system

Abstract

The system according to this embodiment aims to enable easy device configuration and troubleshooting even without technical knowledge. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a setting unit, and a guide unit. The reception unit receives instructions from the user. The analysis unit analyzes the instructions received by the reception unit. The setting unit performs settings based on the instructions analyzed by the analysis unit. The guide unit guides the user based on the settings performed by the setting unit.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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, the settings and troubleshooting of devices are complex, which is a major obstacle especially for users who are not familiar with technology.

[0005] The system according to the embodiment aims to enable easy device settings and troubleshooting even without technical knowledge.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a setting unit, and a guide unit. The reception unit receives instructions from the user. The analysis unit analyzes the instructions received by the reception unit. The setting unit performs settings based on the instructions analyzed by the analysis unit. The guide unit guides the user based on the settings performed by the setting unit. [Effects of the Invention]

[0007] The system according to this embodiment makes it easy to configure and troubleshoot devices even without technical knowledge. [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, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[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 3 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​assistant app "SmartEase" according to an embodiment of the present invention is a system that allows users to easily configure and troubleshoot devices. When a user gives instructions at a conversational level, such as "I want to display it on the TV" or "I want to connect the earphones in the picture," the AI ​​understands the user's intent and automatically performs the necessary settings. Furthermore, an interactive guide provides step-by-step assistance to the user, supporting configuration and troubleshooting. For example, if a user says "I want to display it on the TV," the AI ​​understands the user's intent and automatically configures the connection between the TV and the device. Next, the interactive guide provides step-by-step instructions. For example, when configuring wireless earphones, the AI ​​gives instructions such as "Turn on the earphones" or "Open the wireless settings on the device," and the user completes the setup by following these instructions. This mechanism makes it easy to operate devices even without technical knowledge. For example, even elderly people or children unfamiliar with technology can configure and troubleshoot devices simply by following the AI's instructions. Additionally, problem solving is rapid, saving users time. For example, there is no need to search the internet or contact experts, allowing problems to be solved quickly. Furthermore, it reduces the resources required for IT support and improves the operational efficiency of businesses. For example, even companies without an IT support department can use AI assistants to enable employees to set up devices and troubleshoot problems themselves. This reduces the cost and time spent on IT support and improves the operational efficiency of businesses. Ultimately, it contributes to bridging the digital divide by providing an environment where people unfamiliar with technology can use it with confidence. For example, when the elderly and children become proficient in using devices, the digital divide is bridged, and everyone can benefit from technology. In this way, the AI ​​assistant app "SmartEase" can significantly reduce the burden on users and provide quick and efficient problem solving.

[0029] The AI ​​assistant application "SmartEase" according to this embodiment comprises a reception unit, an analysis unit, a settings unit, and a guide unit. The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit receives the user's voice instructions using, for example, voice recognition technology. The reception unit can also provide an interface for receiving text input. Furthermore, the reception unit can also receive the user's gesture instructions using gesture recognition technology. For example, the reception unit converts the user's voice instructions into text using voice recognition technology and sends it to the analysis unit. The text input interface allows the user to input instructions using a keyboard or touchscreen. Gesture recognition technology recognizes the user's hand movements and facial expressions and interprets them as instructions. The analysis unit analyzes the instructions received by the reception unit. The analysis unit analyzes the user's instructions using, for example, natural language processing technology. The analysis unit can also analyze the user's instructions using image recognition technology. Furthermore, the analysis unit can use intent understanding technology to understand the user's intentions. For example, the analysis unit uses natural language processing technology to analyze and understand the user's voice instructions. Image recognition technology analyzes and understands the content of images and videos sent by the user. Intent understanding technology analyzes the user's speech and behavioral patterns to infer the user's intentions. The configuration unit performs settings based on the instructions analyzed by the analysis unit. The configuration unit uses, for example, automatic configuration technology to automatically configure the device. The configuration unit can also automatically configure applications. Furthermore, the configuration unit can also automatically configure the network. For example, the configuration unit automatically performs the initial setup of the device so that the user can use the device. Application configuration automatically configures applications installed by the user. Network configuration automatically configures the settings for connecting the user's device to the network. The guide unit guides the user based on the settings made by the configuration unit. The guide unit provides, for example, voice guidance. The guide unit can also provide visual guidance.Furthermore, the guide unit can also provide text guides. For example, the guide unit can use voice guidance to guide the user through the next steps. Visual guides guide the user using images or videos displayed on the screen. Text guides guide the user using text displayed on the screen. As a result, the AI ​​assistant app "SmartEase" according to this embodiment can easily perform device setup and troubleshooting by receiving, analyzing, configuring, and guiding the user based on user instructions.

[0030] The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit can, for example, receive voice instructions using speech recognition technology. Speech recognition technology uses a combination of noise cancellation and speech filtering technologies to convert the user's voice into text with high accuracy. This allows for accurate reception of voice instructions even in noisy environments. The reception unit can also provide an interface for receiving text input. The text input interface allows users to enter instructions using a keyboard or touchscreen. This allows users to easily enter instructions even in situations where voice input is difficult. Furthermore, the reception unit can also receive gesture instructions using gesture recognition technology. Gesture recognition technology uses cameras and sensors to detect the user's hand movements and facial expressions in real time and interpret them as instructions. For example, if a user waves their hand, the reception unit recognizes the movement and sends it to the analysis unit as a specific command. This allows the user to use the app with intuitive operation. The reception unit integrates these diverse input methods to maximize user convenience. For example, by combining voice commands and gesture commands, more complex operations can be performed easily. Furthermore, the reception unit has a function that learns the user's input history and predicts future inputs. This makes user operation smoother and improves the usability of the app.

[0031] The analysis unit analyzes the instructions received by the reception unit. For example, the analysis unit analyzes user instructions using natural language processing technology. Natural language processing technology analyzes the user's voice and text instructions and goes through processes such as morphological analysis, grammatical analysis, and semantic analysis to understand their content. This allows for an accurate understanding of the user's intent. The analysis unit can also analyze user instructions using image recognition technology. Image recognition technology analyzes images and videos sent by the user and uses techniques such as object detection, face recognition, and scene analysis to understand their content. For example, if a user sends an image taken with a camera, the analysis unit analyzes the image and recognizes specific objects or situations. Furthermore, the analysis unit can also use intent understanding technology to understand the user's intent. Intent understanding technology analyzes the user's statements and behavioral patterns and uses machine learning algorithms and deep learning models to infer the user's intent. For example, if a user says, "Tell me the weather for tomorrow," the analysis unit analyzes the statement and understands the user's intention to know the weather forecast. This allows the analysis unit to accurately analyze diverse user instructions and generate appropriate responses. Furthermore, the analysis unit also has the function to learn past instruction history and user behavior patterns to predict future instructions. This results in smoother user operation and improved usability of the app.

[0032] The configuration unit performs settings based on instructions analyzed by the analysis unit. For example, the configuration unit uses automatic configuration technology to automatically configure devices. This automatic configuration technology utilizes preset profiles and user configuration history to automatically perform initial setup and customization of devices based on user instructions. This allows users to use the device immediately without performing complex configuration tasks. The configuration unit can also automatically configure applications. For application configuration, it refers to application configuration files and user configuration history to automatically configure applications installed by the user. For example, when a user installs a new application, the configuration unit automatically configures that application so that the user can use it immediately. Furthermore, the configuration unit can also automatically configure networks. For network configuration, it refers to network profiles and connection history to automatically configure the user's device to connect to the network. For example, when a user tries to connect to a new Wi-Fi network, the configuration unit automatically configures that network so that the user can connect to the internet immediately. In this way, the configuration unit automatically configures devices, applications, and networks based on user instructions, improving user convenience. Furthermore, the settings section also has a function that learns the user's setting history and predicts future settings. This makes user operation smoother and improves the usability of the app.

