system

Abstract

The system according to this embodiment aims to streamline the process of providing operational instructions and answering customer questions, thereby reducing the burden on store staff. [Solution] The system according to the embodiment comprises an operation guide generation unit, a visual aid provision unit, a question answering unit, a real-time response unit, and a learning unit. The operation guide generation unit generates an operation guide. The visual aid provision unit provides the operation guide generated by the operation guide generation unit as a visual aid. The question answering unit provides immediate answers to basic questions. The real-time response unit responds to questions in real time. The learning unit learns from past question data and improves its responses.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a lot of time to explain operations and answer questions for customers who are not familiar with digital technology, and the burden on store staff is large.

[0005] The system according to the embodiment aims to improve the efficiency of operation explanations and question answering for customers and reduce the burden on store staff.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an operation guide generation unit, a visual aid provision unit, a question answering unit, a real-time response unit, and a learning unit. The operation guide generation unit generates an operation guide. The visual aid provision unit provides the operation guide generated by the operation guide generation unit as a visual aid. The question answering unit provides immediate answers to basic questions. The real-time response unit responds to questions in real time. The learning unit learns from past question data and improves its responses. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the process of providing operational instructions and answering customer questions, thereby reducing the burden on store staff. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[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 AssistMate system according to an embodiment of the present invention is an interactive learning support tool that incorporates generative AI. This AssistMate system has the following functions: It provides an easy-to-understand and interactive operation guide for customers by utilizing generative AI. It enables explanations that are easier to understand by utilizing visual aids (images and videos). By providing immediate answers to basic questions, store staff can concentrate on more specialized tasks. It responds to questions in real time, minimizing waiting times. Customers can obtain the information they need immediately and receive stress-free service. The generative AI learns from interactions with customers and enables quick responses to past questions, thereby meeting customer expectations. As a result, the AssistMate system can reduce the burden on store staff and enable efficient business operations. Customers can obtain information at their own pace, mitigating the difficulty of understanding due to the digital divide. Furthermore, the ability to respond quickly leads to increased customer satisfaction. Ultimately, it is expected to effectively evolve store operations and maximize sales opportunities.

[0029] The AssistMate system according to this embodiment comprises an operation guide generation unit, a visual aid provision unit, a question answering unit, a real-time response unit, and a learning unit. The operation guide generation unit generates an operation guide. The operation guide generation unit generates an operation guide using, for example, a generation AI. The generation AI can generate an operation guide using a text generation AI (e.g., LLM). The operation guide generation unit can also generate an operation guide that includes images and videos using the generation AI. For example, the generation AI generates a video explaining the operation procedure and provides it as an operation guide. The visual aid provisioning unit provides the operation guide generated by the operation guide generation unit as a visual aid. The visual aid provisioning unit can provide a visual aid using, for example, a generation AI. The visual aid provisioning unit can also provide a visual aid that includes images and videos using the generation AI. For example, the visual aid provisioning unit generates a video explaining the operation procedure and provides it as a visual aid. The question answering unit provides immediate answers to basic questions. The question answering unit can provide immediate answers to basic questions using, for example, a generation AI. The question answering unit can also instantly answer basic questions using generative AI and text generation AI (e.g., LLM). For example, the question answering unit can instantly answer frequently asked questions. The real-time response unit responds to questions in real time. The real-time response unit can respond to questions in real time using generative AI, for example. The real-time response unit can also respond to questions in real time using generative AI and text generation AI (e.g., LLM). For example, the real-time response unit can instantly answer customer questions. The learning unit learns from past question data and improves responses. The learning unit can learn from past question data using generative AI, for example. The learning unit can also learn from past question data using generative AI and text generation AI (e.g., LLM) and improve responses. For example, the learning unit can provide more appropriate answers based on past question data.As a result, the AssistMate system according to this embodiment can reduce the burden on store staff and enable efficient business operations.

[0030] The operation guide generation unit generates operation guides. The operation guide generation unit generates operation guides using, for example, a generation AI. The generation AI can generate operation guides using a text generation AI (e.g., LLM). Specifically, the generation AI receives information about the operation the user wants to perform as input and generates detailed operation procedures based on that information. For example, if a user wants to know how to install a specific software, the generation AI will generate text that explains the installation procedure of that software in detail. The operation guide generation unit can also use the generation AI to generate operation guides that include images and videos. For example, the generation AI can generate a video explaining the operation procedure and provide it as an operation guide. The video visually shows the specific operation procedure and is provided in a format that is easy for the user to understand intuitively. Furthermore, the generation AI can also customize the optimal operation guide by considering the user's skill level and past operation history. In this way, the operation guide generation unit can provide high-quality operation guides that meet the user's needs and support the user's operations.

[0031] The Visual Aid Provider Unit provides the operation guides generated by the Operation Guide Generation Unit as visual aids. The Visual Aid Provider Unit can, for example, provide visual aids using a generation AI. Specifically, the Visual Aid Provider Unit provides users with images and videos generated by the generation AI, visually supporting the operation procedures. For example, the Visual Aid Provider Unit generates a video explaining the operation procedures and provides it as a visual aid. The video visually demonstrates the specific operation procedures and is provided in a format that is easy for users to understand intuitively. Furthermore, the Visual Aid Provider Unit can provide the most suitable visual aids depending on the user's device and environment. For example, it can provide visual aids optimized for mobile devices such as smartphones and tablets, allowing users to access the operation guides anywhere. The Visual Aid Provider Unit can also collect user feedback and continuously improve the quality and content of the visual aids. This enables the Visual Aid Provider Unit to provide effective visual support to users and facilitate their understanding of the operations.

[0032] The question-answering unit provides immediate answers to basic questions. For example, it can use generative AI to provide immediate answers to basic questions. Specifically, the question-answering unit receives questions from users and provides appropriate answers using generative AI. The generative AI generates answers to user questions using text generation AI (e.g., LLM). For example, the question-answering unit can provide immediate answers to frequently asked questions. When a user asks a question about a specific operation or troubleshooting, the question-answering unit provides a quick and accurate answer using generative AI. Furthermore, the question-answering unit can prepare answers to frequently asked questions in advance based on past question data and provide them quickly. This allows the question-answering unit to quickly resolve user doubts and support smooth operation.

[0033] The real-time response unit responds to questions in real time. For example, the real-time response unit can respond to questions in real time using generative AI. Specifically, the real-time response unit receives questions from users in real time and provides appropriate answers on the spot using generative AI. The generative AI uses text generation AI (e.g., LLM) to instantly generate answers to user questions. For example, the real-time response unit can instantly answer customer questions. It can provide real-time support and quickly resolve any questions or problems that arise during user operation. Furthermore, the real-time response unit can collect user feedback in real time and continuously improve the accuracy and effectiveness of its responses. This allows the real-time response unit to provide users with quick and appropriate support, ensuring smooth operation.

[0034] The learning unit learns from past question data and improves its responses. For example, the learning unit can use generative AI to learn from past question data and improve its responses. Specifically, the learning unit stores past questions and answers submitted by users in a database and analyzes that data using generative AI. The generative AI uses text generation AI (e.g., LLM) to learn response patterns and trends based on past question data. For example, the learning unit can provide more appropriate answers based on past question data. This allows the learning unit to continuously improve the accuracy and quality of its responses to user questions. In addition, the learning unit constantly learns from the latest data using generative AI so that it can respond quickly to newly arising questions and problems. This allows the learning unit to always provide optimal support to users and improve the overall performance of the system.

[0035] The operation guide generation unit can generate operation guides using a generation AI. The operation guide generation unit generates operation guides using, for example, a generation AI. The generation AI can generate operation guides using a text generation AI (e.g., LLM). The operation guide generation unit can also generate operation guides that include images and videos using the generation AI. For example, the generation AI can generate a video explaining the operation procedure and provide it as an operation guide. This makes the generation of operation guides more efficient by using a generation AI. The generation AI takes information necessary for generating an operation guide as input and outputs an operation guide. The generation AI generates an operation guide based on the information necessary for generating an operation guide. The generation AI can learn the information necessary for generating an operation guide and generate more appropriate operation guides.

