system

Abstract

The system according to this embodiment aims to quickly and accurately acquire product trouble information and maintenance methods. [Solution] The system according to the embodiment comprises a shooting unit, an analysis unit, a search unit, and a display unit. The shooting unit allows the user to photograph the product using a camera. The analysis unit analyzes the image captured by the shooting unit and automatically identifies the product. The search unit searches for troubleshooting information and maintenance methods based on the product identified by the analysis unit. The display unit displays the information acquired by the search unit on the camera image.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 prior art, there is a problem that it is difficult to quickly and accurately obtain trouble information and maintenance methods of products.

[0005] The system according to the embodiment aims to quickly and accurately obtain trouble information and maintenance methods of products.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a shooting unit, an analysis unit, a search unit, and a display unit. The shooting unit allows the user to photograph the product using a camera. The analysis unit analyzes the image captured by the shooting unit and automatically identifies the product. The search unit searches for troubleshooting information and maintenance methods based on the product identified by the analysis unit. The display unit displays the information acquired by the search unit on the camera image. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and accurately acquire product trouble information and maintenance methods. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The product troubleshooting system according to an embodiment of the present invention is a system that automatically identifies products using the camera of a mobile device, collects common problems, and feeds them back into the camera image. The product troubleshooting system works by having the user photograph a product with a problem using the camera of a smartphone or similar device. For example, the user can photograph problems that occur in daily life, such as plumbing issues or appliance malfunctions. This image is input into the system. Next, the product troubleshooting system analyzes the input image and automatically identifies the product. The system uses image recognition technology to extract product features and identifies the product by comparing them with a database. For example, the system analyzes an image of a washing machine and identifies a specific manufacturer and model. Based on the identified product, the product troubleshooting system searches for common problem information and maintenance methods. The system retrieves the relevant problem information and maintenance methods from the database and provides them to the user. For example, it searches for problem information and solutions related to washing machine water blockages. Finally, the product troubleshooting system displays self-check points on the camera image. The user can perform a self-check according to the displayed points and resolve the problem. For example, in the case of a washing machine water blockage, the system displays instructions on how to clean the filter, and the user cleans it accordingly. This mechanism allows the user to resolve problems through self-checks before requesting repairs from a professional. This is expected to reduce repair costs and speed up troubleshooting. Furthermore, the product troubleshooting system can save the user's self-check history, which can be used to help resolve future problems. For example, by referring to the history of past problems, it can respond quickly if similar problems recur. This allows the product troubleshooting system to automatically acquire troubleshooting information and maintenance methods when the user takes a picture of the product using a camera, and display them on the camera image.

[0029] The product troubleshooting system according to this embodiment comprises a shooting unit, an analysis unit, a search unit, and a display unit. The shooting unit allows the user to photograph the product using a camera. When the user photographs the product using a camera, for example, a smartphone camera can be used. The shooting unit takes an image of the product using, for example, a smartphone camera. The shooting unit can also use a digital camera or a tablet camera. For example, a digital camera can be used to take a high-resolution image. Furthermore, the shooting unit can automatically adjust the camera settings to obtain the optimal image. For example, it can automatically adjust the camera's exposure and white balance. The analysis unit analyzes the image taken by the shooting unit and automatically identifies the product. The analysis unit extracts product features using, for example, image recognition technology. For example, it analyzes product features using deep learning. The analysis unit can also identify products using pattern recognition technology. For example, it identifies products based on their shape and color. Furthermore, the analysis unit identifies products by comparing them with a database. For example, it compares product features with a database to identify a specific manufacturer or model. The search unit searches for troubleshooting information and maintenance methods based on the product identified by the analysis unit. For example, the search unit retrieves relevant troubleshooting information and maintenance methods from a database. For example, it searches for troubleshooting information and solutions related to washing machine water blockages. The search unit can also search for information on the internet. For example, it refers to the product's official website and user reviews. Furthermore, the search unit can provide search results based on past troubleshooting history. For example, it may suggest solutions for similar problems that have occurred in the past. The display unit displays the information obtained by the search unit on the camera image. For example, the display unit may display self-check points on the camera image. For example, it may display instructions on how to clean the filter. The display unit can also save the user's self-check history. For example, it may record the content of past self-checks. Furthermore, the display unit can provide feedback to the user. For example, it may display advice based on the results of the self-check.As a result, the product troubleshooting system according to this embodiment can automatically acquire troubleshooting information and maintenance methods when a user takes a picture of the product using a camera, and display them on the camera image.