[0033] The guide unit guides the user based on the settings made by the settings unit. The guide unit can, for example, provide voice guidance. Voice guidance uses synthesized speech technology to guide the user through the next steps. Synthesized speech technology uses a speech synthesis engine and a speech database to generate speech with natural pronunciation and intonation. This allows the user to easily understand the next steps while listening to the voice guidance. The guide unit can also provide visual guidance. Visual guidance uses graphic design and animation technology to guide the user using images and videos displayed on the screen. For example, when a user is setting up a device, the visual guide uses images and videos displayed on the screen to visually explain each step. This allows the user to proceed with the setup work smoothly while referring to the visual information. Furthermore, the guide unit can also provide text guidance. Text guidance uses text generation and formatting technology to guide the user using text displayed on the screen. For example, when a user is setting up an application, the text guide uses text displayed on the screen to explain each step in detail. This allows the user to perform the setup work accurately while referring to the text guide. The guide unit integrates these diverse guiding methods to maximize user convenience. For example, combining voice and visual guides makes more complex operations easier. Furthermore, the guide unit learns the user's operation history and predicts future guidance. This makes user operation smoother and improves the usability of the app.

[0034] The analysis unit includes a natural language processing unit that performs natural language processing. The natural language processing unit can, for example, perform morphological analysis. The natural language processing unit can, for example, perform grammatical analysis. The natural language processing unit can, for example, perform semantic analysis. For example, the natural language processing unit uses morphological analysis to break down the user's voice instructions into word units and analyzes their meaning. Grammatical analysis analyzes the grammatical structure of the user's voice instructions and understands their meaning. Semantic analysis analyzes the meaning of the user's voice instructions and understands their intent. As a result, by performing natural language processing, the user's instructions can be accurately analyzed.

[0035] The analysis unit includes an image recognition unit that performs image recognition. The image recognition unit can, for example, perform object recognition. The image recognition unit can, for example, perform face recognition. The image recognition unit can, for example, perform scene analysis. For example, the image recognition unit uses object recognition to recognize objects contained in images sent by the user and analyzes their contents. Face recognition recognizes faces contained in images sent by the user and identifies the person. Scene analysis analyzes the scene contained in images sent by the user and understands the situation. As a result, by performing image recognition, the user's instructions can be accurately analyzed.

[0036] The analysis unit includes an intent understanding unit that understands the user's intent. The intent understanding unit can, for example, analyze the content of the user's statements. The intent understanding unit can, for example, analyze the user's behavior patterns. The intent understanding unit can, for example, analyze the user's context. For example, the intent understanding unit analyzes the content of the user's statements and understands their intent. The behavior pattern analysis infers the current intent based on the user's past behavior data. The context analysis understands the intent by considering the user's current situation and environment. As a result, by understanding the user's intent, instructions can be accurately analyzed.

[0037] The configuration unit includes an automatic configuration unit that performs configuration automatically. The automatic configuration unit can, for example, perform the initial setup of a device. The automatic configuration unit can, for example, perform the automatic configuration of an application. The automatic configuration unit can, for example, perform the automatic configuration of a network. For example, the automatic configuration unit automatically performs the initial setup of a device so that the user can use the device. Automatic application configuration automatically configures applications installed by the user. Automatic network configuration automatically configures the user's device to connect to the network. In this way, the burden on the user can be reduced by automating the configuration.

[0038] The reception desk can analyze the user's past instruction history and select the optimal reception method. For example, the reception desk can automatically display instructions that the user has frequently given in the past as suggestions. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest instructions to be used during a specific time period based on the user's past instruction history. In this way, the optimal reception method can be selected by analyzing the user's past instruction history.

[0039] The reception system can filter instructions based on the user's current situation and environment. For example, if the user is in a meeting, the reception system might suppress voice input and prioritize text input. If the user is on the go, the reception system might only accept simple instructions. If the user is at home, the reception system might prioritize instructions related to devices within the home. This allows for more appropriate instruction reception by filtering instructions based on the user's current situation and environment.

[0040] The reception desk can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception desk will prioritize instructions related to that location. For example, if the user is traveling, the reception desk will prioritize instructions related to travel. For example, if the user is at home, the reception desk will prioritize instructions related to devices within the home. In this way, by considering the user's geographical location, the reception desk can prioritize receiving instructions that are highly relevant.

[0041] The reception desk can analyze the user's social media activity when receiving instructions and receive relevant instructions. For example, if the reception desk is talking about a specific topic on social media, it will prioritize receiving instructions related to that topic. For example, if the reception desk is participating in a specific event on social media, it will prioritize receiving instructions related to that event. For example, if the reception desk is talking about a specific device on social media, it will prioritize receiving instructions related to that device. In this way, by analyzing the user's social media activity, it is possible to receive relevant instructions.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the instruction. For example, the analysis unit performs a detailed analysis for instructions of high urgency. For example, the analysis unit performs a standard analysis for instructions of normal importance. For example, the analysis unit performs a simplified analysis for instructions of low importance. By adjusting the level of detail of the analysis based on the importance of the instruction, more appropriate instruction analysis becomes possible.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the instruction when analyzing the instruction. For example, the analysis unit applies a configuration-specific analysis algorithm to instructions related to device settings. For example, the analysis unit applies a troubleshooting-specific analysis algorithm to instructions related to troubleshooting. For example, the analysis unit applies an FAQ-specific analysis algorithm to instructions related to general questions. By applying different analysis algorithms depending on the category of the instruction, more appropriate instruction analysis becomes possible.

[0044] The analysis unit can determine the priority of instructions based on when they were submitted. For example, it will prioritize highly urgent instructions regardless of when they were submitted. For example, it will prioritize normal instructions based on when they were submitted. For example, it will prioritize instructions of low importance based on when they were submitted. By determining the priority of instructions based on when they were submitted, more appropriate instruction analysis becomes possible.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the instructions. For example, the analysis unit prioritizes the analysis of highly relevant instructions. For example, the analysis unit postpones the analysis of less relevant instructions. For example, the analysis unit performs a standard order of analysis for instructions with a moderate level of relevance. By adjusting the order of analysis based on the relevance of the instructions, more appropriate instruction analysis becomes possible.

[0046] The configuration unit can analyze the user's past configuration history to select the optimal configuration method during setup. For example, the configuration unit can suggest the optimal configuration method based on the user's past settings. For example, the configuration unit can predict and suggest settings to be used during a specific time period based on the user's past configuration history. For example, the configuration unit can analyze the user's past configuration history to suggest the most efficient configuration method. In this way, the optimal configuration method can be selected by analyzing the user's past configuration history.

[0047] The settings unit can customize the settings based on the user's current situation during setup. For example, if the user is in a meeting, the settings unit will suppress audio settings and prioritize silent mode. If the user is on the go, for example, the settings unit will prioritize only simple settings. If the user is at home, for example, the settings unit will prioritize settings related to devices within the home. This allows for more appropriate settings by customizing the settings based on the user's current situation.

[0048] The settings unit can select the optimal settings method by considering the user's geographical location during setup. For example, if the user is in a specific location, the settings unit will prioritize settings related to that location. For example, if the user is traveling, the settings unit will prioritize settings related to travel. For example, if the user is at home, the settings unit will prioritize settings related to devices within the home. In this way, the optimal settings method can be selected by considering the user's geographical location.

[0049] The settings unit can analyze the user's social media activity during setup and suggest appropriate settings. For example, if the user is discussing a specific topic on social media, the settings unit will prioritize settings related to that topic. For example, if the user is participating in a specific event on social media, the settings unit will prioritize settings related to that event. For example, if the user is discussing a specific device on social media, the settings unit will prioritize settings related to that device. In this way, by analyzing the user's social media activity, the system can suggest the most suitable settings.

[0050] The guide unit can select the optimal display method by referring to the user's past operation history when displaying guides. For example, the guide unit may prioritize providing display methods that the user has frequently used in the past. For example, the guide unit may suggest the optimal display method for a specific operation based on the user's past operation history. For example, the guide unit may analyze the user's past operation history and provide the most efficient display method. In this way, the optimal guide display method can be selected by referring to the user's past operation history.

[0051] The guide unit can customize the means of guidance based on the user's current situation when displaying guidance. For example, if the user is in a meeting, the guide unit will suppress audio guidance and prioritize text guidance. For example, if the user is on the move, the guide unit will prioritize only simple guidance. For example, if the user is at home, the guide unit will prioritize guidance related to devices in the home. This allows for more appropriate guidance display by customizing the means of guidance based on the user's current situation.

[0052] The guide unit can select the optimal display method when displaying guides, taking into account the user's device information. For example, if the user is using a smartphone, the guide unit provides a display method that matches the screen size. For example, if the user is using a tablet, the guide unit provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the guide unit provides a concise and highly visible display method. In this way, the optimal guide display method can be selected by taking into account the user's device information.