[0036] The visual aid provider can provide visual aids using a generative AI. For example, the visual aid provider can provide visual aids using a generative AI. The generative AI can provide visual aids using a text generation AI (e.g., LLM). Furthermore, the visual aid provider can also provide visual aids that include images and videos using the generative AI. For example, the generative AI can generate a video explaining the operating procedure and provide it as a visual aid. This makes the provision of visual aids more efficient by using a generative AI. For example, the generative AI takes information necessary for providing a visual aid as input and outputs a visual aid. The generative AI provides a visual aid based on the information necessary for providing a visual aid. The generative AI can learn the information necessary for providing a visual aid and provide more appropriate visual aids.

[0037] The question-answering unit can instantly answer basic questions using generative AI. For example, the question-answering unit instantly answers basic questions using generative AI. The generative AI can instantly answer basic questions using text generation AI (e.g., LLM). Furthermore, the question-answering unit can also provide answers that include images and videos using generative AI. For example, the generative AI can instantly answer frequently asked questions. This speeds up the response to basic questions by using generative AI. For example, the generative AI takes the information necessary to answer a basic question as input and outputs an answer. The generative AI provides answers based on the information necessary to answer basic questions. The generative AI can learn the information necessary to answer basic questions and provide more appropriate answers.

[0038] The real-time response unit can respond to questions in real time using a generative AI. The real-time response unit responds to questions in real time, for example, using a generative AI. The generative AI can respond to questions in real time using a text generation AI (for example, LLM). The real-time response unit can also provide answers that include images and videos using the generative AI. For example, the generative AI can answer a customer's question immediately. This makes real-time question response possible by using a generative AI. The generative AI takes information necessary for real-time question response as input and outputs an answer. The generative AI provides an answer based on the information necessary for real-time question response. The generative AI can learn the information necessary for real-time question response and provide more appropriate answers.

[0039] The learning unit can improve its responses by learning from past question data using a generative AI. For example, the learning unit uses a generative AI to learn from past question data and improve its responses. The generative AI can learn from past question data using a text generation AI (e.g., LLM) and improve its responses. Furthermore, the learning unit can also use a generative AI to learn from data including images and videos and improve its responses. For example, the generative AI can provide more appropriate answers based on past question data. This improves the accuracy of responses when using a generative AI. For example, the generative AI takes past question data as input and outputs learning results. The generative AI learns based on past question data. The generative AI learns from past question data and can provide more appropriate answers.

[0040] The operation guide generation unit can generate the most suitable guide by referring to the user's past operation history when generating an operation guide. For example, the operation guide generation unit's AI can generate an operation guide that prioritizes explaining functions frequently used by the user in the past. For example, the operation guide generation unit's AI can analyze the user's past operation errors and generate an operation guide that emphasizes points to watch out for. For example, the operation guide generation unit's AI can generate an operation guide that provides supplementary explanations for parts that the user previously found difficult to understand. In this way, by referring to past operation history, the system can provide the user with the most suitable operation guide. Some or all of the above-described processes in the operation guide generation unit may be performed using AI, for example, or without AI. For example, the operation guide generation unit can input the user's past operation history data into the generation AI and have the generation AI perform the generation of the most suitable operation guide.

[0041] The operation guide generation unit can generate customized operation guides according to the user's device type and settings. For example, if the user is using a smartphone, the AI ​​generates an operation guide optimized for the screen size. If the user is using a tablet, the AI ​​generates an operation guide adapted to the larger screen. If the user has enabled a specific setting, the AI ​​generates an operation guide based on that setting. This improves user convenience by providing operation guides tailored to the device type and settings. Some or all of the above processing in the operation guide generation unit may be performed using AI, for example, or without AI. For example, the operation guide generation unit can input the user's device information into the AI ​​and have the AI ​​generate a customized operation guide.

[0042] The operation guide generation unit can include region-specific information when generating operation guides, taking into account the user's geographical location. For example, if the user is in a specific region, the AI ​​generates an operation guide that explains the region's specific settings and functions. For example, if the user is traveling, the AI ​​generates an operation guide that includes information about using the device at the travel destination. For example, if the user is in a specific country, the AI ​​generates an operation guide tailored to the language and culture of that country. By including region-specific information, the operation guide generation unit can provide users with operation guides that are appropriate for their needs. Some or all of the above-described processes in the operation guide generation unit may be performed using AI, for example, or without AI. For example, the operation guide generation unit can input the user's geographical location information into the AI ​​and have the AI ​​generate an operation guide that includes region-specific information.

[0043] The operation guide generation unit can analyze the user's social media activity and provide relevant operation guides when generating them. For example, the operation guide generation unit's AI can generate an operation guide explaining functions that the user frequently uses on social media. For example, the operation guide generation unit's AI can generate relevant operation guides based on information shared by the user on social media. For example, the operation guide generation unit can analyze the user's social media activity history and its AI can generate the optimal operation guide. In this way, by analyzing social media activity, relevant operation guides can be provided to the user. Some or all of the above processing in the operation guide generation unit may be performed using AI, for example, or without AI. For example, the operation guide generation unit can input the user's social media activity data into the generation AI and have the generation AI perform the generation of relevant operation guides.

[0044] The visual aid provider can provide the most suitable visual aid by referring to the user's past viewing history when providing a visual aid. For example, the visual aid provider can provide a relevant visual aid based on the visual aid the user has viewed in the past. For example, the visual aid provider can provide a visual aid in an easy-to-understand format based on the user's past viewing history. For example, the visual aid provider can provide a visual aid that supplements the content of a visual aid the user has viewed in the past. In this way, by referring to past viewing history, the most suitable visual aid can be provided to the user. Some or all of the above processing in the visual aid provider may be performed using AI, for example, or without AI. For example, the visual aid provider can input the user's past viewing history data into a generating AI and have the generating AI perform the task of providing the most suitable visual aid.

[0045] The visual aid provider can provide visual aids optimized according to the screen size and resolution of the user's device when providing them. For example, if the user is using a smartphone, the visual aid provider will provide a visual aid optimized for the screen size. For example, if the user is using a tablet, the visual aid provider will provide a visual aid optimized for the larger screen. For example, if the user is using a high-resolution device, the visual aid provider will provide a visual aid that supports high resolution. This improves user convenience by providing visual aids that are appropriate for the device's screen size and resolution. Some or all of the above processing in the visual aid provider may be performed using AI, for example, or without AI. For example, the visual aid provider can input the user's device information into a generating AI and have the generating AI perform the task of providing optimized visual aids.

[0046] The visual aid provider can provide region-specific visual aids by taking into account the user's geographical location information when providing visual aids. For example, if the user is in a specific region, the visual aid provider can provide visual aids that explain the specific settings and functions of that region. For example, if the user is traveling, the visual aid provider can provide visual aids that include information about usage at the travel destination. For example, if the user is in a specific country, the visual aid provider can provide visual aids tailored to the language and culture of that country. By providing region-specific visual aids, the system can provide information that is appropriate for the user. Some or all of the above processing in the visual aid provider may be performed using AI, for example, or without AI. For example, the visual aid provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing region-specific visual aids.

[0047] The visual aid provider can analyze a user's social media activity and provide relevant visual aids when providing them. For example, the visual aid provider can provide a visual aid that explains a function the user frequently uses on social media. For example, the visual aid provider can provide relevant visual aids based on information the user has shared on social media. For example, the visual aid provider can analyze a user's social media activity history and provide the most suitable visual aid. This allows the provider to provide visual aids relevant to the user by analyzing their social media activity. Some or all of the above processing in the visual aid provider may be performed using AI, for example, or without AI. For example, the visual aid provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant visual aids.

[0048] The question-answering unit can provide the most appropriate answer by referring to the user's past question history when answering a question. For example, the question-answering unit can use a generating AI to provide relevant answers based on the content of questions the user has asked in the past. For example, the question-answering unit can use a generating AI to provide answers in an easy-to-understand format from the user's past question history. For example, the question-answering unit can use a generating AI to provide answers that supplement the content of questions the user has asked in the past. In this way, by referring to the past question history, the system can provide the user with the most appropriate answer. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's past question history data into a generating AI and have the generating AI perform the task of providing the most appropriate answer.

[0049] The question-answering unit can provide customized answers to questions based on the user's device type and settings. For example, if the user is using a smartphone, the generating AI will provide an answer optimized for the screen size. If the user is using a tablet, the generating AI will provide an answer optimized for the larger screen. If the user has enabled a specific setting, the generating AI will provide an answer based on that setting. This improves user convenience by providing answers tailored to the device type and settings. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's device information into the generating AI and have the generating AI perform the task of providing customized answers.