[0030] The photography unit allows users to photograph products using a camera. Users can use, for example, a smartphone camera to photograph the product. The photography unit can also use digital cameras or tablet cameras. For example, a digital camera can capture high-resolution images. Furthermore, the photography unit can automatically adjust camera settings to obtain optimal images. For example, it can automatically adjust the camera's exposure and white balance. The photography unit also has a function to automatically focus the camera when the user photographs the product. This allows users to easily capture clear images without special skills or knowledge. Additionally, the photography unit has a function to capture multiple images in sequence and combine them to generate high-precision images. For example, it can combine images taken from different angles of a product to generate a 3D model. This allows the analysis unit to provide information for more accurate product identification. The photography unit also has a function to automatically upload images taken by the user to the cloud, making them accessible to the analysis and search units. This eliminates the need for users to manually transfer images. Furthermore, the camera unit also has a function to provide real-time feedback on images captured by the user. For example, if a captured image is unclear, it can display a message prompting the user to retake the image. This allows users to always capture the best possible image, improving the overall accuracy and reliability of the system.

[0031] The analysis unit analyzes images captured by the imaging unit and automatically identifies products. The analysis unit extracts product features using, for example, image recognition technology. For example, it analyzes product features using deep learning. The analysis unit can also identify products using pattern recognition technology. For example, it can identify products based on their shape and color. Furthermore, the analysis unit identifies products by comparing them with a database. For example, it compares product features with a database to identify specific manufacturers and models. The analysis unit utilizes deep learning-based image recognition algorithms to extract subtle product features with high accuracy. For example, it can identify product logos and specific design patterns. This makes it possible to accurately identify different manufacturers and models even within the same product category. In addition to shape and color, the analysis unit can also analyze differences in texture and material. For example, it can distinguish between metal and plastic parts. Furthermore, the analysis unit can analyze the product's usage and the extent of damage. For example, if there are scratches or stains on the product surface, it can identify their location and size and infer the cause of the problem. Based on this information, the analysis unit comprehensively evaluates the product's condition and proposes the optimal troubleshooting method. Furthermore, the analysis unit can continuously improve its accuracy by learning from past analysis data. For example, it can improve its analysis algorithms based on user feedback, achieving more accurate product identification. This allows the analysis unit to always utilize the latest technology and data to provide highly accurate analysis.

[0032] The search unit searches for troubleshooting information and maintenance methods based on the product identified by the analysis unit. For example, the search unit retrieves relevant troubleshooting information and maintenance methods from a database. For instance, it searches for troubleshooting information and solutions related to washing machine clogs. The search unit can also search information on the internet, such as the product's official website or user reviews. Furthermore, the search unit can provide search results based on past troubleshooting history, such as suggesting solutions for similar problems in the past. Based on the product information provided by the analysis unit, the search unit quickly searches for relevant troubleshooting information and maintenance methods. For example, it can identify the optimal solution based on the product model number, manufacturing year, and information on specific parts. The search unit utilizes not only database information but also the latest information from the internet. For example, it can retrieve the latest maintenance guides and troubleshooting information from the product's official website and provide it to the user. It can also collect information on problems experienced by other users and their solutions by referring to user reviews and forum posts. Furthermore, the search unit suggests solutions for similar problems based on past troubleshooting history, such as identifying the most effective solution by referring to the history of problems that have occurred with the same product in the past. The search unit comprehensively evaluates this information and proposes the optimal solution to the user. Furthermore, based on user search history and feedback, the search unit continuously improves its search algorithm, enabling it to provide more accurate search results. This allows the search unit to provide quick and accurate solutions to problems users face and support product maintenance.