[0053] The guide unit can analyze the user's social media activity when displaying guides and suggest appropriate guidance methods. For example, if the user is discussing a specific topic on social media, the guide unit will prioritize providing guides related to that topic. For example, if the user is participating in a specific event on social media, the guide unit will prioritize providing guides related to that event. For example, if the user is discussing a specific device on social media, the guide unit will prioritize providing guides related to that device. In this way, by analyzing the user's social media activity, the guide unit can suggest the most appropriate guidance method.

[0054] The natural language processing unit can analyze the user's past utterance history to select the optimal processing method during natural language processing. For example, the natural language processing unit may prioritize processing phrases that the user has frequently used in the past. For example, the natural language processing unit may predict and process phrases that the user will use at a specific time period based on the user's past utterance history. For example, the natural language processing unit may analyze the user's past utterance history and propose the most efficient language processing method. In this way, the optimal natural language processing method can be selected by analyzing the user's past utterance history.

[0055] The natural language processing unit can customize its processing methods based on the user's current situation during natural language processing. For example, if the user is in a meeting, the natural language processing unit may suppress voice input and prioritize text input. If the user is on the go, for example, the natural language processing unit may prioritize only simple language processing. If the user is at home, for example, the natural language processing unit may prioritize language processing related to devices in the home. This allows for more appropriate natural language processing by customizing the processing methods based on the user's current situation.

[0056] The natural language processing unit can select the optimal processing method by considering the user's geographical location during natural language processing. For example, if the user is in a specific location, the natural language processing unit will prioritize language processing related to that location. For example, if the user is traveling, the natural language processing unit will prioritize language processing related to travel. For example, if the user is at home, the natural language processing unit will prioritize language processing related to devices within the home. In this way, the optimal natural language processing method can be selected by considering the user's geographical location.

[0057] The natural language processing unit can analyze a user's social media activity during natural language processing and suggest processing methods accordingly. For example, if a user is discussing a specific topic on social media, the natural language processing unit will prioritize language processing related to that topic. For example, if a user is participating in a specific event on social media, the natural language processing unit will prioritize language processing related to that event. For example, if a user is discussing a specific device on social media, the natural language processing unit will prioritize language processing related to that device. In this way, by analyzing a user's social media activity, the optimal natural language processing method can be suggested.

[0058] The image recognition unit can analyze the user's past image history to select the optimal recognition method during image recognition. For example, the image recognition unit can propose the optimal recognition method based on images the user has frequently taken in the past. For example, the image recognition unit can predict and recognize images taken during a specific time period based on the user's past image history. For example, the image recognition unit can analyze the user's past image history and propose the most efficient image recognition method. In this way, the optimal image recognition method can be selected by analyzing the user's past image history.

[0059] The image recognition unit can customize the recognition method based on the user's current situation during image recognition. For example, if the user is in a meeting, the image recognition unit will perform image recognition in silent mode. If the user is on the move, the image recognition unit will prioritize only simple image recognition. If the user is at home, the image recognition unit will prioritize image recognition related to devices in the home. By customizing the recognition method based on the user's current situation, more appropriate image recognition becomes possible.

[0060] The image recognition unit can select the optimal recognition method by considering the user's geographical location information during image recognition. For example, if the user is in a specific location, the image recognition unit will prioritize image recognition related to that location. For example, if the user is traveling, the image recognition unit will prioritize image recognition related to travel. For example, if the user is at home, the image recognition unit will prioritize image recognition related to devices within the home. In this way, the optimal image recognition method can be selected by considering the user's geographical location information.

[0061] The image recognition unit can analyze the user's social media activity during image recognition and propose recognition methods. For example, if the user is talking about a specific topic on social media, the image recognition unit will prioritize image recognition related to that topic. For example, if the user is participating in a specific event on social media, the image recognition unit will prioritize image recognition related to that event. For example, if the user is talking about a specific device on social media, the image recognition unit will prioritize image recognition related to that device. In this way, by analyzing the user's social media activity, the optimal image recognition method can be proposed.

[0062] The intent understanding unit can analyze the user's past intent history to select the optimal understanding method when understanding intent. For example, the intent understanding unit proposes the optimal understanding method based on the intent the user has frequently expressed in the past. For example, the intent understanding unit predicts and understands the intent expressed during a specific time period based on the user's past intent history. For example, the intent understanding unit analyzes the user's past intent history and proposes the most efficient intent understanding method. In this way, the optimal intent understanding method can be selected by analyzing the user's past intent history.

[0063] The intent understanding unit can customize its understanding methods based on the user's current situation when understanding intent. For example, if the user is in a meeting, the intent understanding unit will perform intent understanding in silent mode. If the user is on the move, for example, the intent understanding unit will prioritize only simple intent understanding. If the user is at home, for example, the intent understanding unit will prioritize intent understanding related to devices within the home. This allows for more appropriate intent understanding by customizing the understanding methods based on the user's current situation.

[0064] The intent understanding unit can select the optimal understanding method by considering the user's geographical location information when understanding intent. For example, if the user is in a specific location, the intent understanding unit will prioritize understanding intents related to that location. For example, if the user is traveling, the intent understanding unit will prioritize understanding intents related to travel. For example, if the user is at home, the intent understanding unit will prioritize understanding intents related to devices within the home. In this way, the optimal intent understanding method can be selected by considering the user's geographical location information.

[0065] The intent understanding unit can analyze a user's social media activity and propose a means of understanding their intent. For example, if a user is talking about a specific topic on social media, the intent understanding unit will prioritize understanding intent related to that topic. For example, if a user is participating in a specific event on social media, the intent understanding unit will prioritize understanding intent related to that event. For example, if a user is talking about a specific device on social media, the intent understanding unit will prioritize understanding intent related to that device. By analyzing the user's social media activity, the unit can propose the most suitable means of understanding intent.

[0066] The automatic configuration unit can analyze the user's past configuration history to select the optimal configuration method during automatic configuration. For example, the automatic configuration unit can suggest the optimal automatic configuration method based on the user's past configurations. For example, the automatic configuration unit can predict and suggest settings to be used during a specific time period based on the user's past configuration history. For example, the automatic configuration unit can analyze the user's past configuration history and suggest the most efficient automatic configuration method. In this way, the optimal automatic configuration method can be selected by analyzing the user's past configuration history.

[0067] The automatic configuration unit can customize the configuration process based on the user's current situation during automatic configuration. For example, if the user is in a meeting, the automatic configuration unit will suppress audio settings and prioritize silent mode. For example, if the user is on the go, the automatic configuration unit will prioritize only simple settings. For example, if the user is at home, the automatic configuration unit will prioritize settings related to devices within the home. This allows for more appropriate automatic configuration by customizing the configuration process based on the user's current situation.

[0068] The automatic configuration unit can select the optimal configuration method during automatic configuration, taking into account the user's geographical location. For example, if the user is in a specific location, the automatic configuration unit will prioritize settings related to that location. For example, if the user is traveling, the automatic configuration unit will prioritize settings related to travel. For example, if the user is at home, the automatic configuration unit will prioritize settings related to devices within the home. In this way, the optimal automatic configuration method can be selected by taking into account the user's geographical location.

[0069] The automatic configuration unit can analyze the user's social media activity during automatic configuration and suggest configuration methods. For example, if the user is talking about a specific topic on social media, the automatic configuration unit will prioritize settings related to that topic. For example, if the user is participating in a specific event on social media, the automatic configuration unit will prioritize settings related to that event. For example, if the user is talking about a specific device on social media, the automatic configuration unit will prioritize settings related to that device. In this way, by analyzing the user's social media activity, the automatic configuration unit can suggest the most suitable configuration method.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The reception system can analyze the user's past behavior history when receiving instructions and suggest the most suitable method of receiving them. For example, it can prioritize displaying instruction methods that the user has frequently used in the past. It can also suggest instruction methods appropriate for a specific time period based on the instructions the user has given during that time. Furthermore, it can analyze the user's behavior patterns and suggest the most suitable instruction method under specific circumstances. This enables more efficient instruction reception by utilizing the user's past behavior history.

[0072] The analysis unit can adjust its analysis method when analyzing user instructions, taking into account the user's current situation and environment. For example, if the user is in a meeting, a simplified analysis method can be applied to provide results quickly. If the user is on the go, the analysis can be adjusted to analyze only simple instructions. Furthermore, if the user is at home, the system can prioritize analyzing instructions related to devices within the home. This enables flexible analysis tailored to the user's current situation and environment.