[0050] The question-answering unit can provide region-specific answers by considering the user's geographical location information when answering questions. For example, if the user is in a specific region, the generating AI in the question-answering unit can provide answers regarding region-specific settings and functions. For example, if the user is traveling, the generating AI in the question-answering unit can provide answers that include information about usage at the travel destination. For example, if the user is in a specific country, the generating AI in the question-answering unit can provide answers tailored to the language and culture of that country. In this way, by providing region-specific answers, information suitable for the user can be provided. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's geographical location information into the generating AI and have the generating AI perform the task of providing region-specific answers.

[0051] The question-answering unit can analyze the user's social media activity and provide relevant answers when answering questions. For example, the question-answering unit can use a generating AI to provide answers regarding features the user frequently uses on social media. For example, the question-answering unit can use a generating AI to provide relevant answers based on information the user has shared on social media. For example, the question-answering unit can use a generating AI to provide optimal answers by analyzing the user's social media activity history. In this way, by analyzing social media activity, it is possible to provide answers relevant to the user. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant answers.

[0052] The real-time response unit can provide the optimal response by referring to the user's past real-time question history during real-time response. For example, the real-time response unit can generate a relevant response based on the content of questions the user has asked in real time in the past. For example, the real-time response unit can generate a response in an easy-to-understand format from the user's past real-time question history. For example, the real-time response unit can generate a response that supplements the content of questions the user has asked in real time in the past. In this way, the system can provide the user with the optimal response by referring to the past real-time question history. Some or all of the above processing in the real-time response unit may be performed using AI, for example, or without AI. For example, the real-time response unit can input the user's past real-time question history data into the generation AI and have the generation AI perform the task of providing the optimal response.

[0053] The real-time response unit can provide customized responses in real time, depending on the user's device type and settings. For example, if the user is using a smartphone, the generating AI provides a response optimized for the screen size. If the user is using a tablet, the generating AI provides a response optimized for the larger screen. If the user has enabled a specific setting, the generating AI provides a response based on that setting. This improves user convenience by providing responses tailored to the device type and settings. Some or all of the above processing in the real-time response unit may be performed using AI, for example, or without AI. For example, the real-time response unit can input the user's device information into the generating AI and have the generating AI perform the task of providing a customized response.

[0054] The real-time response unit can provide region-specific responses by taking into account the user's geographical location information during real-time responses. For example, if the user is in a specific region, the generating AI in the real-time response unit can provide responses regarding region-specific settings and functions. For example, if the user is traveling, the generating AI in the real-time response unit can provide responses that include information about usage at the travel destination. For example, if the user is in a specific country, the generating AI in the real-time response unit can provide responses tailored to the language and culture of that country. In this way, by providing region-specific responses, information suitable for the user can be provided. Some or all of the above processing in the real-time response unit may be performed using AI, for example, or without AI. For example, the real-time response unit can input the user's geographical location information into the generating AI and have the generating AI perform the provision of region-specific responses.

[0055] The real-time response unit can analyze a user's social media activity and provide relevant responses in real time. For example, the real-time response unit can generate AI responses regarding features that the user frequently uses on social media. For example, the real-time response unit can generate AI responses based on information shared by the user on social media. For example, the real-time response unit can analyze a user's social media activity history and generate AI responses to provide the most appropriate responses. This allows the system to provide user-relevant responses by analyzing social media activity. Some or all of the above-described processes in the real-time response unit may be performed using AI, for example, or without AI. For example, the real-time response unit can input user social media activity data into the generation AI and have the generation AI provide relevant responses.

[0056] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit's generating AI selects the optimal learning algorithm based on past learning data. For example, the learning unit's generating AI selects an effective learning method from past learning data. For example, the learning unit analyzes past learning data and optimizes the learning algorithm. This improves the accuracy of the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into the generating AI and have the generating AI perform the optimization of the learning algorithm.

[0057] The learning unit can customize the learning data during training according to the user's device type and settings. For example, if the user is using a smartphone, the learning unit generates AI-generated learning data optimized for the screen size. If the user is using a tablet, the learning unit generates AI-generated learning data optimized for the larger screen. If the user has enabled a specific setting, the learning unit generates AI-generated learning data based on that setting. This improves user convenience by providing learning data tailored to the device type and settings. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's device information into the generating AI and have the generating AI provide customized learning data.

[0058] The learning unit can use region-specific learning data, taking into account the user's geographical location information, during the learning process. For example, if the user is in a specific region, the learning unit can generate learning data containing region-specific information using the AI. For example, if the user is traveling, the learning unit can generate learning data containing information about usage at the travel destination using the AI. For example, if the user is in a specific country, the learning unit can generate learning data tailored to the language and culture of that country using the AI. This allows the learning unit to provide information that is appropriate for the user by providing region-specific learning data. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into the generating AI and have the generating AI perform the task of providing region-specific learning data.

[0059] The learning unit can analyze the user's social media activity during training and use relevant training data. For example, the learning unit can generate AI to provide training data about features the user frequently uses on social media. For example, the learning unit can generate AI to provide relevant training data based on information the user has shared on social media. For example, the learning unit can analyze the user's social media activity history and generate AI to provide optimal training data. This allows the learning unit to provide training data relevant to the user by analyzing social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity data into the generating AI and have the generating AI provide relevant training data.

[0060] The learning unit can provide optimal learning data during learning, taking into account the user's health condition. For example, if the user is tired, the learning unit's generating AI provides concise and easy-to-understand learning data. If the user is relaxed, the learning unit's generating AI provides learning data that includes detailed explanations. If the user is in a hurry, the learning unit's generating AI provides short, to-the-point learning data. This improves the effectiveness of learning by providing learning data tailored to the user's health condition. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user health condition data into the generating AI and have the generating AI provide optimal learning data.

[0061] The learning unit can provide optimal learning data by referring to the user's past learning history during the learning process. For example, the learning unit can generate AI to provide relevant learning data based on what the user has learned in the past. For example, the learning unit can generate AI to provide learning data in an easy-to-understand format from the user's past learning history. For example, the learning unit can generate AI to provide learning data that supplements what the user has learned in the past. In this way, by referring to the past learning history, the learning unit can provide the user with optimal learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past learning history data into the generating AI and have the generating AI perform the task of providing optimal learning data.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The AssistMate system can generate optimal operation guides by referring to the user's past operation history. For example, it can provide operation guides that prioritize explaining functions the user has frequently used in the past. It can also analyze the user's past operation errors and provide operation guides that highlight points to be careful about. Furthermore, it can provide operation guides that supplement explanations of parts that the user previously found difficult to understand. In this way, by referring to past operation history, the system can provide the user with the most suitable operation guide, deepening the user's understanding. The operation guide generation unit can input the user's past operation history data into a generation AI and have it perform the generation of the optimal operation guide.

[0064] The AssistMate system can generate customized operation guides according to the user's device type and settings. For example, if the user is using a smartphone, it can provide an operation guide optimized for the screen size. If the user is using a tablet, it can provide an operation guide adapted to the larger screen. Furthermore, if the user has enabled specific settings, it can provide an operation guide based on those settings. This improves user convenience by providing operation guides tailored to the device type and settings. The operation guide generation unit inputs the user's device information into the generation AI and executes the generation of a customized operation guide.

[0065] The AssistMate system can generate operation guides that include region-specific information, taking into account the user's geographical location. For example, if the user is in a specific region, it can provide an operation guide that explains the region's specific settings and functions. If the user is traveling, it can provide an operation guide that includes information about using the system at their travel destination. Furthermore, if the user is in a specific country, it can provide an operation guide tailored to the language and culture of that country. This allows the system to provide users with information relevant to their needs by offering operation guides that include region-specific information. The operation guide generation unit inputs the user's geographical location information into the generation AI, which then generates an operation guide that includes region-specific information.

[0066] The AssistMate system can analyze a user's social media activity and provide relevant operation guides. For example, it can provide operation guides that explain functions frequently used by the user on social media. It can also provide relevant operation guides based on information shared by the user on social media. Furthermore, it can analyze a user's social media activity history and provide the most suitable operation guide. In this way, by analyzing social media activity, it can provide operation guides relevant to the user. The operation guide generation unit can input the user's social media activity data into a generation AI and have it perform the generation of relevant operation guides.

