to the importance. Some or all of the above processing in the answering unit may be performed using, for example, AI, or not using AI. For example, the answering unit inputs the importance of the question into the AI, and the AI ​​adjusts the level of detail in the response based on the importance.

[0048] The answering unit can apply different answering algorithms depending on the category of the question when providing an answer. For example, the answering unit applies a technical answering algorithm to technical questions. For example, the answering unit applies an answering algorithm specialized in operating procedures to questions about how to operate something. For example, the answering unit applies an answering algorithm specialized in problem solving to questions about troubleshooting. This allows the appropriate answering algorithm to be applied depending on the category of the question. Categories include, but are not limited to, categorization based on the type of question. Answering algorithms include, but are not limited to, text generation algorithms and machine learning algorithms. Some or all of the above processing in the answering unit may be performed using, for example, AI, or not using AI. For example, the answering unit inputs the category of the question into the AI, and the AI ​​applies a different answering algorithm depending on the category.

[0049] The answering unit can determine the priority of answers based on when the questions were submitted. For example, the answering unit will prioritize answers to the most recent questions. For example, the answering unit will answer older questions as needed. The answering unit can adjust the priority of answers according to when the questions were submitted. This allows the priority of answers to be determined according to when the questions were submitted. The submission date includes, but is not limited to, the date and time of submission of the question. Determining the priority includes, but is not limited to, a method for determining priority based on the submission date. Some or all of the above processing in the answering unit may be performed using, for example, AI, or not using AI. For example, the answering unit inputs the submission date of the question into the AI, and the AI ​​determines the priority of answers based on the submission date.

[0050] The answering unit can adjust the order of answers based on the relevance of the questions when providing answers. For example, the answering unit prioritizes providing the answer most relevant to the user's question. For example, the answering unit postpones questions that are less relevant. For example, the answering unit adjusts the order of answers according to the relevance of the questions. This allows the order of answers to be adjusted according to the relevance of the questions. Relevance includes, but is not limited to, the relevance of the question content or the relevance of usage. Adjusting the order includes, but is not limited to, changing the order of answers according to relevance. Some or all of the above processing in the answering unit may be performed using, for example, AI, or not using AI. For example, the answering unit inputs the relevance of the questions into the AI, and the AI ​​adjusts the order of answers based on the relevance.

[0051] The generation unit can improve the accuracy of FAQ generation by considering the interrelationships between manuals. For example, the generation unit improves the accuracy of FAQs by relating the contents of manuals to each other. For example, the generation unit generates relevant FAQs by considering the interrelationships between manuals. For example, the generation unit generates comprehensive FAQs by integrating the contents of manuals. This allows for improved accuracy of FAQs by considering the interrelationships between manuals. Interrelationships include, but are not limited to, the relationships between manuals and methods of cross-referencing. Accuracy of generation includes, but are not limited to, methods for improving the accuracy of FAQ generation based on interrelationships. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit inputs the interrelationships between manuals into the AI, and the AI ​​generates FAQs considering the interrelationships.

[0052] The generation unit can generate FAQs while considering the attribute information of the manual's creator. For example, if the manual's creator is a technician, the generation unit will generate technical FAQs. For example, if the manual's creator is a sales representative, the generation unit will generate FAQs related to sales. For example, the generation unit will generate relevant FAQs based on the manual's area of ​​expertise. This allows for the generation of appropriate FAQs by considering the manual's attribute information. Attribute information includes, but is not limited to, the creator's area of ​​expertise and years of experience. Generation includes, but is not limited to, methods for generating FAQs based on attribute information. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the manual's creator's attribute information into the AI, and the AI ​​generates FAQs while considering the attribute information.

[0053] The generation unit can perform FAQ generation while considering the geographical distribution of manuals. For example, the generation unit can prioritize the generation of FAQs related to a specific region. For example, the generation unit can generate relevant FAQs based on geographical distribution. For example, the generation unit can generate optimal FAQs while considering the characteristics of each region. This makes it possible to generate appropriate FAQs by considering the geographical distribution of manuals. Geographical distribution includes, but is not limited to, the distribution of manuals by region, geographical relevance, etc. Generation includes, but is not limited to, methods for generating FAQs based on geographical distribution, etc. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the geographical distribution of manuals into AI, and the AI ​​generates FAQs while considering the geographical distribution.

[0054] The generation unit can improve the accuracy of FAQ generation by referring to relevant documents in the manual. For example, the generation unit improves the accuracy of the FAQ by referring to relevant documents. For example, the generation unit integrates the contents of the manual with relevant documents to generate a comprehensive FAQ. For example, the generation unit generates a detailed FAQ based on relevant documents. This allows the accuracy of the FAQ to be improved by referring to relevant documents in the manual. Relevant documents include, but are not limited to, documents and reference materials related to the contents of the manual. Accuracy of generation includes, but are not limited to, methods for improving the accuracy of FAQ generation based on relevant documents. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the relevant documents in the manual into the AI, and the AI ​​generates the FAQ by referring to the relevant documents.

[0055] The service provider can select the optimal display method by referring to the user's past operation history when providing FAQs. For example, the service provider provides the optimal display method based on the user's past operation history. For example, the service provider prioritizes providing display methods that the user has used in the past. For example, the service provider analyzes the user's operation history to select the optimal display method. This allows the service provider to select the optimal display method based on the user's past operation history. Operation history includes, but is not limited to, the user's past operation data and usage history. Display methods include, but are not limited to, methods for selecting a display method based on past operation history. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider inputs the user's past operation history into AI, and the AI ​​analyzes the operation history to select the optimal display method.