[0073] The settings unit can analyze the user's past settings history to suggest the optimal settings method when performing settings based on user instructions. For example, it can automatically perform similar settings based on settings the user has made in the past. It can also predict and suggest settings to be used during specific time periods based on the user's past settings history. Furthermore, it can analyze the user's past settings history and suggest the most efficient settings method. This makes it possible to perform more efficient settings by utilizing the user's past settings history.

[0074] The guidance system can adjust its guiding methods to suit the user's current situation and environment. For example, if the user is in a meeting, audio guidance can be suppressed and text guidance can be prioritized. If the user is on the move, the guidance can be adjusted to provide only simple information. Furthermore, if the user is at home, guidance related to devices within the home can be prioritized. This enables flexible guidance tailored to the user's current situation and environment.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The reception unit receives user instructions. User instructions may include voice instructions, text instructions, gesture instructions, etc. The reception unit can receive the user's voice instructions using voice recognition technology, provide a text input interface, and receive the user's gesture instructions using gesture recognition technology. Step 2: The analysis unit analyzes the instructions received by the reception unit. The analysis unit uses natural language processing technology, image recognition technology, and intent understanding technology to analyze the user's instructions and understand their content and intent. Step 3: The configuration unit performs the configuration based on the instructions analyzed by the analysis unit. The configuration unit automatically configures the device, application, and network using automated configuration technology. Step 4: The guide unit guides the user based on the settings made by the configuration unit. The guide unit provides audio, visual, and text guidance to lead the user through the next steps.

[0077] (Example of form 2) The AI ​​assistant app "SmartEase" according to an embodiment of the present invention is a system that allows users to easily configure and troubleshoot devices. When a user gives instructions at a conversational level, such as "I want to display it on the TV" or "I want to connect the earphones in the picture," the AI ​​understands the user's intent and automatically performs the necessary settings. Furthermore, an interactive guide provides step-by-step assistance to the user, supporting configuration and troubleshooting. For example, if a user says "I want to display it on the TV," the AI ​​understands the user's intent and automatically configures the connection between the TV and the device. Next, the interactive guide provides step-by-step instructions. For example, when configuring wireless earphones, the AI ​​gives instructions such as "Turn on the earphones" or "Open the wireless settings on the device," and the user completes the setup by following these instructions. This mechanism makes it easy to operate devices even without technical knowledge. For example, even elderly people or children unfamiliar with technology can configure and troubleshoot devices simply by following the AI's instructions. Additionally, problem solving is rapid, saving users time. For example, there is no need to search the internet or contact experts, allowing problems to be solved quickly. Furthermore, it reduces the resources required for IT support and improves the operational efficiency of businesses. For example, even companies without an IT support department can use AI assistants to enable employees to set up devices and troubleshoot problems themselves. This reduces the cost and time spent on IT support and improves the operational efficiency of businesses. Ultimately, it contributes to bridging the digital divide by providing an environment where people unfamiliar with technology can use it with confidence. For example, when the elderly and children become proficient in using devices, the digital divide is bridged, and everyone can benefit from technology. In this way, the AI ​​assistant app "SmartEase" can significantly reduce the burden on users and provide quick and efficient problem solving.

[0078] The AI ​​assistant application "SmartEase" according to this embodiment comprises a reception unit, an analysis unit, a settings unit, and a guide unit. The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit receives the user's voice instructions using, for example, voice recognition technology. The reception unit can also provide an interface for receiving text input. Furthermore, the reception unit can also receive the user's gesture instructions using gesture recognition technology. For example, the reception unit converts the user's voice instructions into text using voice recognition technology and sends it to the analysis unit. The text input interface allows the user to input instructions using a keyboard or touchscreen. Gesture recognition technology recognizes the user's hand movements and facial expressions and interprets them as instructions. The analysis unit analyzes the instructions received by the reception unit. The analysis unit analyzes the user's instructions using, for example, natural language processing technology. The analysis unit can also analyze the user's instructions using image recognition technology. Furthermore, the analysis unit can use intent understanding technology to understand the user's intentions. For example, the analysis unit uses natural language processing technology to analyze and understand the user's voice instructions. Image recognition technology analyzes and understands the content of images and videos sent by the user. Intent understanding technology analyzes the user's speech and behavioral patterns to infer the user's intentions. The configuration unit performs settings based on the instructions analyzed by the analysis unit. The configuration unit uses, for example, automatic configuration technology to automatically configure the device. The configuration unit can also automatically configure applications. Furthermore, the configuration unit can also automatically configure the network. For example, the configuration unit automatically performs the initial setup of the device so that the user can use the device. Application configuration automatically configures applications installed by the user. Network configuration automatically configures the settings for connecting the user's device to the network. The guide unit guides the user based on the settings made by the configuration unit. The guide unit provides, for example, voice guidance. The guide unit can also provide visual guidance.Furthermore, the guide unit can also provide text guides. For example, the guide unit can use voice guidance to guide the user through the next steps. Visual guides guide the user using images or videos displayed on the screen. Text guides guide the user using text displayed on the screen. As a result, the AI ​​assistant app "SmartEase" according to this embodiment can easily perform device setup and troubleshooting by receiving, analyzing, configuring, and guiding the user based on user instructions.

[0079] The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit can, for example, receive voice instructions using speech recognition technology. Speech recognition technology uses a combination of noise cancellation and speech filtering technologies to convert the user's voice into text with high accuracy. This allows for accurate reception of voice instructions even in noisy environments. The reception unit can also provide an interface for receiving text input. The text input interface allows users to enter instructions using a keyboard or touchscreen. This allows users to easily enter instructions even in situations where voice input is difficult. Furthermore, the reception unit can also receive gesture instructions using gesture recognition technology. Gesture recognition technology uses cameras and sensors to detect the user's hand movements and facial expressions in real time and interpret them as instructions. For example, if a user waves their hand, the reception unit recognizes the movement and sends it to the analysis unit as a specific command. This allows the user to use the app with intuitive operation. The reception unit integrates these diverse input methods to maximize user convenience. For example, by combining voice commands and gesture commands, more complex operations can be performed easily. Furthermore, the reception unit has a function that learns the user's input history and predicts future inputs. This makes user operation smoother and improves the usability of the app.

[0080] The analysis unit analyzes the instructions received by the reception unit. For example, the analysis unit analyzes user instructions using natural language processing technology. Natural language processing technology analyzes the user's voice and text instructions and goes through processes such as morphological analysis, grammatical analysis, and semantic analysis to understand their content. This allows for an accurate understanding of the user's intent. The analysis unit can also analyze user instructions using image recognition technology. Image recognition technology analyzes images and videos sent by the user and uses techniques such as object detection, face recognition, and scene analysis to understand their content. For example, if a user sends an image taken with a camera, the analysis unit analyzes the image and recognizes specific objects or situations. Furthermore, the analysis unit can also use intent understanding technology to understand the user's intent. Intent understanding technology analyzes the user's statements and behavioral patterns and uses machine learning algorithms and deep learning models to infer the user's intent. For example, if a user says, "Tell me the weather for tomorrow," the analysis unit analyzes the statement and understands the user's intention to know the weather forecast. This allows the analysis unit to accurately analyze diverse user instructions and generate appropriate responses. Furthermore, the analysis unit also has the function to learn past instruction history and user behavior patterns to predict future instructions. This results in smoother user operation and improved usability of the app.

[0081] The configuration unit performs settings based on instructions analyzed by the analysis unit. For example, the configuration unit uses automatic configuration technology to automatically configure devices. This automatic configuration technology utilizes preset profiles and user configuration history to automatically perform initial setup and customization of devices based on user instructions. This allows users to use the device immediately without performing complex configuration tasks. The configuration unit can also automatically configure applications. For application configuration, it refers to application configuration files and user configuration history to automatically configure applications installed by the user. For example, when a user installs a new application, the configuration unit automatically configures that application so that the user can use it immediately. Furthermore, the configuration unit can also automatically configure networks. For network configuration, it refers to network profiles and connection history to automatically configure the user's device to connect to the network. For example, when a user tries to connect to a new Wi-Fi network, the configuration unit automatically configures that network so that the user can connect to the internet immediately. In this way, the configuration unit automatically configures devices, applications, and networks based on user instructions, improving user convenience. Furthermore, the settings section also has a function that learns the user's setting history and predicts future settings. This makes user operation smoother and improves the usability of the app.