[0033] The display unit displays information acquired by the search unit on the camera image. For example, the display unit can display self-check points on the camera image, such as how to clean the filter. The display unit can also save the user's self-check history, recording the details of past self-checks. Furthermore, the display unit can provide feedback to the user, displaying advice based on the self-check results. The display unit provides an interface to clearly display information provided by the search unit to the user. For example, it can overlay information on the camera image to visually indicate points the user should check, allowing the user to intuitively understand the specific steps. The display unit also has a function to provide real-time feedback when the user performs a self-check. For example, when the user cleans the filter, it can sequentially display the correct procedure and guide the user to the next step according to their progress. The display unit also saves the user's self-check history for later reference, allowing the user to review past maintenance history and use it as a reference for regular maintenance. Furthermore, the display unit can provide customized advice to the user. For example, if a particular problem occurs frequently, the display unit can identify the cause and suggest preventative measures. Through these functions, the display unit provides support to help users quickly and effectively resolve product problems. Furthermore, based on user feedback, the display unit can continuously improve its display content and interface to provide a more user-friendly system. In this way, the display unit becomes a powerful tool for users to resolve product problems themselves and supports product maintenance.

[0034] The display unit can display self-check points on the camera image. For example, the display unit can overlay self-check points on the camera image. For example, it can display instructions on how to clean the filter on the camera image. The display unit can also highlight self-check points. For example, it can highlight important parts in red. Furthermore, the display unit can display self-check points with animation. For example, it can use arrows or icons to show the self-check procedure. This allows the user to confirm the self-check points on the camera image. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input self-check points into a generating AI, and the generating AI can display the self-check points on the camera image.

[0035] The search unit can retrieve relevant trouble information and maintenance methods from the database. For example, the search unit can search the database for trouble information. For example, it can retrieve trouble information regarding clogged washing machines. The search unit can also search the database for maintenance methods. For example, it can retrieve instructions on how to clean a filter. Furthermore, the search unit can search the database for past trouble history. For example, it can retrieve solutions to past troubles. This allows the search unit to retrieve trouble information and maintenance methods from the database. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the search for trouble information and maintenance methods into a generating AI, and the generating AI can output the search results.

[0036] The analysis unit can identify products by extracting product features using image recognition technology and comparing them with a database. The analysis unit can analyze product features using, for example, deep learning. For example, it can identify products based on their shape and color. The analysis unit can also identify products using pattern recognition technology. For example, it can identify products based on their logo or label. Furthermore, the analysis unit identifies products by comparing them with a database. For example, it can compare product features with a database to identify a specific manufacturer or model. This improves the accuracy of product identification using image recognition technology. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input product features into a generating AI, which can then identify the product.

[0037] The display unit may include a history storage unit that stores the user's self-check history. The display unit can, for example, record the content of the self-check performed by the user. For example, it can store the history of filter cleaning. The display unit can also store the results of the self-check. For example, it can record whether a problem was resolved as a result of the self-check. Furthermore, the display unit can store the self-check history in a database. For example, past self-check history can be stored in a database to help resolve future problems. This allows the user's self-check history to be saved. Some or all of the above-described processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the self-check history into a generating AI, and the generating AI can save the history.

[0038] The history storage unit can save past trouble histories to help resolve future problems. For example, the history storage unit can record the details of past troubles. For example, it can save trouble histories related to washing machine water blockages. The history storage unit can also save methods for resolving troubles. For example, it can record methods for cleaning filters. Furthermore, the history storage unit can save the date and time when troubles occurred. For example, it can record the date and time when troubles occurred. By saving past trouble histories, it can be used to help resolve future problems. Some or all of the above processing in the history storage unit may be performed using AI, for example, or without AI. For example, the history storage unit can input trouble histories into a generating AI, and the generating AI can save the history.

[0039] The camera unit can automatically adjust the angle and distance of the product during shooting to acquire the optimal image. For example, the system can automatically adjust the angle of the product and shoot from the optimal viewpoint. The camera unit can also automatically adjust the distance to the product to acquire a clear image. Furthermore, the camera unit can shoot from multiple angles so that the system can capture both the overall image and details of the product in a balanced way. This allows for the acquisition of the optimal image by automatically adjusting the angle and distance of the product. Some or all of the above processing in the camera unit may be performed using AI, for example, or not using AI. For example, the camera unit can input the adjustment of the product's angle and distance to a generating AI, which can then acquire the optimal image.