[0056] The service provider can select the optimal display method when providing FAQs, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider will provide a concise and highly visible display method. This allows the service provider to select the optimal display method based on the user's device information. Device information includes, but is not limited to, the type of device the user is using and the screen size. Display methods include, but are not limited to, methods for selecting a display method based on device information. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider may input the user's device information into the AI, and the AI ​​may select the optimal display method taking the device information into consideration.

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

[0058] The reception unit may have a function to automatically summarize the content of a manual entered by the user. For example, the reception unit can summarize a long manual into a short one, allowing the user to quickly obtain the information they need. The reception unit can also present the summarized content to the user and allow the user to access the original manual if they wish to check the details. This allows the user to efficiently obtain the information they need. The summarization may include, but is not limited to, summarization algorithms using natural language processing technology. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI.

[0059] The analysis unit can automatically search for relevant video tutorials based on the content of the manual when analyzing the input manual's contents and provide them to the user. For example, the analysis unit can search for video tutorials on specific operating procedures, enabling the user to understand them visually. The analysis unit can also provide the user with links to the video tutorials, allowing the user to visually confirm the necessary information. This makes it easier for the user to understand how to operate the system. Searching for video tutorials includes, but is not limited to, using video platforms on the internet. Some or all of the processing described above in the analysis unit may be performed using AI, for example, or without AI.

[0060] When generating an FAQ chatbot, the generation unit can analyze the user's past question history and prioritize including the most frequently asked questions in the FAQ. For example, if the generation unit finds that there are many questions about a particular operation method based on the past question history, it will prioritize generating an FAQ about that operation method. The generation unit also analyzes the user's question history and enriches the answers to frequently asked questions. This allows users to quickly obtain the information they need. The analysis of the question history includes, but is not limited to, methods using data mining techniques. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI.

[0061] The service provider can dynamically change the display order of FAQs based on user usage when providing the generated FAQ chatbot. For example, if a user frequently refers to an FAQ about a particular operation, the service provider will display that FAQ at the top. The service provider also monitors user usage in real time and prioritizes displaying the most relevant FAQs. This allows users to quickly obtain the information they need. Monitoring usage includes, but is not limited to, methods such as analyzing user operation logs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0062] The reception desk may have a function to automatically translate the content of manuals entered by users. For example, the reception desk could translate a manual entered in English into Japanese, making it easier for Japanese-speaking users to understand. The reception desk could also present the translated content to the user, allowing the user to revert to the original language if necessary. This would allow users to obtain the necessary information over language barriers. Translation methods include, but are not limited to, using machine translation technology. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives the manual as a file from the user. The reception desk can accept manuals in formats such as PDF or Word. Users only need to upload the manual in their preferred format. Step 2: The analysis unit analyzes the manual entered by the reception unit. The analysis unit uses natural language processing technology to understand the content of the manual and provides the necessary information in a conversational format. Step 3: The answering unit provides the necessary knowledge in an interactive format based on the content of the manual analyzed by the analysis unit. The answering unit uses natural language processing technology to provide appropriate answers to the user's questions. Step 4: The generation unit automatically generates an FAQ chatbot based on the content analyzed by the analysis unit. The generation unit uses natural language processing technology to generate FAQs about a specific device and provides appropriate answers to questions about that device. Step 5: The provider unit provides the FAQ chatbot generated by the generator unit. The provider unit provides the generated FAQ chatbot to users through a website or application.

[0065] (Example of form 2) The agent service system according to an embodiment of the present invention is a system for efficiently utilizing manuals. This agent service system allows users to input the manual as a file, and it provides the necessary knowledge in a conversational format. Furthermore, this agent service system automatically generates an FAQ chatbot tailored to the specific site. This eliminates reliance on veteran employees familiar with manuals, creating a world where anyone can freely use equipment from the first day of installation. Moreover, by formatting the manual from the outset with the premise of loading it into an LLM (Large-Scale Language Model) rather than simply reading existing manuals, it reduces unnecessary effort for manual creators, such as creating easy-to-read layouts and arranging detailed diagrams and charts. For example, a user inputs the manual as a file. In this case, the user only needs to upload the manual in a specific format, such as PDF or Word. This information is input into the agent service system. Next, the agent service system analyzes the input manual. The agent service system understands the content of the manual and provides the necessary knowledge in a conversational format. For example, if a user asks about a specific operation method, the agent service system provides information about that operation method. Furthermore, the agent service system automatically generates an FAQ chatbot tailored to the specific site. For example, it can generate FAQs about specific equipment and provide appropriate answers to questions about that equipment. In this way, users can quickly obtain the information they need. This system eliminates reliance on veteran employees who are familiar with manuals. For example, when new equipment is introduced, anyone can freely use it, even without veteran employees. Furthermore, by formatting the manual from the outset with the assumption that it will be read by the LLM, rather than having to read existing manuals, it reduces unnecessary work for manual creators, such as creating an easy-to-read layout and arranging detailed diagrams and charts.For example, manual creators only need to create manuals according to a specific format, eliminating the need to spend time on detailed layouts and the placement of diagrams and charts. This allows agent service systems to enable users to utilize manuals efficiently.