[0082] The guide unit guides the user based on the settings made by the settings unit. The guide unit can, for example, provide voice guidance. Voice guidance uses synthesized speech technology to guide the user through the next steps. Synthesized speech technology uses a speech synthesis engine and a speech database to generate speech with natural pronunciation and intonation. This allows the user to easily understand the next steps while listening to the voice guidance. The guide unit can also provide visual guidance. Visual guidance uses graphic design and animation technology to guide the user using images and videos displayed on the screen. For example, when a user is setting up a device, the visual guide uses images and videos displayed on the screen to visually explain each step. This allows the user to proceed with the setup work smoothly while referring to the visual information. Furthermore, the guide unit can also provide text guidance. Text guidance uses text generation and formatting technology to guide the user using text displayed on the screen. For example, when a user is setting up an application, the text guide uses text displayed on the screen to explain each step in detail. This allows the user to perform the setup work accurately while referring to the text guide. The guide unit integrates these diverse guiding methods to maximize user convenience. For example, combining voice and visual guides makes more complex operations easier. Furthermore, the guide unit learns the user's operation history and predicts future guidance. This makes user operation smoother and improves the usability of the app.

[0083] The analysis unit includes a natural language processing unit that performs natural language processing. The natural language processing unit can, for example, perform morphological analysis. The natural language processing unit can, for example, perform grammatical analysis. The natural language processing unit can, for example, perform semantic analysis. For example, the natural language processing unit uses morphological analysis to break down the user's voice instructions into word units and analyzes their meaning. Grammatical analysis analyzes the grammatical structure of the user's voice instructions and understands their meaning. Semantic analysis analyzes the meaning of the user's voice instructions and understands their intent. As a result, by performing natural language processing, the user's instructions can be accurately analyzed.

[0084] The analysis unit includes an image recognition unit that performs image recognition. The image recognition unit can, for example, perform object recognition. The image recognition unit can, for example, perform face recognition. The image recognition unit can, for example, perform scene analysis. For example, the image recognition unit uses object recognition to recognize objects contained in images sent by the user and analyzes their contents. Face recognition recognizes faces contained in images sent by the user and identifies the person. Scene analysis analyzes the scene contained in images sent by the user and understands the situation. As a result, by performing image recognition, the user's instructions can be accurately analyzed.

[0085] The analysis unit includes an intent understanding unit that understands the user's intent. The intent understanding unit can, for example, analyze the content of the user's statements. The intent understanding unit can, for example, analyze the user's behavior patterns. The intent understanding unit can, for example, analyze the user's context. For example, the intent understanding unit analyzes the content of the user's statements and understands their intent. The behavior pattern analysis infers the current intent based on the user's past behavior data. The context analysis understands the intent by considering the user's current situation and environment. As a result, by understanding the user's intent, instructions can be accurately analyzed.

[0086] The configuration unit includes an automatic configuration unit that performs configuration automatically. The automatic configuration unit can, for example, perform the initial setup of a device. The automatic configuration unit can, for example, perform the automatic configuration of an application. The automatic configuration unit can, for example, perform the automatic configuration of a network. For example, the automatic configuration unit automatically performs the initial setup of a device so that the user can use the device. Automatic application configuration automatically configures applications installed by the user. Automatic network configuration automatically configures the user's device to connect to the network. In this way, the burden on the user can be reduced by automating the configuration.

[0087] The reception system can estimate the user's emotions and adjust how instructions are received based on those emotions. For example, if the user is stressed, the reception system may provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception system may provide detailed input options and suggest customizable input methods. If the user is in a hurry, for example, the reception system may prioritize voice input and receive instructions quickly. This allows for more appropriate instruction reception by adjusting the instruction reception method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The reception desk can analyze the user's past instruction history and select the optimal reception method. For example, the reception desk can automatically display instructions that the user has frequently given in the past as suggestions. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest instructions to be used during a specific time period based on the user's past instruction history. In this way, the optimal reception method can be selected by analyzing the user's past instruction history.

[0089] The reception system can filter instructions based on the user's current situation and environment. For example, if the user is in a meeting, the reception system might suppress voice input and prioritize text input. If the user is on the go, the reception system might only accept simple instructions. If the user is at home, the reception system might prioritize instructions related to devices within the home. This allows for more appropriate instruction reception by filtering instructions based on the user's current situation and environment.

[0090] The reception desk can estimate the user's emotions and determine the priority of instructions to be received based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize receiving urgent instructions. For example, if the user is relaxed, the reception desk will prioritize receiving normal instructions. For example, if the user is in a hurry, the reception desk will prioritize receiving instructions that require a quick response. This allows for more appropriate instruction reception by determining the priority of instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The reception desk can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception desk will prioritize instructions related to that location. For example, if the user is traveling, the reception desk will prioritize instructions related to travel. For example, if the user is at home, the reception desk will prioritize instructions related to devices within the home. In this way, by considering the user's geographical location, the reception desk can prioritize receiving instructions that are highly relevant.

[0092] The reception desk can analyze the user's social media activity when receiving instructions and receive relevant instructions. For example, if the reception desk is talking about a specific topic on social media, it will prioritize receiving instructions related to that topic. For example, if the reception desk is participating in a specific event on social media, it will prioritize receiving instructions related to that event. For example, if the reception desk is talking about a specific device on social media, it will prioritize receiving instructions related to that device. In this way, by analyzing the user's social media activity, it is possible to receive relevant instructions.

[0093] The analysis unit can estimate the user's emotions and adjust the instruction analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit applies a simple analysis method. For example, if the user is relaxed, the analysis unit applies a detailed analysis method. For example, if the user is in a hurry, the analysis unit applies a method that performs a rapid analysis. By adjusting the instruction analysis method according to the user's emotions, more appropriate instruction analysis becomes possible. 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.

[0094] The analysis unit can adjust the level of detail of the analysis based on the importance of the instruction. For example, the analysis unit performs a detailed analysis for instructions of high urgency. For example, the analysis unit performs a standard analysis for instructions of normal importance. For example, the analysis unit performs a simplified analysis for instructions of low importance. By adjusting the level of detail of the analysis based on the importance of the instruction, more appropriate instruction analysis becomes possible.

[0095] The analysis unit can apply different analysis algorithms depending on the category of the instruction when analyzing the instruction. For example, the analysis unit applies a configuration-specific analysis algorithm to instructions related to device settings. For example, the analysis unit applies a troubleshooting-specific analysis algorithm to instructions related to troubleshooting. For example, the analysis unit applies an FAQ-specific analysis algorithm to instructions related to general questions. By applying different analysis algorithms depending on the category of the instruction, more appropriate instruction analysis becomes possible.

[0096] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing instructions of high urgency. For example, if the user is relaxed, the analysis unit will prioritize analyzing normal instructions. For example, if the user is in a hurry, the analysis unit will prioritize analyzing instructions that require a quick response. This allows for more appropriate instruction analysis by determining the priority of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The analysis unit can determine the priority of instructions based on when they were submitted. For example, it will prioritize highly urgent instructions regardless of when they were submitted. For example, it will prioritize normal instructions based on when they were submitted. For example, it will prioritize instructions of low importance based on when they were submitted. By determining the priority of instructions based on when they were submitted, more appropriate instruction analysis becomes possible.

[0098] The analysis unit can adjust the order of analysis based on the relevance of the instructions. For example, the analysis unit prioritizes the analysis of highly relevant instructions. For example, the analysis unit postpones the analysis of less relevant instructions. For example, the analysis unit performs a standard order of analysis for instructions with a moderate level of relevance. By adjusting the order of analysis based on the relevance of the instructions, more appropriate instruction analysis becomes possible.

[0099] The settings unit can estimate the user's emotions and adjust the settings method based on the estimated emotions. For example, if the user is stressed, the settings unit will apply a simple settings method. For example, if the user is relaxed, the settings unit will apply a detailed settings method. For example, if the user is in a hurry, the settings unit will apply a quick settings method. This allows for more appropriate settings by adjusting the settings method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The configuration unit can analyze the user's past configuration history to select the optimal configuration method during setup. For example, the configuration unit can suggest the optimal configuration method based on the user's past settings. For example, the configuration unit can predict and suggest settings to be used during a specific time period based on the user's past configuration history. For example, the configuration unit can analyze the user's past configuration history to suggest the most efficient configuration method. In this way, the optimal configuration method can be selected by analyzing the user's past configuration history.