[0067] The AssistMate system can provide optimal visual aids by referencing the user's past viewing history. For example, it can provide relevant visual aids based on visual aids the user has viewed in the past. It can also provide visual aids in an easy-to-understand format based on the user's past viewing history. Furthermore, it can provide visual aids that supplement the content of visual aids the user has viewed in the past. As a result, by referring to past viewing history, the system can provide the user with the most suitable visual aids, deepening their understanding. The visual aid provisioning unit can input the user's past viewing history data into a generating AI and have it perform the task of providing the optimal visual aids.

[0068] The AssistMate system can provide visual aids optimized according to the screen size and resolution of the user's device. For example, if the user is using a smartphone, it can provide visual aids optimized for the screen size. If the user is using a tablet, it can provide visual aids optimized for the larger screen. Furthermore, if the user is using a high-resolution device, it can provide visual aids that support high resolution. This improves user convenience by providing visual aids that are appropriate for the device's screen size and resolution. The visual aid provisioning unit inputs the user's device information into a generating AI and executes the provision of optimized visual aids.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The operation guide generation unit generates an operation guide. For example, it can generate an operation guide using a generation AI, or it can generate an operation guide using a text generation AI (e.g., LLM). It can also generate an operation guide that includes images and videos using a generation AI. For example, the generation AI can generate a video explaining the operation procedure and provide it as an operation guide. Step 2: The Visual Aid Provider Unit provides the operation guide generated by the Operation Guide Generation Unit as a Visual Aid. For example, it is also possible to provide a Visual Aid using a generation AI, and to provide a Visual Aid that includes images and videos. For example, the Visual Aid Provider Unit generates a video explaining the operation procedure and provides it as a Visual Aid. Step 3: The question-answering unit provides immediate answers to basic questions. For example, it can use generative AI to provide immediate answers to basic questions, or it can use text-generating AI (e.g., LLM) to provide immediate answers to basic questions. For example, the question-answering unit can provide immediate answers to frequently asked questions. Step 4: The real-time response unit responds to questions in real time. For example, it can respond to questions in real time using generative AI, or it can respond to questions in real time using text generation AI (e.g., LLM). For example, the real-time response unit can answer customer questions immediately. Step 5: The learning unit learns from past question data and improves its responses. For example, it can use a generative AI to learn from past question data and improve its responses. It can also use a generative AI, such as a text generation AI (e.g., LLM), to learn from past question data and improve its responses. For example, the learning unit can provide more appropriate answers based on past question data.

[0071] (Example of form 2) The AssistMate system according to an embodiment of the present invention is an interactive learning support tool that incorporates generative AI. This AssistMate system has the following functions: It provides an easy-to-understand and interactive operation guide for customers by utilizing generative AI. It enables explanations that are easier to understand by utilizing visual aids (images and videos). By providing immediate answers to basic questions, store staff can concentrate on more specialized tasks. It responds to questions in real time, minimizing waiting times. Customers can obtain the information they need immediately and receive stress-free service. The generative AI learns from interactions with customers and enables quick responses to past questions, thereby meeting customer expectations. As a result, the AssistMate system can reduce the burden on store staff and enable efficient business operations. Customers can obtain information at their own pace, mitigating the difficulty of understanding due to the digital divide. Furthermore, the ability to respond quickly leads to increased customer satisfaction. Ultimately, it is expected to effectively evolve store operations and maximize sales opportunities.

[0072] The AssistMate system according to this embodiment comprises an operation guide generation unit, a visual aid provision unit, a question answering unit, a real-time response unit, and a learning unit. The operation guide generation unit generates an operation guide. The operation guide generation unit generates an operation guide using, for example, a generation AI. The generation AI can generate an operation guide using a text generation AI (e.g., LLM). The operation guide generation unit can also generate an operation guide that includes images and videos using the generation AI. For example, the generation AI generates a video explaining the operation procedure and provides it as an operation guide. The visual aid provisioning unit provides the operation guide generated by the operation guide generation unit as a visual aid. The visual aid provisioning unit can provide a visual aid using, for example, a generation AI. The visual aid provisioning unit can also provide a visual aid that includes images and videos using the generation AI. For example, the visual aid provisioning unit generates a video explaining the operation procedure and provides it as a visual aid. The question answering unit provides immediate answers to basic questions. The question answering unit can provide immediate answers to basic questions using, for example, a generation AI. The question answering unit can also instantly answer basic questions using generative AI and text generation AI (e.g., LLM). For example, the question answering unit can instantly answer frequently asked questions. The real-time response unit responds to questions in real time. The real-time response unit can respond to questions in real time using generative AI, for example. The real-time response unit can also respond to questions in real time using generative AI and text generation AI (e.g., LLM). For example, the real-time response unit can instantly answer customer questions. The learning unit learns from past question data and improves responses. The learning unit can learn from past question data using generative AI, for example. The learning unit can also learn from past question data using generative AI and text generation AI (e.g., LLM) and improve responses. For example, the learning unit can provide more appropriate answers based on past question data.As a result, the AssistMate system according to this embodiment can reduce the burden on store staff and enable efficient business operations.

[0073] The operation guide generation unit generates operation guides. The operation guide generation unit generates operation guides using, for example, a generation AI. The generation AI can generate operation guides using a text generation AI (e.g., LLM). Specifically, the generation AI receives information about the operation the user wants to perform as input and generates detailed operation procedures based on that information. For example, if a user wants to know how to install a specific software, the generation AI will generate text that explains the installation procedure of that software in detail. The operation guide generation unit can also use the generation AI to generate operation guides that include images and videos. For example, the generation AI can generate a video explaining the operation procedure and provide it as an operation guide. The video visually shows the specific operation procedure and is provided in a format that is easy for the user to understand intuitively. Furthermore, the generation AI can also customize the optimal operation guide by considering the user's skill level and past operation history. In this way, the operation guide generation unit can provide high-quality operation guides that meet the user's needs and support the user's operations.

[0074] The Visual Aid Provider Unit provides the operation guides generated by the Operation Guide Generation Unit as visual aids. The Visual Aid Provider Unit can, for example, provide visual aids using a generation AI. Specifically, the Visual Aid Provider Unit provides users with images and videos generated by the generation AI, visually supporting the operation procedures. For example, the Visual Aid Provider Unit generates a video explaining the operation procedures and provides it as a visual aid. The video visually demonstrates the specific operation procedures and is provided in a format that is easy for users to understand intuitively. Furthermore, the Visual Aid Provider Unit can provide the most suitable visual aids depending on the user's device and environment. For example, it can provide visual aids optimized for mobile devices such as smartphones and tablets, allowing users to access the operation guides anywhere. The Visual Aid Provider Unit can also collect user feedback and continuously improve the quality and content of the visual aids. This enables the Visual Aid Provider Unit to provide effective visual support to users and facilitate their understanding of the operations.

[0075] The question-answering unit provides immediate answers to basic questions. For example, it can use generative AI to provide immediate answers to basic questions. Specifically, the question-answering unit receives questions from users and provides appropriate answers using generative AI. The generative AI generates answers to user questions using text generation AI (e.g., LLM). For example, the question-answering unit can provide immediate answers to frequently asked questions. When a user asks a question about a specific operation or troubleshooting, the question-answering unit provides a quick and accurate answer using generative AI. Furthermore, the question-answering unit can prepare answers to frequently asked questions in advance based on past question data and provide them quickly. This allows the question-answering unit to quickly resolve user doubts and support smooth operation.

[0076] The real-time response unit responds to questions in real time. For example, the real-time response unit can respond to questions in real time using generative AI. Specifically, the real-time response unit receives questions from users in real time and provides appropriate answers on the spot using generative AI. The generative AI uses text generation AI (e.g., LLM) to instantly generate answers to user questions. For example, the real-time response unit can instantly answer customer questions. It can provide real-time support and quickly resolve any questions or problems that arise during user operation. Furthermore, the real-time response unit can collect user feedback in real time and continuously improve the accuracy and effectiveness of its responses. This allows the real-time response unit to provide users with quick and appropriate support, ensuring smooth operation.

[0077] The learning unit learns from past question data and improves its responses. For example, the learning unit can use generative AI to learn from past question data and improve its responses. Specifically, the learning unit stores past questions and answers submitted by users in a database and analyzes that data using generative AI. The generative AI uses text generation AI (e.g., LLM) to learn response patterns and trends based on past question data. For example, the learning unit can provide more appropriate answers based on past question data. This allows the learning unit to continuously improve the accuracy and quality of its responses to user questions. In addition, the learning unit constantly learns from the latest data using generative AI so that it can respond quickly to newly arising questions and problems. This allows the learning unit to always provide optimal support to users and improve the overall performance of the system.