[0066] The agent service system according to this embodiment comprises a reception unit, an analysis unit, a response unit, a generation unit, and a provision unit. The reception unit receives manuals as files from the user. The reception unit can accept manuals in PDF or Word format, for example. The user only needs to upload the manual in a specific format. For example, they can upload a manual in PDF or Word format. This information is input into the agent service system. The analysis unit analyzes the manual input by the reception unit. The analysis unit, for example, understands the content of the input manual and provides the necessary knowledge in a conversational format. The analysis unit can analyze the content of the manual using natural language processing technology. For example, if the user asks about a specific operation method, the analysis unit provides information about that operation method. The response unit provides the necessary knowledge in a conversational format based on the content of the manual analyzed by the analysis unit. For example, if the user asks about a specific operation method, the response unit provides information about that operation method. The response unit can provide appropriate answers to user questions using natural language processing technology. The generation unit automatically generates an FAQ chatbot based on the content analyzed by the analysis unit. The generation unit can, for example, generate FAQs about a specific device and provide appropriate answers to questions about that device. The generation unit can automatically generate an FAQ chatbot using natural language processing technology. The provision unit provides the FAQ chatbot generated by the generation unit. The provision unit can, for example, provide the generated FAQ chatbot to the user. The provision unit can provide the generated FAQ chatbot through a website or application. As a result, the agent service system according to this embodiment can provide an agent service that allows users to efficiently utilize manuals.

[0067] The reception desk receives manuals as files from users. The reception desk can accept manuals in formats such as PDF or Word. Users simply upload manuals in the specified format, for example, PDF or Word. This information is entered into the agent service system. The reception desk provides an intuitive interface to make it easy for users to upload manuals. For example, it allows users to easily upload files using drag-and-drop functionality. The reception desk also automatically detects the format of uploaded files and prepares them for corresponding analysis. Furthermore, the reception desk has a function to pre-check the contents of uploaded files to ensure they are readable correctly. This allows users to immediately confirm whether their manual upload was successful. The reception desk also provides a function that allows users to upload multiple files at once. This enables users to input multiple manuals into the system simultaneously, allowing for more efficient work. Additionally, the reception desk automatically retrieves metadata from uploaded files (e.g., file name, creation date, file size, etc.) to facilitate management within the system. This allows users to easily view detailed information about uploaded files.

[0068] The analysis unit analyzes the manual entered by the reception unit. For example, the analysis unit understands the content of the entered manual and provides the necessary knowledge in a conversational format. The analysis unit can analyze the content of the manual using natural language processing technology. Specifically, the analysis unit tokenizes the text data of the manual and analyzes the meaning of each token. Furthermore, the analysis unit analyzes the structure of sentences and contextual information to understand the context and generates appropriate answers to user questions. For example, if a user asks about a specific operation method, it provides information about that operation method. The analysis unit understands the intent of the question and extracts relevant information from the manual. The analysis unit can use machine learning algorithms to learn from past question and answer data and provide more accurate answers. Furthermore, the analysis unit uses a server with high processing power to generate answers to user questions in real time. This allows users to obtain quick and accurate answers. The analysis unit can collect user feedback and continuously improve the accuracy of the analysis algorithm. For example, by having users evaluate the answers provided, the analysis unit evaluates the quality of the answers and adjusts the algorithm as needed. This allows the analysis unit to provide highly accurate analysis based on the latest information at all times, meeting the needs of users.

[0069] The answering unit provides necessary information in an interactive format based on the content of the manual analyzed by the analysis unit. For example, if a user asks about a specific operation method, the answering unit will provide information about that operation method. The answering unit can provide appropriate answers to user questions using natural language processing technology. Specifically, the answering unit analyzes the user's question and generates the optimal answer to that question. For example, if a user asks, "Please tell me how to set up this device," the answering unit will provide detailed instructions on how to set it up based on the content of the manual. The answering unit can provide answers to user questions not only in text format but also using images and videos. This allows users to obtain information in a visually easy-to-understand format. Furthermore, the answering unit can save the user's question history and provide more appropriate answers based on past questions and answers. For example, if a user asks the same question in the past, the answering unit will refer to past answers to that question and provide an answer quickly. The answering unit can collect user feedback and continuously improve the quality of its answers. For example, by having users evaluate the answers provided, the answering unit can evaluate the quality of the answers and adjust the content of the answers as needed. This allows the answering function to always provide high-quality answers based on the latest information, meeting the needs of users.

[0070] The generation unit automatically generates an FAQ chatbot based on the content analyzed by the analysis unit. For example, the generation unit generates FAQs about a specific device and provides appropriate answers to questions about that device. The generation unit can automatically generate FAQ chatbots using natural language processing technology. Specifically, the generation unit automatically generates frequently asked questions and their answers based on the content of the manual analyzed by the analysis unit. For example, it generates questions and answers regarding the setup and troubleshooting of a specific device. The generation unit tests the generated FAQ chatbot and evaluates its accuracy and usefulness. For example, it simulates questions from actual users to the generated FAQ chatbot and evaluates the accuracy of its answers. The generation unit can collect user feedback and continuously improve the accuracy of the FAQ chatbot. For example, by having users evaluate the answers they provide, the generation unit evaluates the quality of the FAQ chatbot's answers and adjusts the answer content as needed. Furthermore, the generation unit has a function to automate the updating of the FAQ chatbot. This allows the generation unit to automatically update the FAQ chatbot when a new manual is entered or an existing manual is updated, providing the latest information. This allows the generation unit to consistently provide high-quality FAQ chatbots based on the latest information, meeting user needs.