[0101] The settings unit can customize the settings based on the user's current situation during setup. For example, if the user is in a meeting, the settings unit will suppress audio settings and prioritize silent mode. If the user is on the go, for example, the settings unit will prioritize only simple settings. If the user is at home, for example, the settings unit will prioritize settings related to devices within the home. This allows for more appropriate settings by customizing the settings based on the user's current situation.

[0102] The settings unit can estimate the user's emotions and determine the priority of settings based on the estimated emotions. For example, if the user is stressed, the settings unit will prioritize settings of high urgency. For example, if the user is relaxed, the settings unit will prioritize settings of normality. For example, if the user is in a hurry, the settings unit will prioritize settings that require immediate attention. This allows for more appropriate settings by determining the priority of settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The settings unit can select the optimal settings method by considering the user's geographical location during setup. For example, if the user is in a specific location, the settings unit will prioritize settings related to that location. For example, if the user is traveling, the settings unit will prioritize settings related to travel. For example, if the user is at home, the settings unit will prioritize settings related to devices within the home. In this way, the optimal settings method can be selected by considering the user's geographical location.

[0104] The settings unit can analyze the user's social media activity during setup and suggest appropriate settings. For example, if the user is discussing a specific topic on social media, the settings unit will prioritize settings related to that topic. For example, if the user is participating in a specific event on social media, the settings unit will prioritize settings related to that event. For example, if the user is discussing a specific device on social media, the settings unit will prioritize settings related to that device. In this way, by analyzing the user's social media activity, the system can suggest the most suitable settings.

[0105] The guide unit can estimate the user's emotions and adjust how the guide is displayed based on those emotions. For example, if the user is tense, the guide unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the guide unit can provide an interface with bright colors to make the input process enjoyable. For example, if the user is tired, the guide unit can provide a simple and highly visible interface to facilitate the input process. By adjusting how the guide is displayed according to the user's emotions, a more appropriate guide display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The guide unit can select the optimal display method by referring to the user's past operation history when displaying guides. For example, the guide unit may prioritize providing display methods that the user has frequently used in the past. For example, the guide unit may suggest the optimal display method for a specific operation based on the user's past operation history. For example, the guide unit may analyze the user's past operation history and provide the most efficient display method. In this way, the optimal guide display method can be selected by referring to the user's past operation history.

[0107] The guide unit can customize the means of guidance based on the user's current situation when displaying guidance. For example, if the user is in a meeting, the guide unit will suppress audio guidance and prioritize text guidance. For example, if the user is on the move, the guide unit will prioritize only simple guidance. For example, if the user is at home, the guide unit will prioritize guidance related to devices in the home. This allows for more appropriate guidance display by customizing the means of guidance based on the user's current situation.

[0108] The guidance unit can estimate the user's emotions and determine the priority of the guides based on the estimated emotions. For example, if the user is stressed, the guidance unit will prioritize providing urgent guides. For example, if the user is relaxed, the guidance unit will prioritize providing normal guides. For example, if the user is in a hurry, the guidance unit will prioritize providing guides that require immediate attention. This allows for the provision of more appropriate guides by prioritizing guides according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The guide unit can select the optimal display method when displaying guides, taking into account the user's device information. For example, if the user is using a smartphone, the guide unit provides a display method that matches the screen size. For example, if the user is using a tablet, the guide unit provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the guide unit provides a concise and highly visible display method. In this way, the optimal guide display method can be selected by taking into account the user's device information.

[0110] The guide unit can analyze the user's social media activity when displaying guides and suggest appropriate guidance methods. For example, if the user is discussing a specific topic on social media, the guide unit will prioritize providing guides related to that topic. For example, if the user is participating in a specific event on social media, the guide unit will prioritize providing guides related to that event. For example, if the user is discussing a specific device on social media, the guide unit will prioritize providing guides related to that device. In this way, by analyzing the user's social media activity, the guide unit can suggest the most appropriate guidance method.

[0111] The natural language processing unit can estimate the user's emotions and adjust its natural language processing method based on the estimated emotions. For example, if the user is stressed, the natural language processing unit applies simple language processing. For example, if the user is relaxed, the natural language processing unit applies detailed language processing. For example, if the user is in a hurry, the natural language processing unit applies a method of rapid language processing. This allows for more appropriate natural language processing by adjusting the natural language processing method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The natural language processing unit can analyze the user's past utterance history to select the optimal processing method during natural language processing. For example, the natural language processing unit may prioritize processing phrases that the user has frequently used in the past. For example, the natural language processing unit may predict and process phrases that the user will use at a specific time period based on the user's past utterance history. For example, the natural language processing unit may analyze the user's past utterance history and propose the most efficient language processing method. In this way, the optimal natural language processing method can be selected by analyzing the user's past utterance history.

[0113] The natural language processing unit can customize its processing methods based on the user's current situation during natural language processing. For example, if the user is in a meeting, the natural language processing unit may suppress voice input and prioritize text input. If the user is on the go, for example, the natural language processing unit may prioritize only simple language processing. If the user is at home, for example, the natural language processing unit may prioritize language processing related to devices in the home. This allows for more appropriate natural language processing by customizing the processing methods based on the user's current situation.

[0114] The natural language processing unit (NLP) can estimate the user's emotions and prioritize NLP processing based on the estimated emotions. For example, if the user is stressed, the NLP will prioritize urgent language processing. If the user is relaxed, the NLP will prioritize normal language processing. If the user is in a hurry, the NLP will prioritize language processing that requires a quick response. By prioritizing NLP processing according to the user's emotions, more appropriate NLP processing becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0115] The natural language processing unit can select the optimal processing method by considering the user's geographical location during natural language processing. For example, if the user is in a specific location, the natural language processing unit will prioritize language processing related to that location. For example, if the user is traveling, the natural language processing unit will prioritize language processing related to travel. For example, if the user is at home, the natural language processing unit will prioritize language processing related to devices within the home. In this way, the optimal natural language processing method can be selected by considering the user's geographical location.

[0116] The natural language processing unit can analyze a user's social media activity during natural language processing and suggest processing methods accordingly. For example, if a user is discussing a specific topic on social media, the natural language processing unit will prioritize language processing related to that topic. For example, if a user is participating in a specific event on social media, the natural language processing unit will prioritize language processing related to that event. For example, if a user is discussing a specific device on social media, the natural language processing unit will prioritize language processing related to that device. In this way, by analyzing a user's social media activity, the optimal natural language processing method can be suggested.

[0117] The image recognition unit can estimate the user's emotions and adjust the image recognition method based on the estimated emotions. For example, if the user is stressed, the image recognition unit applies a simple image recognition method. For example, if the user is relaxed, the image recognition unit applies a detailed image recognition method. For example, if the user is in a hurry, the image recognition unit applies a method for rapid image recognition. By adjusting the image recognition method according to the user's emotions, more appropriate image recognition becomes possible. 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.

[0118] The image recognition unit can analyze the user's past image history to select the optimal recognition method during image recognition. For example, the image recognition unit can propose the optimal recognition method based on images the user has frequently taken in the past. For example, the image recognition unit can predict and recognize images taken during a specific time period based on the user's past image history. For example, the image recognition unit can analyze the user's past image history and propose the most efficient image recognition method. In this way, the optimal image recognition method can be selected by analyzing the user's past image history.

[0119] The image recognition unit can customize the recognition method based on the user's current situation during image recognition. For example, if the user is in a meeting, the image recognition unit will perform image recognition in silent mode. If the user is on the move, the image recognition unit will prioritize only simple image recognition. If the user is at home, the image recognition unit will prioritize image recognition related to devices in the home. By customizing the recognition method based on the user's current situation, more appropriate image recognition becomes possible.

[0120] The image recognition unit can estimate the user's emotions and determine the priority of image recognition based on the estimated emotions. For example, if the user is stressed, the image recognition unit will prioritize high-urgency image recognition. For example, if the user is relaxed, the image recognition unit will prioritize normal image recognition. For example, if the user is in a hurry, the image recognition unit will prioritize image recognition that requires a quick response. This allows for more appropriate image recognition by determining the priority of image recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The image recognition unit can select the optimal recognition method by considering the user's geographical location information during image recognition. For example, if the user is in a specific location, the image recognition unit will prioritize image recognition related to that location. For example, if the user is traveling, the image recognition unit will prioritize image recognition related to travel. For example, if the user is at home, the image recognition unit will prioritize image recognition related to devices within the home. In this way, the optimal image recognition method can be selected by considering the user's geographical location information.