[0078] The operation guide generation unit can generate operation guides using a generation AI. The operation guide generation unit generates operation guides using, for example, a generation AI. The generation AI can generate operation guides using a text generation AI (e.g., LLM). The operation guide generation unit can also generate operation guides that include images and videos using the generation AI. For example, the generation AI can generate a video explaining the operation procedure and provide it as an operation guide. This makes the generation of operation guides more efficient by using a generation AI. The generation AI takes information necessary for generating an operation guide as input and outputs an operation guide. The generation AI generates an operation guide based on the information necessary for generating an operation guide. The generation AI can learn the information necessary for generating an operation guide and generate more appropriate operation guides.

[0079] The visual aid provider can provide visual aids using a generative AI. For example, the visual aid provider can provide visual aids using a generative AI. The generative AI can provide visual aids using a text generation AI (e.g., LLM). Furthermore, the visual aid provider can also provide visual aids that include images and videos using the generative AI. For example, the generative AI can generate a video explaining the operating procedure and provide it as a visual aid. This makes the provision of visual aids more efficient by using a generative AI. For example, the generative AI takes information necessary for providing a visual aid as input and outputs a visual aid. The generative AI provides a visual aid based on the information necessary for providing a visual aid. The generative AI can learn the information necessary for providing a visual aid and provide more appropriate visual aids.

[0080] The question-answering unit can instantly answer basic questions using generative AI. For example, the question-answering unit instantly answers basic questions using generative AI. The generative AI can instantly answer basic questions using text generation AI (e.g., LLM). Furthermore, the question-answering unit can also provide answers that include images and videos using generative AI. For example, the generative AI can instantly answer frequently asked questions. This speeds up the response to basic questions by using generative AI. For example, the generative AI takes the information necessary to answer a basic question as input and outputs an answer. The generative AI provides answers based on the information necessary to answer basic questions. The generative AI can learn the information necessary to answer basic questions and provide more appropriate answers.

[0081] The real-time response unit can respond to questions in real time using a generative AI. The real-time response unit responds to questions in real time, for example, using a generative AI. The generative AI can respond to questions in real time using a text generation AI (for example, LLM). The real-time response unit can also provide answers that include images and videos using the generative AI. For example, the generative AI can answer a customer's question immediately. This makes real-time question response possible by using a generative AI. The generative AI takes information necessary for real-time question response as input and outputs an answer. The generative AI provides an answer based on the information necessary for real-time question response. The generative AI can learn the information necessary for real-time question response and provide more appropriate answers.

[0082] The learning unit can improve its responses by learning from past question data using a generative AI. For example, the learning unit uses a generative AI to learn from past question data and improve its responses. The generative AI can learn from past question data using a text generation AI (e.g., LLM) and improve its responses. Furthermore, the learning unit can also use a generative AI to learn from data including images and videos and improve its responses. For example, the generative AI can provide more appropriate answers based on past question data. This improves the accuracy of responses when using a generative AI. For example, the generative AI takes past question data as input and outputs learning results. The generative AI learns based on past question data. The generative AI learns from past question data and can provide more appropriate answers.

[0083] The operation guide generation unit can estimate the user's emotions and adjust the content of the operation guide based on the estimated emotions. For example, if the user is stressed, the operation guide generation unit's generating AI will generate a simple and intuitive operation guide. For example, if the user is relaxed, the operation guide generation unit's generating AI will generate an operation guide that includes detailed explanations. For example, if the user is in a hurry, the operation guide generation unit's generating AI will generate a short operation guide that gets straight to the point. This deepens the user's understanding by providing an operation guide that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the operation guide generation unit may be performed using AI, for example, or without AI. For example, the operation guide generation unit can input user emotion data into the generating AI and have the generating AI perform the generation of an emotion-based operation guide.

[0084] The operation guide generation unit can generate the most suitable guide by referring to the user's past operation history when generating an operation guide. For example, the operation guide generation unit's AI can generate an operation guide that prioritizes explaining functions frequently used by the user in the past. For example, the operation guide generation unit's AI can analyze the user's past operation errors and generate an operation guide that emphasizes points to watch out for. For example, the operation guide generation unit's AI can generate an operation guide that provides supplementary explanations for parts that the user previously found difficult to understand. In this way, by referring to past operation history, the system can provide the user with the most suitable operation guide. Some or all of the above-described processes in the operation guide generation unit may be performed using AI, for example, or without AI. For example, the operation guide generation unit can input the user's past operation history data into the generation AI and have the generation AI perform the generation of the most suitable operation guide.

[0085] The operation guide generation unit can generate customized operation guides according to the user's device type and settings. For example, if the user is using a smartphone, the AI ​​generates an operation guide optimized for the screen size. If the user is using a tablet, the AI ​​generates an operation guide adapted to the larger screen. If the user has enabled a specific setting, the AI ​​generates an operation guide based on that setting. This improves user convenience by providing operation guides tailored to the device type and settings. Some or all of the above processing in the operation guide generation unit may be performed using AI, for example, or without AI. For example, the operation guide generation unit can input the user's device information into the AI ​​and have the AI ​​generate a customized operation guide.

[0086] The operation guide generation unit can estimate the user's emotions and adjust the display order of the operation guide based on the estimated user emotions. For example, if the user is nervous, the generation AI in the operation guide generation unit generates an operation guide that displays important information first. For example, if the user is relaxed, the generation AI in the operation guide generation unit generates an operation guide that displays detailed information later. For example, if the user is in a hurry, the generation AI in the operation guide generation unit generates an operation guide that displays the main points first. This enhances user understanding by providing a display order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the operation guide generation unit may be performed using AI, for example, or without AI. For example, the operation guide generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the display order based on emotions.

[0087] The operation guide generation unit can include region-specific information when generating operation guides, taking into account the user's geographical location. For example, if the user is in a specific region, the AI ​​generates an operation guide that explains the region's specific settings and functions. For example, if the user is traveling, the AI ​​generates an operation guide that includes information about using the device at the travel destination. For example, if the user is in a specific country, the AI ​​generates an operation guide tailored to the language and culture of that country. By including region-specific information, the operation guide generation unit can provide users with operation guides that are appropriate for their needs. Some or all of the above-described processes in the operation guide generation unit may be performed using AI, for example, or without AI. For example, the operation guide generation unit can input the user's geographical location information into the AI ​​and have the AI ​​generate an operation guide that includes region-specific information.

[0088] The operation guide generation unit can analyze the user's social media activity and provide relevant operation guides when generating them. For example, the operation guide generation unit's AI can generate an operation guide explaining functions that the user frequently uses on social media. For example, the operation guide generation unit's AI can generate relevant operation guides based on information shared by the user on social media. For example, the operation guide generation unit can analyze the user's social media activity history and its AI can generate the optimal operation guide. In this way, by analyzing social media activity, relevant operation guides can be provided to the user. Some or all of the above processing in the operation guide generation unit may be performed using AI, for example, or without AI. For example, the operation guide generation unit can input the user's social media activity data into the generation AI and have the generation AI perform the generation of relevant operation guides.

[0089] The visual aid provider can estimate the user's emotions and adjust the format of the visual aid based on the estimated emotions. For example, if the user is stressed, the visual aid provider can provide a simple and visually easy-to-understand visual aid. For example, if the user is relaxed, the visual aid provider can provide a visual aid that includes detailed information. For example, if the user is in a hurry, the visual aid provider can provide a short, concise visual aid that gets straight to the point. By providing visual aids that match the user's emotions, the user's understanding is deepened. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visual aid provider may be performed using AI, for example, or not using AI. For example, the visual aid provider can input user emotion data into the generative AI and have the generative AI perform an adjustment of the format of the visual aid based on the emotions.

[0090] The visual aid provider can provide the most suitable visual aid by referring to the user's past viewing history when providing a visual aid. For example, the visual aid provider can provide a relevant visual aid based on the visual aid the user has viewed in the past. For example, the visual aid provider can provide a visual aid in an easy-to-understand format based on the user's past viewing history. For example, the visual aid provider can provide a visual aid that supplements the content of a visual aid the user has viewed in the past. In this way, by referring to past viewing history, the most suitable visual aid can be provided to the user. Some or all of the above processing in the visual aid provider may be performed using AI, for example, or without AI. For example, the visual aid provider can input the user's past viewing history data into a generating AI and have the generating AI perform the task of providing the most suitable visual aid.