[0071] The service provider provides the FAQ chatbot generated by the generation service provider. For example, the service provider provides the generated FAQ chatbot to users. The service provider can provide the generated FAQ chatbot through websites and applications. Specifically, the service provider integrates the FAQ chatbot into the FAQ section or customer support page of a website. This allows users to use the FAQ chatbot directly on the website and quickly obtain the information they need. The service provider also integrates the FAQ chatbot into mobile applications, allowing users to access it from smartphones and tablets. This enables users to use the FAQ chatbot and obtain the information they need even when on the go. Furthermore, the service provider has the capability to support multiple platforms. For example, it can provide the FAQ chatbot through social media and messaging applications, allowing users to access it from their preferred platforms. The service provider can monitor the usage of the FAQ chatbot and improve its functionality according to user needs. For example, it can analyze the frequency of FAQ chatbot usage and user feedback, and improve the answers and interface as needed. This allows the service provider to provide users with a high-quality FAQ chatbot and improve user satisfaction.

[0072] The reception desk can input manuals in PDF or Word format. For example, the reception desk inputs manuals in PDF or Word format. The reception desk only needs users to upload manuals in a specific format. For example, users upload manuals in PDF or Word format. This information is entered into the agent service system. This allows users to input manuals in various formats. PDF and Word formats include, but are not limited to, PDF versions and Word file formats. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can use AI to convert file formats when inputting PDF or Word manuals.

[0073] The analysis unit can understand the content of the input manual and provide the necessary knowledge in a conversational format. For example, the analysis unit can understand the content of the input manual and provide the necessary knowledge in a conversational format. The analysis unit can analyze the content of the manual using natural language processing technology. For example, the analysis unit can understand the content of the manual and provide the necessary knowledge in a conversational format. For example, if a user asks about a specific operation method, the analysis unit can provide information about that operation method. This allows the user to obtain the necessary knowledge in a conversational format. Understanding the content includes, but is not limited to, natural language processing technology and machine learning algorithms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit inputs the content of the input manual into the AI, and the AI ​​understands the content and provides the necessary knowledge in a conversational format.

[0074] The generation unit can generate FAQs about specific devices. For example, the generation unit generates FAQs about specific devices. The generation unit can automatically generate an FAQ chatbot using natural language processing technology. For example, the generation unit generates FAQs about specific devices and provides appropriate answers to questions about those devices. This enables the automatic generation of FAQs about specific devices. Specific devices include, but are not limited to, home appliances and industrial equipment. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs information about specific devices into the AI, and the AI ​​generates the FAQs.

[0075] The service provider can provide the generated FAQ chatbot. The service provider can, for example, provide the generated FAQ chatbot. The service provider can provide the generated FAQ chatbot through a website or application. For example, the service provider provides the generated FAQ chatbot to a user, thereby enabling the user to use the generated FAQ chatbot. Provision includes, but is not limited to, provision on a website or provision in an application. Some or all of the processing described above in the service provider may be performed, for example, using AI or not using AI. For example, the service provider provides the generated FAQ chatbot to a user using AI.

[0076] The reception desk can estimate the user's emotions and adjust the timing of manual input based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the input timing to help them relax. For example, if the user is in a hurry, the reception desk can speed up the input timing to respond quickly. For example, if the user is concentrating, the reception desk can optimize the input timing for efficient input. This allows the timing of manual input to be adjusted according to the user's emotions. Estimation of emotions includes, but is not limited to, facial recognition and voice analysis. Adjusting the input timing includes, but is not limited to, timing adjustments according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk inputs the user's emotions into the AI, and the AI ​​estimates the emotions and adjusts the input timing.

[0077] The reception desk can analyze the user's past manual input history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods that the user has frequently used in the past. For example, the reception desk may select the most efficient input method from the user's past input history. For example, the reception desk may analyze the user's input history and suggest input methods based on specific patterns. This allows the reception desk to select the optimal input method based on the user's past input history. Analyzing input history includes, but is not limited to, methods for analyzing past input data. Selecting the optimal input method includes, but is not limited to, methods based on the user's operation history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk may input the user's past input history into AI, and the AI ​​may select the optimal input method.

[0078] The reception unit can filter manuals based on the user's current projects and areas of interest when they are entered. For example, the reception unit prioritizes manuals related to the project the user is currently working on. For example, the reception unit filters and enters relevant manuals based on the user's areas of interest. For example, the reception unit selects and enters necessary manuals according to the user's project progress. This allows manuals to be filtered based on the user's projects and areas of interest. Projects and areas of interest include, but are not limited to, the user's project information and area of ​​interest data. Filtering includes, but is not limited to, methods for extracting highly relevant information. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit enters the user's project information into AI, and the AI ​​filters the relevant manuals.

[0079] The reception desk can estimate the user's emotions and determine the priority of manuals to input based on the estimated emotions. For example, if the user is stressed, the reception desk will postpone less important manuals. For example, if the user is relaxed, the reception desk will prioritize inputting more important manuals. For example, if the user is in a hurry, the reception desk will quickly input the most necessary manuals. This allows the reception desk to determine the priority of manuals to input according to the user's emotions. Estimation of emotions includes, but is not limited to, facial recognition and voice analysis. Determining priorities includes, but is not limited to, methods for determining priorities based on importance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk inputs the user's emotions into the AI, which then estimates the emotions and determines the priority of the manual to input.

[0080] The reception desk can prioritize inputting manuals that are highly relevant to the user, taking into account the user's geographical location information when inputting manuals. For example, if the user is in a specific region, the reception desk will prioritize inputting manuals related to that region. For example, the reception desk will filter and input relevant manuals based on the user's location information. For example, if the user is on the move, the reception desk will prioritize inputting manuals related to the user's current location. This allows for the priority input of highly relevant manuals based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Highly relevant manuals include, but are not limited to, methods for selecting manuals based on geographical relevance. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk inputs the user's geographical location information into the AI, and the AI ​​prioritizes inputting relevant manuals.