[0122] The image recognition unit can analyze the user's social media activity during image recognition and propose recognition methods. For example, if the user is talking about a specific topic on social media, the image recognition unit will prioritize image recognition related to that topic. For example, if the user is participating in a specific event on social media, the image recognition unit will prioritize image recognition related to that event. For example, if the user is talking about a specific device on social media, the image recognition unit will prioritize image recognition related to that device. In this way, by analyzing the user's social media activity, the optimal image recognition method can be proposed.

[0123] The intent understanding unit can estimate the user's emotions and adjust its intent understanding method based on the estimated emotions. For example, if the user is stressed, the intent understanding unit applies a simple intent understanding method. For example, if the user is relaxed, the intent understanding unit applies a detailed intent understanding method. For example, if the user is in a hurry, the intent understanding unit applies a method for rapid intent understanding. By adjusting the intent understanding method according to the user's emotions, more appropriate intent understanding becomes possible. 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.

[0124] The intent understanding unit can analyze the user's past intent history to select the optimal understanding method when understanding intent. For example, the intent understanding unit proposes the optimal understanding method based on the intent the user has frequently expressed in the past. For example, the intent understanding unit predicts and understands the intent expressed during a specific time period based on the user's past intent history. For example, the intent understanding unit analyzes the user's past intent history and proposes the most efficient intent understanding method. In this way, the optimal intent understanding method can be selected by analyzing the user's past intent history.

[0125] The intent understanding unit can customize its understanding methods based on the user's current situation when understanding intent. For example, if the user is in a meeting, the intent understanding unit will perform intent understanding in silent mode. If the user is on the move, for example, the intent understanding unit will prioritize only simple intent understanding. If the user is at home, for example, the intent understanding unit will prioritize intent understanding related to devices within the home. This allows for more appropriate intent understanding by customizing the understanding methods based on the user's current situation.

[0126] The intent understanding unit can estimate the user's emotions and determine the priority of intent understanding based on the estimated emotions. For example, if the user is stressed, the intent understanding unit will prioritize understanding urgent intents. For example, if the user is relaxed, the intent understanding unit will prioritize understanding normal intents. For example, if the user is in a hurry, the intent understanding unit will prioritize understanding intents that require a quick response. This allows for more appropriate intent understanding by determining the priority of intent understanding according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The intent understanding unit can select the optimal understanding method by considering the user's geographical location information when understanding intent. For example, if the user is in a specific location, the intent understanding unit will prioritize understanding intents related to that location. For example, if the user is traveling, the intent understanding unit will prioritize understanding intents related to travel. For example, if the user is at home, the intent understanding unit will prioritize understanding intents related to devices within the home. In this way, the optimal intent understanding method can be selected by considering the user's geographical location information.

[0128] The intent understanding unit can analyze a user's social media activity and propose a means of understanding their intent. For example, if a user is talking about a specific topic on social media, the intent understanding unit will prioritize understanding intent related to that topic. For example, if a user is participating in a specific event on social media, the intent understanding unit will prioritize understanding intent related to that event. For example, if a user is talking about a specific device on social media, the intent understanding unit will prioritize understanding intent related to that device. By analyzing the user's social media activity, the unit can propose the most suitable means of understanding intent.

[0129] The automatic setting unit can estimate the user's emotions and adjust the automatic setting method based on the estimated emotions. For example, if the user is stressed, the automatic setting unit applies a simple automatic setting method. For example, if the user is relaxed, the automatic setting unit applies a detailed automatic setting method. For example, if the user is in a hurry, the automatic setting unit applies a method for quick automatic setting. This allows for more appropriate automatic setting by adjusting the automatic setting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0130] The automatic configuration unit can analyze the user's past configuration history to select the optimal configuration method during automatic configuration. For example, the automatic configuration unit can suggest the optimal automatic configuration method based on the user's past configurations. For example, the automatic configuration unit can predict and suggest settings to be used during a specific time period based on the user's past configuration history. For example, the automatic configuration unit can analyze the user's past configuration history and suggest the most efficient automatic configuration method. In this way, the optimal automatic configuration method can be selected by analyzing the user's past configuration history.

[0131] The automatic configuration unit can customize the configuration process based on the user's current situation during automatic configuration. For example, if the user is in a meeting, the automatic configuration unit will suppress audio settings and prioritize silent mode. For example, if the user is on the go, the automatic configuration unit will prioritize only simple settings. For example, if the user is at home, the automatic configuration unit will prioritize settings related to devices within the home. This allows for more appropriate automatic configuration by customizing the configuration process based on the user's current situation.

[0132] The automatic setting unit can estimate the user's emotions and determine the priority of automatic settings based on the estimated emotions. For example, if the user is stressed, the automatic setting unit will prioritize settings that require urgency. For example, if the user is relaxed, the automatic setting unit will prioritize settings that require immediate attention. For example, if the user is in a hurry, the automatic setting unit will prioritize settings that require immediate attention. This allows for more appropriate automatic settings by determining the priority of automatic settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0133] The automatic configuration unit can select the optimal configuration method during automatic configuration, taking into account the user's geographical location. For example, if the user is in a specific location, the automatic configuration unit will prioritize settings related to that location. For example, if the user is traveling, the automatic configuration unit will prioritize settings related to travel. For example, if the user is at home, the automatic configuration unit will prioritize settings related to devices within the home. In this way, the optimal automatic configuration method can be selected by taking into account the user's geographical location.

[0134] The automatic configuration unit can analyze the user's social media activity during automatic configuration and suggest configuration methods. For example, if the user is talking about a specific topic on social media, the automatic configuration unit will prioritize settings related to that topic. For example, if the user is participating in a specific event on social media, the automatic configuration unit will prioritize settings related to that event. For example, if the user is talking about a specific device on social media, the automatic configuration unit will prioritize settings related to that device. In this way, by analyzing the user's social media activity, the automatic configuration unit can suggest the most suitable configuration method.

[0135] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0136] The reception system can analyze the user's past behavior history when receiving instructions and suggest the most suitable method of receiving them. For example, it can prioritize displaying instruction methods that the user has frequently used in the past. It can also suggest instruction methods appropriate for a specific time period based on the instructions the user has given during that time. Furthermore, it can analyze the user's behavior patterns and suggest the most suitable instruction method under specific circumstances. This enables more efficient instruction reception by utilizing the user's past behavior history.

[0137] The analysis unit can adjust its analysis method when analyzing user instructions, taking into account the user's current situation and environment. For example, if the user is in a meeting, a simplified analysis method can be applied to provide results quickly. If the user is on the go, the analysis can be adjusted to analyze only simple instructions. Furthermore, if the user is at home, the system can prioritize analyzing instructions related to devices within the home. This enables flexible analysis tailored to the user's current situation and environment.

[0138] The settings unit can analyze the user's past settings history to suggest the optimal settings method when performing settings based on user instructions. For example, it can automatically perform similar settings based on settings the user has made in the past. It can also predict and suggest settings to be used during specific time periods based on the user's past settings history. Furthermore, it can analyze the user's past settings history and suggest the most efficient settings method. This makes it possible to perform more efficient settings by utilizing the user's past settings history.

[0139] The guidance system can adjust its guiding methods to suit the user's current situation and environment. For example, if the user is in a meeting, audio guidance can be suppressed and text guidance can be prioritized. If the user is on the move, the guidance can be adjusted to provide only simple information. Furthermore, if the user is at home, guidance related to devices within the home can be prioritized. This enables flexible guidance tailored to the user's current situation and environment.

[0140] The reception system can estimate the user's emotions and adjust how instructions are received based on those estimates. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input and receive instructions quickly. This enables flexible instruction reception that responds to the user's emotions.

[0141] The analysis unit can estimate the user's emotions and adjust the instruction analysis method based on the estimated emotions. For example, if the user is stressed, a simple analysis method can be applied. If the user is relaxed, a more detailed analysis method can be applied. Furthermore, if the user is in a hurry, a method for rapid analysis can be applied. This enables flexible instruction analysis that responds to the user's emotions.