[0091] The visual aid provider can provide visual aids optimized according to the screen size and resolution of the user's device when providing them. For example, if the user is using a smartphone, the visual aid provider will provide a visual aid optimized for the screen size. For example, if the user is using a tablet, the visual aid provider will provide a visual aid optimized for the larger screen. For example, if the user is using a high-resolution device, the visual aid provider will provide a visual aid that supports high resolution. This improves user convenience by providing visual aids that are appropriate for the device's screen size and resolution. Some or all of the above processing in the visual aid provider may be performed using AI, for example, or without AI. For example, the visual aid provider can input the user's device information into a generating AI and have the generating AI perform the task of providing optimized visual aids.

[0092] The visual aid provider can estimate the user's emotions and adjust the display order of visual aids based on the estimated user emotions. For example, if the user is nervous, the visual aid provider can provide a visual aid that displays important information first. For example, if the user is relaxed, the visual aid provider can provide a visual aid that displays detailed information later. For example, if the user is in a hurry, the visual aid provider can provide a visual aid that displays the main points first. This enhances user understanding by providing a display order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visual aid provider may be performed using AI, for example, or without AI. For example, the visual aid provider can input user emotion data into the generative AI and have the generative AI perform the emotion-based adjustment of the display order.

[0093] The visual aid provider can provide region-specific visual aids by taking into account the user's geographical location information when providing visual aids. For example, if the user is in a specific region, the visual aid provider can provide visual aids that explain the specific settings and functions of that region. For example, if the user is traveling, the visual aid provider can provide visual aids that include information about usage at the travel destination. For example, if the user is in a specific country, the visual aid provider can provide visual aids tailored to the language and culture of that country. By providing region-specific visual aids, the system can provide information that is appropriate for the user. Some or all of the above processing in the visual aid provider may be performed using AI, for example, or without AI. For example, the visual aid provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing region-specific visual aids.

[0094] The visual aid provider can analyze a user's social media activity and provide relevant visual aids when providing them. For example, the visual aid provider can provide a visual aid that explains a function the user frequently uses on social media. For example, the visual aid provider can provide relevant visual aids based on information the user has shared on social media. For example, the visual aid provider can analyze a user's social media activity history and provide the most suitable visual aid. This allows the provider to provide visual aids relevant to the user by analyzing their social media activity. Some or all of the above processing in the visual aid provider may be performed using AI, for example, or without AI. For example, the visual aid provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant visual aids.

[0095] The question-answering unit can estimate the user's emotions and adjust the way it expresses its answers based on those emotions. For example, if the user is stressed, the question-answering unit can generate an AI that provides a concise and easy-to-understand answer. If the user is relaxed, the question-answering unit can generate an AI that provides an answer that includes detailed explanations. If the user is in a hurry, the question-answering unit can generate an AI that provides a short, to-the-point answer. This deepens the user's understanding by providing answers that are appropriate to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the question-answering unit may be performed using AI, or not. For example, the question-answering unit can input user emotion data into the generative AI and have the generative AI adjust the way it expresses its answers based on those emotions.

[0096] The question-answering unit can provide the most appropriate answer by referring to the user's past question history when answering a question. For example, the question-answering unit can use a generating AI to provide relevant answers based on the content of questions the user has asked in the past. For example, the question-answering unit can use a generating AI to provide answers in an easy-to-understand format from the user's past question history. For example, the question-answering unit can use a generating AI to provide answers that supplement the content of questions the user has asked in the past. In this way, by referring to the past question history, the system can provide the user with the most appropriate answer. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's past question history data into a generating AI and have the generating AI perform the task of providing the most appropriate answer.

[0097] The question-answering unit can provide customized answers to questions based on the user's device type and settings. For example, if the user is using a smartphone, the generating AI will provide an answer optimized for the screen size. If the user is using a tablet, the generating AI will provide an answer optimized for the larger screen. If the user has enabled a specific setting, the generating AI will provide an answer based on that setting. This improves user convenience by providing answers tailored to the device type and settings. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's device information into the generating AI and have the generating AI perform the task of providing customized answers.

[0098] The question-answering unit can estimate the user's emotions and adjust the display order of answers based on the estimated emotions. For example, if the user is nervous, the generating AI in the question-answering unit can provide answers that display important information first. For example, if the user is relaxed, the generating AI in the question-answering unit can provide answers that display detailed information later. For example, if the user is in a hurry, the generating AI in the question-answering unit can provide answers that display the main points first. This enhances user understanding by providing a display order that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input user emotion data into the generating AI and have the generating AI perform the emotion-based adjustment of the display order.

[0099] The question-answering unit can provide region-specific answers by considering the user's geographical location information when answering questions. For example, if the user is in a specific region, the generating AI in the question-answering unit can provide answers regarding region-specific settings and functions. For example, if the user is traveling, the generating AI in the question-answering unit can provide answers that include information about usage at the travel destination. For example, if the user is in a specific country, the generating AI in the question-answering unit can provide answers tailored to the language and culture of that country. In this way, by providing region-specific answers, information suitable for the user can be provided. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's geographical location information into the generating AI and have the generating AI perform the task of providing region-specific answers.

[0100] The question-answering unit can analyze the user's social media activity and provide relevant answers when answering questions. For example, the question-answering unit can use a generating AI to provide answers regarding features the user frequently uses on social media. For example, the question-answering unit can use a generating AI to provide relevant answers based on information the user has shared on social media. For example, the question-answering unit can use a generating AI to provide optimal answers by analyzing the user's social media activity history. In this way, by analyzing social media activity, it is possible to provide answers relevant to the user. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant answers.

[0101] The real-time response unit can estimate the user's emotions and adjust the content of the real-time response based on the estimated emotions. For example, if the user is stressed, the real-time response unit can generate a concise and easy-to-understand real-time response using a generating AI. For example, if the user is relaxed, the real-time response unit can generate a real-time response that includes detailed explanations using a generating AI. For example, if the user is in a hurry, the real-time response unit can generate a short, to-the-point real-time response using a generating AI. This deepens the user's understanding by providing real-time responses that are tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the real-time response unit may be performed using AI, for example, or without AI. For example, the real-time response unit can input user emotion data into the generating AI and have the generating AI adjust the content of the real-time response based on the emotion.

[0102] The real-time response unit can provide the optimal response by referring to the user's past real-time question history during real-time response. For example, the real-time response unit can generate a relevant response based on the content of questions the user has asked in real time in the past. For example, the real-time response unit can generate a response in an easy-to-understand format from the user's past real-time question history. For example, the real-time response unit can generate a response that supplements the content of questions the user has asked in real time in the past. In this way, the system can provide the user with the optimal response by referring to the past real-time question history. Some or all of the above processing in the real-time response unit may be performed using AI, for example, or without AI. For example, the real-time response unit can input the user's past real-time question history data into the generation AI and have the generation AI perform the task of providing the optimal response.

[0103] The real-time response unit can provide customized responses in real time, depending on the user's device type and settings. For example, if the user is using a smartphone, the generating AI provides a response optimized for the screen size. If the user is using a tablet, the generating AI provides a response optimized for the larger screen. If the user has enabled a specific setting, the generating AI provides a response based on that setting. This improves user convenience by providing responses tailored to the device type and settings. Some or all of the above processing in the real-time response unit may be performed using AI, for example, or without AI. For example, the real-time response unit can input the user's device information into the generating AI and have the generating AI perform the task of providing a customized response.

[0104] The real-time response unit can estimate the user's emotions and adjust the display order of real-time responses based on the estimated user emotions. For example, if the user is nervous, the real-time response unit provides a generating AI that displays important information first in the real-time response. For example, if the user is relaxed, the real-time response unit provides a generating AI that displays detailed information later in the real-time response. For example, if the user is in a hurry, the real-time response unit provides a generating AI that displays the main points first in the real-time response. This enhances user understanding by providing a display order that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the real-time response unit may be performed using AI, for example, or without AI. For example, the real-time response unit can input user emotion data into the generating AI and have the generating AI perform an adjustment of the display order based on emotions.