[0081] The reception desk can analyze a user's social media activity and input relevant manuals when entering manuals. For example, the reception desk can input relevant manuals based on the content of the user's social media posts. For example, the reception desk can suggest relevant manuals based on the activity of the user's followers and friends. For example, the reception desk can prioritize inputting relevant manuals based on the user's social media interests. This allows for the input of relevant manuals based on the user's social media activity. Social media activity includes, but is not limited to, analysis of post content and use of activity history. Relevant manuals include, but are not limited to, methods for selecting manuals based on social media content. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk inputs the user's social media activity into AI, and the AI ​​inputs relevant manuals.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit uses a simple presentation. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Examples of emotion estimation include, but are not limited to, facial recognition and voice analysis. Examples of adjusting the presentation include, but are not limited to, changing the presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit inputs the user's emotions into the AI, and the AI ​​estimates the emotions and adjusts the presentation of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the manual during the analysis. For example, the analysis unit performs a detailed analysis for manuals of high importance. For example, the analysis unit performs a simplified analysis for manuals of low importance. The analysis unit adjusts the depth of the analysis according to the importance of the manual. This allows the level of detail of the analysis to be adjusted according to the importance of the manual. Importance includes, but is not limited to, an evaluation of the importance of the manual's content. Adjusting the level of detail includes, but is not limited to, changing the level of detail of the analysis according to the importance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit inputs the importance of the manual into the AI, and the AI ​​adjusts the level of detail of the analysis based on the importance.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the manual during analysis. For example, the analysis unit applies a technical analysis algorithm to a technical manual. For example, the analysis unit applies an analysis algorithm specialized in operating procedures to an operation manual. For example, the analysis unit applies an analysis algorithm specialized in problem solving to a troubleshooting manual. This allows the application of an appropriate analysis algorithm depending on the category of the manual. Categories include, but are not limited to, categorization according to the type of manual. Analysis algorithms include, but are not limited to, text analysis algorithms and machine learning algorithms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit inputs the manual category into the AI, and the AI ​​applies a different analysis algorithm depending on the category.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. Examples of emotion estimation include, but are not limited to, facial recognition and voice analysis. Examples of length adjustment include, but are not limited to, changing the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI examples include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit inputs the user's emotions into the AI, and the AI ​​estimates the emotions and adjusts the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on the creation date of the manuals during the analysis. For example, the analysis unit will prioritize the analysis of the most recent manuals. For example, the analysis unit will perform analysis on older manuals as needed. The analysis unit will adjust the priority of analysis according to the creation date of the manuals. This allows the priority of analysis to be determined according to the creation date of the manuals. The creation date includes, but is not limited to, the manual's creation date and update history. Determining the priority includes, but is not limited to, a method for determining priority based on the creation date. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit inputs the manual's creation date into the AI, and the AI ​​determines the priority of analysis based on the creation date.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the manuals during the analysis. For example, the analysis unit prioritizes analyzing the manuals most relevant to the user's question. For example, the analysis unit postpones analyzing less relevant manuals. The analysis unit adjusts the order of analysis according to the relevance of the manuals. This allows the order of analysis to be adjusted according to the relevance of the manuals. Relevance includes, but is not limited to, content relevance and usage relevance. Adjusting the order includes, but is not limited to, changing the order of analysis according to relevance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit inputs the relevance of the manuals into the AI, and the AI ​​adjusts the order of analysis based on the relevance.

[0088] The response unit can estimate the user's emotions and adjust the way it expresses its response based on those emotions. For example, if the user is stressed, the response unit will use a simple and easy-to-understand expression. For example, if the user is relaxed, the response unit will provide a detailed response. For example, if the user is in a hurry, the response unit will provide a concise response. This allows the response to be expressed in accordance with the user's emotions. Emotion estimation includes, but is not limited to, facial recognition and speech analysis. Adjusting the expression includes, but is not limited to, changing the way the response is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not using AI. For example, the response unit inputs the user's emotions into the AI, which estimates the emotions and adjusts the way the response is expressed.

[0089] The answering unit can adjust the level of detail in its response based on the importance of the question. For example, the answering unit provides a detailed response to a high-importance question. For example, the answering unit provides a simplified response to a low-importance question. The answering unit adjusts the depth of its response according to the importance of the question. This allows the level of detail in the response to be adjusted according to the importance of the question. Importance includes, but is not limited to, an evaluation of the importance of the question's content. Adjusting the level of detail includes, but is not limited to, changing the level of detail in the response according to the importance. Some or all of the above processing in the answering unit may be performed using, for example, AI, or not using AI. For example, the answering unit inputs the importance of the question into the AI, and the AI ​​adjusts the level of detail in the response based on the importance.

[0090] The answering unit can apply different answering algorithms depending on the category of the question when providing an answer. For example, the answering unit applies a technical answering algorithm to technical questions. For example, the answering unit applies an answering algorithm specialized in operating procedures to questions about how to operate something. For example, the answering unit applies an answering algorithm specialized in problem solving to questions about troubleshooting. This allows the appropriate answering algorithm to be applied depending on the category of the question. Categories include, but are not limited to, categorization based on the type of question. Answering algorithms include, but are not limited to, text generation algorithms and machine learning algorithms. Some or all of the above processing in the answering unit may be performed using, for example, AI, or not using AI. For example, the answering unit inputs the category of the question into the AI, and the AI ​​applies a different answering algorithm depending on the category.