[0142] The settings unit can estimate the user's emotions and adjust the settings method based on those estimates. For example, if the user is stressed, a simple settings method can be applied. If the user is relaxed, a more detailed settings method can be applied. Furthermore, if the user is in a hurry, a method for quick settings can be applied. This allows for flexible settings that respond to the user's emotions.

[0143] The guide unit can estimate the user's emotions and adjust how the guide is displayed based on those emotions. For example, if the user is stressed, it can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, it can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, it can provide a simple and highly visible interface to make the input process easier. This enables flexible guide display that responds to the user's emotions.

[0144] The intent understanding unit can estimate the user's emotions and adjust its intent understanding method based on those emotions. For example, if the user is stressed, a simple intent understanding method can be applied. If the user is relaxed, a more detailed intent understanding method can be applied. Furthermore, if the user is in a hurry, a method for rapid intent understanding can be applied. This enables flexible intent understanding that responds to the user's emotions.

[0145] The automatic configuration unit can estimate the user's emotions and adjust the configuration method based on those emotions. For example, if the user is stressed, a simple configuration method can be applied. If the user is relaxed, a more detailed configuration method can be applied. Furthermore, if the user is in a hurry, a method for rapid configuration can be applied. This enables flexible configuration that responds to the user's emotions.

[0146] The following briefly describes the processing flow for example form 2.

[0147] Step 1: The reception unit receives user instructions. User instructions may include voice instructions, text instructions, gesture instructions, etc. The reception unit can receive the user's voice instructions using voice recognition technology, provide a text input interface, and receive the user's gesture instructions using gesture recognition technology. Step 2: The analysis unit analyzes the instructions received by the reception unit. The analysis unit uses natural language processing technology, image recognition technology, and intent understanding technology to analyze the user's instructions and understand their content and intent. Step 3: The configuration unit performs the configuration based on the instructions analyzed by the analysis unit. The configuration unit automatically configures the device, application, and network using automated configuration technology. Step 4: The guide unit guides the user based on the settings made by the configuration unit. The guide unit provides audio, visual, and text guidance to lead the user through the next steps.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, analysis unit, setting unit, and guide unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's voice instructions using voice recognition technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically configures the device. The guide unit is implemented by the control unit 46A of the smart device 14 and provides voice guidance and visual guidance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the reception unit, analysis unit, setting unit, and guide unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's voice instructions using voice recognition technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically configures the device. The guide unit is implemented by the control unit 46A of the smart glasses 214 and provides voice guidance and visual guidance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the reception unit, analysis unit, setting unit, and guide unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's voice instructions using voice recognition technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically configures the device. The guide unit is implemented by the control unit 46A of the headset terminal 314 and provides voice and visual guidance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0184] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] Each of the multiple elements described above, including the reception unit, analysis unit, setting unit, and guide unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives voice instructions from the user using speech recognition technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically configures the device. The guide unit is implemented by the control unit 46A of the robot 414 and provides voice guidance and visual guidance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] (Note 1) A reception area that takes user instructions, An analysis unit that analyzes the instructions received by the reception unit, A setting unit that performs settings based on the instructions analyzed by the analysis unit, The system includes a guide unit that guides the user based on the settings made by the setting unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a natural language processing unit that performs natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It includes an image recognition unit that performs image recognition. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It is equipped with an intent understanding unit that understands the user's intentions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The setting unit is, It features an automatic configuration unit that handles settings automatically. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts how instructions are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past instruction history and select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving instructions, filtering is performed based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of instructions to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving instructions, the system prioritizes accepting instructions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving instructions, the system analyzes the user's social media activity and accepts relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the instruction analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing instructions, adjust the level of detail in the analysis based on the importance of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing instructions, different analysis algorithms are applied depending on the category of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing instructions, the priority of the analysis is determined based on when the instructions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing instructions, adjust the order of analysis based on the relationships between the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The setting unit is, It estimates the user's emotions and adjusts the settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The setting unit is, During setup, the system analyzes the user's past configuration history to select the optimal configuration method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The setting unit is, During setup, customize the configuration method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The setting unit is, It estimates the user's emotions and determines the priority of settings based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The setting unit is, During setup, the optimal setup method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The setting unit is, During setup, the system analyzes the user's social media activity and suggests configuration options. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is It estimates the user's emotions and adjusts how the guide is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is When displaying the guide, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is When displaying the guide, customize the guidance method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is It estimates the user's emotions and determines the priority of the guide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide section is When displaying guides, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide section is When displaying guides, the system analyzes the user's social media activity and suggests methods for providing guidance. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned natural language processing unit, It estimates the user's emotions and adjusts the natural language processing method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned natural language processing unit, During natural language processing, the system analyzes the user's past utterance history to select the optimal processing method. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned natural language processing unit, When processing natural language, the processing method is customized based on the user's current situation. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned natural language processing unit, It estimates the user's emotions and determines the priority of natural language processing based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned natural language processing unit, When processing natural language, the optimal processing method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned natural language processing unit, When processing natural language, we analyze the user's social media activity and propose processing methods. The system described in Appendix 2, characterized by the features described herein. (Note 36) The image recognition unit, It estimates the user's emotions and adjusts the image recognition method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The image recognition unit, During image recognition, the system analyzes the user's past image history to select the optimal recognition method. The system described in Appendix 3, characterized by the features described herein. (Note 38) The image recognition unit, During image recognition, the recognition method is customized based on the user's current situation. The system described in Appendix 3, characterized by the features described herein. (Note 39) The image recognition unit, It estimates the user's emotions and determines the priority of image recognition based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The image recognition unit, During image recognition, the optimal recognition method is selected by considering the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 41) The image recognition unit, When performing image recognition, analyze the user's social media activities and propose means of recognition The system according to appended note 3, characterized by this (Appended note 42) The intention understanding unit Estimate the user's emotion and adjust the method of intention understanding based on the estimated user emotion The system according to appended note 4, characterized by this (Appended note 43) The intention understanding unit When performing intention understanding, analyze the user's past intention history and select an optimal understanding method The system according to appended note 4, characterized by this (Appended note 44) The intention understanding unit When performing intention understanding, customize the means of understanding based on the user's current situation The system according to appended note 4, characterized by this (Appended note 45) The intention understanding unit Estimate the user's emotion and determine the priority of intention understanding based on the estimated user emotion The system according to appended note 4, characterized by this (Appended note 46) The intention understanding unit When performing intention understanding, consider the user's geographical location information and select an optimal understanding method The system according to appended note 4, characterized by this (Appended note 47) The intention understanding unit When performing intention understanding, analyze the user's social media activities and propose means of understanding The system according to appended note 4, characterized by this (Appended note 48) The automatic setting unit Estimate the user's emotion and adjust the method of automatic setting based on the estimated user emotion The system according to appended note 5, characterized by this (Appended note 49) The automatic setting unit When automatically setting, analyze the user's past setting history to select the optimal setting method The system according to Supplementary Note 5, characterized by this (Supplementary Note 50) The automatic setting unit When automatically setting, customize the setting means based on the user's current situation The system according to Supplementary Note 5, characterized by this (Supplementary Note 51) The automatic setting unit Estimate the user's emotion and determine the priority order of automatic setting based on the estimated user emotion The system according to Supplementary Note 5, characterized by this (Supplementary Note 52) The automatic setting unit When automatically setting, select the optimal setting method considering the user's geographical location information The system according to Supplementary Note 5, characterized by this (Supplementary Note 53) The automatic setting unit When automatically setting, analyze the user's social media activities and propose setting means The system according to Supplementary Note 5, characterized by this

Explanation of Signs

[0220] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that takes user instructions, An analysis unit that analyzes the instructions received by the reception unit, A setting unit that performs settings based on the instructions analyzed by the analysis unit, The system includes a guide unit that guides the user based on the settings made by the setting unit. A system characterized by the following features.

2. The aforementioned analysis unit, It includes a natural language processing unit that performs natural language processing. The system according to feature 1.

3. The aforementioned analysis unit, It includes an image recognition unit that performs image recognition. The system according to feature 1.

4. The aforementioned analysis unit, It is equipped with an intent understanding unit that understands the user's intentions. The system according to feature 1.

5. The aforementioned setting unit is, It features an automatic configuration unit that handles settings automatically. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts how instructions are received based on those estimated emotions. The system according to feature 1.

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

8. The aforementioned reception unit is When receiving instructions, filtering is performed based on the user's current situation and environment. The system according to feature 1.

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