[0105] The real-time response unit can provide region-specific responses by taking into account the user's geographical location information during real-time responses. For example, if the user is in a specific region, the generating AI in the real-time response unit can provide responses regarding region-specific settings and functions. For example, if the user is traveling, the generating AI in the real-time response unit can provide responses that include information about usage at the travel destination. For example, if the user is in a specific country, the generating AI in the real-time response unit can provide responses tailored to the language and culture of that country. In this way, by providing region-specific responses, information suitable for the user can be provided. Some or all of the above processing in the real-time response unit may be performed using AI, for example, or without AI. For example, the real-time response unit can input the user's geographical location information into the generating AI and have the generating AI perform the provision of region-specific responses.

[0106] The real-time response unit can analyze a user's social media activity and provide relevant responses in real time. For example, the real-time response unit can generate AI responses regarding features that the user frequently uses on social media. For example, the real-time response unit can generate AI responses based on information shared by the user on social media. For example, the real-time response unit can analyze a user's social media activity history and generate AI responses to provide the most appropriate responses. This allows the system to provide user-relevant responses by analyzing social media activity. Some or all of the above-described processes in the real-time response unit may be performed using AI, for example, or without AI. For example, the real-time response unit can input user social media activity data into the generation AI and have the generation AI provide relevant responses.

[0107] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit's generating AI will select concise and easy-to-understand training data. If the user is relaxed, the learning unit's generating AI will select training data that includes detailed explanations. If the user is in a hurry, the learning unit's generating AI will select short, to the point. This improves the effectiveness of learning by providing training data that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into the generating AI and have the generating AI perform the selection of training data based on emotions.

[0108] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit's generating AI selects the optimal learning algorithm based on past learning data. For example, the learning unit's generating AI selects an effective learning method from past learning data. For example, the learning unit analyzes past learning data and optimizes the learning algorithm. This improves the accuracy of the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into the generating AI and have the generating AI perform the optimization of the learning algorithm.

[0109] The learning unit can customize the learning data during training according to the user's device type and settings. For example, if the user is using a smartphone, the learning unit generates AI-generated learning data optimized for the screen size. If the user is using a tablet, the learning unit generates AI-generated learning data optimized for the larger screen. If the user has enabled a specific setting, the learning unit generates AI-generated learning data based on that setting. This improves user convenience by providing learning data tailored to the device type and settings. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's device information into the generating AI and have the generating AI provide customized learning data.

[0110] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit reduces the learning frequency. For example, if the user is relaxed, the learning unit increases the learning frequency. For example, if the user is in a hurry, the learning unit adjusts the learning frequency. This improves the effectiveness of learning by providing a learning frequency that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the learning frequency.

[0111] The learning unit can use region-specific learning data, taking into account the user's geographical location information, during the learning process. For example, if the user is in a specific region, the learning unit can generate learning data containing region-specific information using the AI. For example, if the user is traveling, the learning unit can generate learning data containing information about usage at the travel destination using the AI. For example, if the user is in a specific country, the learning unit can generate learning data tailored to the language and culture of that country using the AI. This allows the learning unit to provide information that is appropriate for the user by providing region-specific learning data. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into the generating AI and have the generating AI perform the task of providing region-specific learning data.

[0112] The learning unit can analyze the user's social media activity during training and use relevant training data. For example, the learning unit can generate AI to provide training data about features the user frequently uses on social media. For example, the learning unit can generate AI to provide relevant training data based on information the user has shared on social media. For example, the learning unit can analyze the user's social media activity history and generate AI to provide optimal training data. This allows the learning unit to provide training data relevant to the user by analyzing social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity data into the generating AI and have the generating AI provide relevant training data.

[0113] The learning unit can provide optimal learning data during learning, taking into account the user's health condition. For example, if the user is tired, the learning unit's generating AI provides concise and easy-to-understand learning data. If the user is relaxed, the learning unit's generating AI provides learning data that includes detailed explanations. If the user is in a hurry, the learning unit's generating AI provides short, to-the-point learning data. This improves the effectiveness of learning by providing learning data tailored to the user's health condition. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user health condition data into the generating AI and have the generating AI provide optimal learning data.

[0114] The learning unit can provide optimal learning data by referring to the user's past learning history during the learning process. For example, the learning unit can generate AI to provide relevant learning data based on what the user has learned in the past. For example, the learning unit can generate AI to provide learning data in an easy-to-understand format from the user's past learning history. For example, the learning unit can generate AI to provide learning data that supplements what the user has learned in the past. In this way, by referring to the past learning history, the learning unit can provide the user with optimal learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past learning history data into the generating AI and have the generating AI perform the task of providing optimal learning data.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The AssistMate system can estimate the user's emotions and adjust the content of the operation guide based on those emotions. For example, if the user is stressed, it can provide a simple and intuitive operation guide. If the user is relaxed, it can provide an operation guide with detailed explanations. Furthermore, if the user is in a hurry, it can provide a concise operation guide that gets straight to the point. By providing operation guides that are tailored to the user's emotions, the system deepens the user's understanding and improves their satisfaction. Emotion estimation is achieved using an emotion engine or generative AI. The generative AI can be a text generation AI or a multimodal generation AI, among others. The operation guide generation unit inputs the user's emotion data into the generative AI and performs the generation of an operation guide based on those emotions.

[0117] The AssistMate system can generate optimal operation guides by referring to the user's past operation history. For example, it can provide operation guides that prioritize explaining functions the user has frequently used in the past. It can also analyze the user's past operation errors and provide operation guides that highlight points to be careful about. Furthermore, it can provide operation guides that supplement explanations of parts that the user previously found difficult to understand. In this way, by referring to past operation history, the system can provide the user with the most suitable operation guide, deepening the user's understanding. The operation guide generation unit can input the user's past operation history data into a generation AI and have it perform the generation of the optimal operation guide.

[0118] The AssistMate system can generate customized operation guides according to the user's device type and settings. For example, if the user is using a smartphone, it can provide an operation guide optimized for the screen size. If the user is using a tablet, it can provide an operation guide adapted to the larger screen. Furthermore, if the user has enabled specific settings, it can provide an operation guide based on those settings. This improves user convenience by providing operation guides tailored to the device type and settings. The operation guide generation unit inputs the user's device information into the generation AI and executes the generation of a customized operation guide.

[0119] The AssistMate system can generate operation guides that include region-specific information, taking into account the user's geographical location. For example, if the user is in a specific region, it can provide an operation guide that explains the region's specific settings and functions. If the user is traveling, it can provide an operation guide that includes information about using the system at their travel destination. Furthermore, if the user is in a specific country, it can provide an operation guide tailored to the language and culture of that country. This allows the system to provide users with information relevant to their needs by offering operation guides that include region-specific information. The operation guide generation unit inputs the user's geographical location information into the generation AI, which then generates an operation guide that includes region-specific information.

[0120] The AssistMate system can analyze a user's social media activity and provide relevant operation guides. For example, it can provide operation guides that explain functions frequently used by the user on social media. It can also provide relevant operation guides based on information shared by the user on social media. Furthermore, it can analyze a user's social media activity history and provide the most suitable operation guide. In this way, by analyzing social media activity, it can provide operation guides relevant to the user. The operation guide generation unit can input the user's social media activity data into a generation AI and have it perform the generation of relevant operation guides.

[0121] The AssistMate system can estimate the user's emotions and adjust the format of visual aids based on those emotions. For example, if the user is stressed, it can provide simple and visually easy-to-understand visual aids. If the user is relaxed, it can provide visual aids containing detailed information. Furthermore, if the user is in a hurry, it can provide short, concise visual aids that get straight to the point. By providing visual aids that match the user's emotions, the system deepens the user's understanding and improves their satisfaction. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI can include text generation AI and multimodal generation AI. The visual aid provider inputs the user's emotion data into the generative AI and performs the adjustment of the visual aid format based on those emotions.

[0122] The AssistMate system can provide optimal visual aids by referencing the user's past viewing history. For example, it can provide relevant visual aids based on visual aids the user has viewed in the past. It can also provide visual aids in an easy-to-understand format based on the user's past viewing history. Furthermore, it can provide visual aids that supplement the content of visual aids the user has viewed in the past. As a result, by referring to past viewing history, the system can provide the user with the most suitable visual aids, deepening their understanding. The visual aid provisioning unit can input the user's past viewing history data into a generating AI and have it perform the task of providing the optimal visual aids.