[0091] The response unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the response unit will provide a short, to-the-point response. For example, if the user is relaxed, the response unit will provide a detailed response. For example, if the user is excited, the response unit will provide a visually stimulating response. This allows the response length to be adjusted according to the user's emotions. Estimation of emotions includes, but is not limited to, facial recognition and speech analysis. Adjusting the length includes, but is not limited to, changing the length of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not using AI. For example, the response unit inputs the user's emotions into the AI, and the AI ​​estimates the emotions and adjusts the length of the response.

[0092] The answering unit can determine the priority of answers based on when the questions were submitted. For example, the answering unit will prioritize answers to the most recent questions. For example, the answering unit will answer older questions as needed. The answering unit can adjust the priority of answers according to when the questions were submitted. This allows the priority of answers to be determined according to when the questions were submitted. The submission date includes, but is not limited to, the date and time of submission of the question. Determining the priority includes, but is not limited to, a method for determining priority based on the submission date. Some or all of the above processing in the answering unit may be performed using, for example, AI, or not using AI. For example, the answering unit inputs the submission date of the question into the AI, and the AI ​​determines the priority of answers based on the submission date.

[0093] The answering unit can adjust the order of answers based on the relevance of the questions when providing answers. For example, the answering unit prioritizes providing the answer most relevant to the user's question. For example, the answering unit postpones questions that are less relevant. For example, the answering unit adjusts the order of answers according to the relevance of the questions. This allows the order of answers to be adjusted according to the relevance of the questions. Relevance includes, but is not limited to, the relevance of the question content or the relevance of usage. Adjusting the order includes, but is not limited to, changing the order of answers according to relevance. Some or all of the above processing in the answering unit may be performed using, for example, AI, or not using AI. For example, the answering unit inputs the relevance of the questions into the AI, and the AI ​​adjusts the order of answers based on the relevance.

[0094] The generation unit can estimate the user's emotions and determine the priority of the FAQs to be generated based on the estimated user emotions. For example, if the user is stressed, the generation unit will postpone generating less important FAQs. For example, if the user is relaxed, the generation unit will prioritize generating more important FAQs. For example, if the user is in a hurry, the generation unit will quickly generate the most necessary FAQs. This allows the generation unit to determine the priority of FAQs according to the user's emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Prioritization includes, but is not limited to, methods for determining the priority of FAQs based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the user's emotions into the AI, which then estimates the emotions and determines the priority of the FAQs.

[0095] The generation unit can improve the accuracy of FAQ generation by considering the interrelationships between manuals. For example, the generation unit improves the accuracy of FAQs by relating the contents of manuals to each other. For example, the generation unit generates relevant FAQs by considering the interrelationships between manuals. For example, the generation unit generates comprehensive FAQs by integrating the contents of manuals. This allows for improved accuracy of FAQs by considering the interrelationships between manuals. Interrelationships include, but are not limited to, the relationships between manuals and methods of cross-referencing. Accuracy of generation includes, but are not limited to, methods for improving the accuracy of FAQ generation based on interrelationships. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit inputs the interrelationships between manuals into the AI, and the AI ​​generates FAQs considering the interrelationships.

[0096] The generation unit can generate FAQs while considering the attribute information of the manual's creator. For example, if the manual's creator is a technician, the generation unit will generate technical FAQs. For example, if the manual's creator is a sales representative, the generation unit will generate FAQs related to sales. For example, the generation unit will generate relevant FAQs based on the manual's area of ​​expertise. This allows for the generation of appropriate FAQs by considering the manual's attribute information. Attribute information includes, but is not limited to, the creator's area of ​​expertise and years of experience. Generation includes, but is not limited to, methods for generating FAQs based on attribute information. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the manual's creator's attribute information into the AI, and the AI ​​generates FAQs while considering the attribute information.

[0097] The generation unit can estimate the user's emotions and adjust the display method of the generated FAQ based on the estimated user emotions. For example, if the user is stressed, the generation unit provides a simple and highly visible display method. For example, if the user is relaxed, the generation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the generation unit provides a display method that gets straight to the point. This allows the display method of the FAQ to be adjusted according to the user's emotions. Estimation of emotions includes, but is not limited to, facial recognition and voice analysis. Adjusting the display method includes, but is not limited to, changing the display method of the FAQ according to the emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the user's emotions into the AI, and the AI ​​estimates the emotions and adjusts the display method of the FAQ.

[0098] The generation unit can perform FAQ generation while considering the geographical distribution of manuals. For example, the generation unit can prioritize the generation of FAQs related to a specific region. For example, the generation unit can generate relevant FAQs based on geographical distribution. For example, the generation unit can generate optimal FAQs while considering the characteristics of each region. This makes it possible to generate appropriate FAQs by considering the geographical distribution of manuals. Geographical distribution includes, but is not limited to, the distribution of manuals by region, geographical relevance, etc. Generation includes, but is not limited to, methods for generating FAQs based on geographical distribution, etc. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the geographical distribution of manuals into AI, and the AI ​​generates FAQs while considering the geographical distribution.

[0099] The generation unit can improve the accuracy of FAQ generation by referring to relevant documents in the manual. For example, the generation unit improves the accuracy of the FAQ by referring to relevant documents. For example, the generation unit integrates the contents of the manual with relevant documents to generate a comprehensive FAQ. For example, the generation unit generates a detailed FAQ based on relevant documents. This allows the accuracy of the FAQ to be improved by referring to relevant documents in the manual. Relevant documents include, but are not limited to, documents and reference materials related to the contents of the manual. Accuracy of generation includes, but are not limited to, methods for improving the accuracy of FAQ generation based on relevant documents. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the relevant documents in the manual into the AI, and the AI ​​generates the FAQ by referring to the relevant documents.