[0040] The imaging unit can apply filters to highlight specific parts of a product during imaging. For example, the imaging unit may apply a specific color filter to highlight important parts of the product. The imaging unit may also apply an edge detection filter to highlight problem areas on the product. Furthermore, the imaging unit may apply a zoom filter to enlarge specific parts of the product. This makes important parts clearer by highlighting specific parts of the product. Some or all of the above processing in the imaging unit may be performed using AI, for example, or not using AI. For example, the imaging unit can input the application of a filter to highlight specific parts of the product to a generating AI, which can then apply the filter.

[0041] The camera unit can automatically select the optimal shooting settings by referring to the user's past shooting history during shooting. For example, the system can analyze the user's past shooting history and automatically select the optimal exposure setting. The system can also automatically set the optimal white balance based on the user's past shooting history. Furthermore, the system can automatically select the optimal focus setting by referring to the user's past shooting history. In this way, the optimal shooting settings can be automatically selected by referring to the user's past shooting history. Some or all of the above processes in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the user's past shooting history into a generating AI, which can then select the optimal shooting settings.

[0042] The shooting unit can set optimal shooting conditions by considering the user's surrounding environment during shooting. For example, the system can detect ambient brightness and automatically set the optimal exposure. The system can also detect ambient sound and apply noise reduction. Furthermore, the system can detect ambient temperature and optimize camera settings. In this way, optimal shooting conditions can be set by considering the user's surrounding environment. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input ambient environmental information into a generating AI, which can then set the optimal shooting conditions.

[0043] The analysis unit can automatically detect and identify abnormal parts of a product during analysis. For example, the analysis unit can perform image analysis to automatically detect abnormal parts of the product. The analysis unit can also identify the abnormal parts and notify the user. Furthermore, the analysis unit can provide detailed information about the abnormal parts and suggest repair methods to the user. This enables a rapid response by automatically detecting and identifying abnormal parts of the product. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the detection and identification of abnormal parts into a generating AI, which can then detect and identify the abnormal parts.

[0044] The analysis unit can identify the cause of an anomaly by referring to the product's usage history during analysis. For example, the system analyzes the product's usage history and identifies the cause of the anomaly. The analysis unit can also evaluate the frequency of anomalies based on the usage history. Furthermore, the analysis unit can refer to the usage history and explain the cause of the anomaly to the user. In this way, the cause of an anomaly can be identified by referring to the product's usage history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the analysis of the usage history and the identification of the cause of the anomaly into a generating AI, which can then identify the cause of the anomaly.

[0045] The analysis unit can improve the accuracy of its analysis by referring to the product's manufacturer and model information during the analysis process. For example, the system can refer to the product's manufacturer information to improve the accuracy of the analysis. The analysis unit can also improve the accuracy of its analysis based on the product's model information. Furthermore, the analysis unit can refer to the manufacturer and model information to perform analysis for specific problems. This improves the accuracy of the analysis by referring to the product's manufacturer and model information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input manufacturer and model information into a generating AI, which can then improve the accuracy of the analysis.

[0046] The analysis unit can correct the analysis results by taking into account the product's usage environment information during the analysis. For example, the system may refer to the product's usage environment information and correct the analysis results. The analysis unit can also improve the reliability of the analysis results based on the usage environment information. Furthermore, the system may consider the usage environment information and identify the cause of the anomaly. This improves the reliability of the analysis results by considering the product's usage environment information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the usage environment information into a generating AI, and the generating AI can correct the analysis results.

[0047] The search unit can prioritize displaying troubleshooting information related to specific parts of a product during a search. For example, the system can prioritize displaying troubleshooting information related to specific parts of a product. The search unit can also prioritize displaying troubleshooting information related to parts of high user interest. Furthermore, the search unit can prioritize displaying troubleshooting information related to critical parts of a product. This allows for the rapid provision of important information by prioritizing the display of troubleshooting information related to specific parts of a product. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the priority display of troubleshooting information related to specific parts into a generating AI, and the generating AI can prioritize displaying the troubleshooting information.