[0123] The AssistMate system can provide visual aids optimized according to the screen size and resolution of the user's device. For example, if the user is using a smartphone, it can provide visual aids optimized for the screen size. If the user is using a tablet, it can provide visual aids optimized for the larger screen. Furthermore, if the user is using a high-resolution device, it can provide visual aids that support high resolution. This improves user convenience by providing visual aids that are appropriate for the device's screen size and resolution. The visual aid provisioning unit inputs the user's device information into a generating AI and executes the provision of optimized visual aids.

[0124] The AssistMate system can estimate the user's emotions and adjust the display order of visual aids based on those emotions. For example, if the user is nervous, it can provide visual aids that display important information first. If the user is relaxed, it can provide visual aids that display detailed information later. Furthermore, if the user is in a hurry, it can provide visual aids that display the main points first. By providing a display order that matches the user's emotions, the system deepens the user's understanding and improves their satisfaction. Emotion estimation is achieved using an emotion engine or generative AI. The generative AI can be a text generation AI or a multimodal generation AI, among others. The visual aid provider inputs the user's emotion data into the generative AI and performs the adjustment of the display order based on emotions.

[0125] The AssistMate system can estimate the user's emotions and adjust the way questions are phrased based on those emotions. For example, if the user is stressed, it can provide a concise and easy-to-understand answer. If the user is relaxed, it can provide an answer that includes detailed explanations. Furthermore, if the user is in a hurry, it can provide a short, to-the-point answer. By providing answers that are tailored to the user's emotions, the system deepens the user's understanding and improves their satisfaction. Emotion estimation is achieved using an emotion engine or generative AI. The generative AI can be a text generation AI or a multimodal generation AI, among others. The question answering unit can input the user's emotion data into the generative AI and have it adjust the way answers are phrased based on those emotions.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The operation guide generation unit generates an operation guide. For example, it can generate an operation guide using a generation AI, or it can generate an operation guide using a text generation AI (e.g., LLM). It can also generate an operation guide that includes images and videos using a generation AI. For example, the generation AI can generate a video explaining the operation procedure and provide it as an operation guide. Step 2: The Visual Aid Provider Unit provides the operation guide generated by the Operation Guide Generation Unit as a Visual Aid. For example, it is also possible to provide a Visual Aid using a generation AI, and to provide a Visual Aid that includes images and videos. For example, the Visual Aid Provider Unit generates a video explaining the operation procedure and provides it as a Visual Aid. Step 3: The question-answering unit provides immediate answers to basic questions. For example, it can use generative AI to provide immediate answers to basic questions, or it can use text-generating AI (e.g., LLM) to provide immediate answers to basic questions. For example, the question-answering unit can provide immediate answers to frequently asked questions. Step 4: The real-time response unit responds to questions in real time. For example, it can respond to questions in real time using generative AI, or it can respond to questions in real time using text generation AI (e.g., LLM). For example, the real-time response unit can answer customer questions immediately. Step 5: The learning unit learns from past question data and improves its responses. For example, it can use a generative AI to learn from past question data and improve its responses. It can also use a generative AI, such as a text generation AI (e.g., LLM), to learn from past question data and improve its responses. For example, the learning unit can provide more appropriate answers based on past question data.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the operation guide generation unit, visual aid provision unit, question answering unit, real-time response unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the operation guide generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The visual aid provision unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The question answering unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The real-time response unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the operation guide generation unit, visual aid provision unit, question answering unit, real-time response unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the operation guide generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The visual aid provision unit is implemented by the output device 40 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The question answering unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The real-time response unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the operation guide generation unit, visual aid provision unit, question answering unit, real-time response unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the operation guide generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The visual aid provision unit is implemented by the output device 40 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The question answering unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The real-time response unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Each of the multiple elements described above, including the operation guide generation unit, visual aid provision unit, question answering unit, real-time response unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the operation guide generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The visual aid provision unit is implemented by the output device 40 of the robot 414 or the specific processing unit 290 of the data processing unit 12. The question answering unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The real-time response unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] (Note 1) An operation guide generation unit that generates operation guides, A visual aid providing unit provides the operation guide generated by the operation guide generation unit as a visual aid, A question answering section that provides immediate answers to basic questions, A real-time response unit that handles questions in real time, It comprises a learning unit that learns from past question data and improves its responses. A system characterized by the following features. (Note 2) The aforementioned operation guide generation unit is: Generate an operation guide using a generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned Visual Aid Provisioning Unit, Provides visual aids using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned question answering unit is Generative AI provides instant answers to basic questions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The real-time response unit is AI generates responses to questions in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, Generative AI learns from past question data and improves responses. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned operation guide generation unit is: The system estimates the user's emotions and adjusts the content of the user guide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned operation guide generation unit is: When generating an operation guide, the system references the user's past operation history to generate the most suitable guide. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned operation guide generation unit is: When generating an operation guide, a customized guide is generated according to the user's device type and settings. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned operation guide generation unit is: It estimates the user's emotions and adjusts the display order of the operation guide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned operation guide generation unit is: When generating the user guide, take the user's geographical location into consideration and include region-specific information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned operation guide generation unit is: When generating user guides, the system analyzes the user's social media activity and provides relevant user guides. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned Visual Aid Provisioning Unit, It estimates the user's emotions and adjusts the format of the visual aids based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned Visual Aid Provisioning Unit, When providing visual aids, the system refers to the user's past viewing history to provide the most suitable visual aids. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned Visual Aid Provisioning Unit, When providing visual aids, we provide visual aids optimized according to the screen size and resolution of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned Visual Aid Provisioning Unit, It estimates the user's emotions and adjusts the display order of visual aids based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned Visual Aid Provisioning Unit, When providing visual aids, the system takes into account the user's geographical location to provide region-specific visual aids. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned Visual Aid Provisioning Unit, When providing visual aids, we analyze the user's social media activity and provide relevant visual aids. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned question answering unit 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 20) The aforementioned question answering unit is When answering questions, the system provides the most appropriate answer by referring to the user's past question history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned question answering unit is When answering questions, provide customized answers based on the user's device type and settings. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned question answering unit is The system estimates the user's emotions and adjusts the display order of responses based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned question answering unit is When answering questions, the system takes the user's geographical location into account to provide region-specific answers. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned question answering unit is When answering questions, the system analyzes the user's social media activity and provides relevant answers. The system described in Appendix 1, characterized by the features described herein. (Note 25) The real-time response unit is It estimates the user's emotions and adjusts the content of the response in real time based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The real-time response unit is During real-time responses, the system provides the optimal response by referring to the user's past real-time question history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The real-time response unit is During real-time responses, the system provides customized responses based on the user's device type and settings. The system described in Appendix 1, characterized by the features described herein. (Note 28) The real-time response unit is It estimates the user's emotions and adjusts the display order of real-time responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The real-time response unit is When providing real-time responses, the system takes the user's geographical location into account to provide region-specific responses. The system described in Appendix 1, characterized by the features described herein. (Note 30) The real-time response unit is During real-time responses, the system analyzes the user's social media activity and provides relevant responses. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned learning unit, During training, the training data is customized according to the user's device type and settings. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned learning unit, During training, region-specific training data is used, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned learning unit, During training, the system analyzes users' social media activity and uses relevant training data. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned learning unit, During training, the system provides optimal training data while considering the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned learning unit, During training, the system provides optimal training data by referencing the user's past training history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0200] 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. An operation guide generation unit that generates operation guides, A visual aid providing unit provides the operation guide generated by the operation guide generation unit as a visual aid, A question answering section that provides immediate answers to basic questions, A real-time response unit that handles questions in real time, It comprises a learning unit that learns from past question data and improves its responses. A system characterized by the following features.

2. The aforementioned operation guide generation unit, Generate an operation guide using AI. The system according to feature 1.

3. The aforementioned Visual Aid Provisioning Unit, Provides visual aids using generative AI. The system according to feature 1.

4. The aforementioned question answering unit is Generative AI provides instant answers to basic questions. The system according to feature 1.

5. The real-time response unit is AI-generated answers to questions in real time. The system according to feature 1.

6. The aforementioned learning unit, Generative AI learns from past question data and improves responses. The system according to feature 1.

7. The aforementioned operation guide generation unit, The system estimates the user's emotions and adjusts the content of the user guide based on those estimated emotions. The system according to feature 1.

8. The aforementioned operation guide generation unit, When generating an operation guide, the system references the user's past operation history to generate the most suitable guide. The system according to feature 1.

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