[0100] The service provider can estimate the user's emotions and adjust the display method of the FAQs based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. This allows the service provider to adjust the display method of the FAQs according to the user's emotions. Examples of how to estimate emotions include, but are not limited to, facial recognition and voice analysis. Examples of how to adjust the display method include, but are not limited to, changing the display method of the FAQs according to the emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider inputs the user's emotions into the AI, and the AI ​​estimates the emotions and adjusts the display method of the FAQs.

[0101] The service provider can select the optimal display method by referring to the user's past operation history when providing FAQs. For example, the service provider provides the optimal display method based on the user's past operation history. For example, the service provider prioritizes providing display methods that the user has used in the past. For example, the service provider analyzes the user's operation history to select the optimal display method. This allows the service provider to select the optimal display method based on the user's past operation history. Operation history includes, but is not limited to, the user's past operation data and usage history. Display methods include, but are not limited to, methods for selecting a display method based on past operation history. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider inputs the user's past operation history into AI, and the AI ​​analyzes the operation history to select the optimal display method.

[0102] The service provider can estimate the user's emotions and adjust the operating procedures of the FAQ based on the estimated emotions. For example, if the user is stressed, the service provider will provide simple and easy-to-understand operating procedures. For example, if the user is relaxed, the service provider will provide detailed operating procedures. For example, if the user is in a hurry, the service provider will provide procedures that can be operated quickly. This allows the operating procedures of the FAQ to be adjusted according to the user's emotions. Estimation of emotions includes, but is not limited to, facial recognition and voice analysis. Adjusting operating procedures includes, but is not limited to, changing the operating procedures according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider inputs the user's emotions into the AI, and the AI ​​estimates the emotions and adjusts the operating procedures.

[0103] The service provider can select the optimal display method when providing FAQs, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider will provide a concise and highly visible display method. This allows the service provider to select the optimal display method based on the user's device information. Device information includes, but is not limited to, the type of device the user is using and the screen size. Display methods include, but are not limited to, methods for selecting a display method based on device information. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider may input the user's device information into the AI, and the AI ​​may select the optimal display method taking the device information into consideration.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The reception unit may have a function to automatically summarize the content of a manual entered by the user. For example, the reception unit can summarize a long manual into a short one, allowing the user to quickly obtain the information they need. The reception unit can also present the summarized content to the user and allow the user to access the original manual if they wish to check the details. This allows the user to efficiently obtain the information they need. The summarization may include, but is not limited to, summarization algorithms using natural language processing technology. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI.

[0106] The analysis unit can automatically search for relevant video tutorials based on the content of the manual when analyzing the input manual's contents and provide them to the user. For example, the analysis unit can search for video tutorials on specific operating procedures, enabling the user to understand them visually. The analysis unit can also provide the user with links to the video tutorials, allowing the user to visually confirm the necessary information. This makes it easier for the user to understand how to operate the system. Searching for video tutorials includes, but is not limited to, using video platforms on the internet. Some or all of the processing described above in the analysis unit may be performed using AI, for example, or without AI.

[0107] When generating an FAQ chatbot, the generation unit can analyze the user's past question history and prioritize including the most frequently asked questions in the FAQ. For example, if the generation unit finds that there are many questions about a particular operation method based on the past question history, it will prioritize generating an FAQ about that operation method. The generation unit also analyzes the user's question history and enriches the answers to frequently asked questions. This allows users to quickly obtain the information they need. The analysis of the question history includes, but is not limited to, methods using data mining techniques. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI.

[0108] The service provider can dynamically change the display order of FAQs based on user usage when providing the generated FAQ chatbot. For example, if a user frequently refers to an FAQ about a particular operation, the service provider will display that FAQ at the top. The service provider also monitors user usage in real time and prioritizes displaying the most relevant FAQs. This allows users to quickly obtain the information they need. Monitoring usage includes, but is not limited to, methods such as analyzing user operation logs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0109] The reception desk may have a function to automatically translate the content of manuals entered by users. For example, the reception desk could translate a manual entered in English into Japanese, making it easier for Japanese-speaking users to understand. The reception desk could also present the translated content to the user, allowing the user to revert to the original language if necessary. This would allow users to obtain the necessary information over language barriers. Translation methods include, but are not limited to, using machine translation technology. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0110] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a concise analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is in a hurry, the analysis unit provides a concise analysis result. This allows the depth of the analysis to be adjusted according to the user's emotions. Examples of emotion estimation include, but are not limited to, facial recognition and voice analysis. Examples of adjusting the depth of the analysis include, but are not limited to, changing the depth of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Examples of generative AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0111] The response unit can estimate the user's emotions and adjust the tone of its response based on the estimated emotions. For example, if the user is stressed, the response unit will provide a gentle response. If the user is relaxed, the response unit will provide a friendly response. If the user is in a hurry, the response unit will provide a quick and clear response. This allows the response unit to adjust its tone according to the user's emotions. Estimation of emotions includes, but is not limited to, facial recognition and voice analysis. Tone adjustment includes, but is not limited to, changing the tone of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the response unit may be performed using, for example, AI, or not using AI.

[0112] The generation unit can estimate the user's emotions and adjust the content of the generated FAQ based on the estimated user emotions. For example, if the user is stressed, the generation unit generates a concise and easy-to-understand FAQ. If the user is relaxed, the generation unit generates an FAQ with detailed information. If the user is in a hurry, the generation unit generates a concise FAQ. This allows the content of the FAQ to be adjusted according to the user's emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Content adjustment includes, but is not limited to, modifying the content of the FAQ according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, for example, but is not limited to these. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0113] The service provider can estimate the user's emotions and adjust how the FAQs are displayed based on the estimated emotions. For example, if the user is stressed, the service provider provides a simple and highly visible display. If the user is relaxed, the service provider provides a display that includes detailed information. If the user is in a hurry, the service provider provides a display that gets straight to the point. This allows the FAQ display method to be adjusted according to the user's emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Adjusting the display method includes, but is not limited to, changing how the FAQs are displayed according to the emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0114] The service provider can estimate the user's emotions and adjust the operating procedures of the FAQ based on the estimated user emotions. For example, if the user is stressed, the service provider will provide simple and easy-to-understand operating procedures. If the user is relaxed, the service provider will provide detailed operating procedures. If the user is in a hurry, it will provide procedures that allow for quick operation. This allows the operating procedures of the FAQ to be adjusted according to the user's emotions. Examples of emotion estimation include, but are not limited to, facial recognition and voice analysis. Examples of adjusting operating procedures include, but are not limited to, changing the operating procedures according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, for example, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives the manual as a file from the user. The reception desk can accept manuals in formats such as PDF or Word. Users only need to upload the manual in their preferred format. Step 2: The analysis unit analyzes the manual entered by the reception unit. The analysis unit uses natural language processing technology to understand the content of the manual and provides the necessary information in a conversational format. Step 3: The answering unit provides the necessary knowledge in an interactive format based on the content of the manual analyzed by the analysis unit. The answering unit uses natural language processing technology to provide appropriate answers to the user's questions. Step 4: The generation unit automatically generates an FAQ chatbot based on the content analyzed by the analysis unit. The generation unit uses natural language processing technology to generate FAQs about a specific device and provides appropriate answers to questions about that device. Step 5: The provider unit provides the FAQ chatbot generated by the generator unit. The provider unit provides the generated FAQ chatbot to users through a website or application.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, response unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs the manual as a file. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the input manual. The response unit is implemented by the specific processing unit 290 of the data processing unit 12, where it answers the necessary knowledge in a conversational format based on the analyzed content. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it automatically generates an FAQ chatbot. The provision unit is implemented by the control unit 46A of the smart device 14, where it provides the generated FAQ chatbot to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, answering unit, generation unit, and provisioning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs a manual as a file. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, where the input manual is analyzed. The answering unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, where the necessary knowledge is answered in a conversational format based on the analyzed content. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, where an FAQ chatbot is automatically generated. The provisioning unit is implemented, for example, by the control unit 46A of the smart glasses 214, where the generated FAQ chatbot is provided to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, answering unit, generation unit, and provisioning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs the manual as a file. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the input manual is analyzed. The answering unit is implemented by the specific processing unit 290 of the data processing unit 12, where the necessary knowledge is answered in a conversational format based on the analyzed content. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where the FAQ chatbot is automatically generated. The provisioning unit is implemented by the control unit 46A of the headset terminal 314, where the generated FAQ chatbot is provided to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, answering unit, generation unit, and provisioning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user inputs a manual as a file. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the input manual. The answering unit is implemented by the specific processing unit 290 of the data processing unit 12, where it answers with the necessary knowledge in an interactive format based on the analyzed content. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it automatically generates an FAQ chatbot. The provisioning unit is implemented by the control unit 46A of the robot 414, where it provides the generated FAQ chatbot to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A reception area where users input manuals as files, An analysis unit that analyzes the manual entered by the reception unit, Based on the contents of the manual analyzed by the aforementioned analysis unit, the answer unit provides answers in an interactive format that provide the necessary knowledge. A generation unit that automatically generates an FAQ chatbot based on the content analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the FAQ chatbot generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter manuals in PDF or Word format. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Understand the contents of the entered manual and answer the necessary questions in a conversational format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate FAQs for specific devices The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides a generated FAQ chatbot. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of manual input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past manual input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering manual data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of manual entries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering manuals, the system prioritizes inputting the most relevant manuals by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering manuals, the system analyzes the user's social media activity and enters relevant manuals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the manual. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the manual. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the manual was created. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the manual. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned response section is, It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned response section is, When responding, adjust the level of detail in your answer based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned response section is, When answering, different answer algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned response section is, It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned response section is, When responding, prioritize your answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned response section is, When answering, adjust the order of your answers based on their relevance to the questions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and determines the priority of FAQs to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating FAQs, we improve the accuracy of the generation by considering the interrelationships within the manual. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating FAQs, the system takes into account the attribute information of the manual's creator. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is We estimate the user's emotions and adjust how FAQs are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating FAQs, the geographical distribution of manuals is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating FAQs, we improve the accuracy of the generation by referring to relevant documents in the manual. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, We estimate the user's emotions and adjust how FAQs are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing FAQs, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, We estimate the user's emotions and adjust the FAQ instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing FAQs, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input manuals as files, An analysis unit that analyzes the manual entered by the reception unit, Based on the contents of the manual analyzed by the aforementioned analysis unit, the answer unit provides answers in an interactive format that provide the necessary knowledge. A generation unit that automatically generates an FAQ chatbot based on the content analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the FAQ chatbot generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Understand the contents of the entered manual and answer the necessary questions in a conversational format. The system according to feature 1.

3. The generating unit is Generate FAQs for specific devices The system according to feature 1.

4. The aforementioned supply unit is, Provides a generated FAQ chatbot. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of manual input based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past manual input history and select the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When entering manual data, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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