[0048] The search unit can suggest the optimal maintenance method by referring to the product's usage history during a search. For example, the system can refer to the product's usage history and suggest the optimal maintenance method. The search unit can also suggest the frequency of maintenance based on the usage history. Furthermore, the search unit can refer to the system's usage history and suggest maintenance methods for specific problems. In this way, the optimal maintenance method can be suggested by referring to the product's usage history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the usage history into a generating AI, and the generating AI can suggest the optimal maintenance method.

[0049] The search unit can display optimal troubleshooting information by referencing the product's manufacturer and model information during a search. For example, the system can refer to the product's manufacturer information and display the optimal troubleshooting information. The search unit can also display optimal troubleshooting information based on the product's model information. Furthermore, the search unit can refer to the manufacturer and model information and display information for a specific problem. This allows the system to display optimal troubleshooting information by referencing the product's manufacturer and model information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input manufacturer and model information into a generating AI, which can then display the optimal troubleshooting information.

[0050] The search unit can suggest the optimal maintenance method when searching, taking into account the product's usage environment information. For example, the system can refer to the product's usage environment information and suggest the optimal maintenance method. The search unit can also suggest the frequency of maintenance based on the usage environment information. Furthermore, the search unit can refer to the system's usage environment information and suggest maintenance methods for specific problems. In this way, the optimal maintenance method can be suggested by considering the product's usage environment information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the usage environment information into a generating AI, and the generating AI can suggest the optimal maintenance method.

[0051] The display unit can highlight specific parts of the product to indicate points for self-checking. For example, the system can highlight important parts of the product to indicate points for self-checking. The display unit can also highlight problem areas and show the user how to repair them. Furthermore, the system can enlarge specific parts of the product to provide details of the self-check. This makes the points for self-checking clearer by highlighting specific parts of the product. Some or all of the above processing in the display unit may be performed using AI, for example, or not. For example, the display unit can input the highlighting of specific parts into a generating AI, and the generating AI can indicate points for self-checking.

[0052] The display unit can select the optimal display method by referring to the user's past self-check history when displaying information. For example, the system can refer to the user's past self-check history and select the optimal display method. The display unit can also provide the user with a display method suitable for them based on their past history. Furthermore, the system can analyze the user's past self-check history and propose the optimal display method. This allows the system to select the optimal display method by referring to the user's past self-check history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input past self-check history into a generating AI, which can then select the optimal display method.

[0053] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, the system may refer to the user's device information and select the optimal display method. The display unit can also provide a display method that matches the screen size of the device. Furthermore, the display unit can select the optimal display method by considering the device's performance. In this way, the optimal display method can be selected by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input device information into a generating AI, and the generating AI can select the optimal display method.

[0054] The display unit can set optimal display conditions when displaying information, taking into account the user's surrounding environment. For example, the system can detect ambient brightness and set optimal display conditions. The display unit can also detect ambient sound and apply noise reduction. Furthermore, the system can detect ambient temperature and optimize display conditions. In this way, optimal display conditions can be set by taking into account the user's surrounding environment. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input ambient environmental information into a generating AI, which can then set optimal display conditions.

[0055] The history storage unit can prioritize saving trouble history related to specific parts of a product when saving history. For example, the system can prioritize saving trouble history related to specific parts of a product. The history storage unit can also prioritize saving trouble history related to parts of high user interest. Furthermore, the history storage unit can also prioritize saving trouble history related to critical parts of a product. This allows for the rapid provision of important information by prioritizing the saving of trouble history related to specific parts of a product. Some or all of the above processing in the history storage unit may be performed using AI, for example, or without AI. For example, the history storage unit can input the saving of trouble history related to a specific part to a generating AI, and the generating AI can prioritize saving the trouble history.

[0056] The history storage unit can save the history while considering the product's usage environment information. For example, the system may refer to the product's usage environment information and save the history. The history storage unit can also adjust the history saving method based on the system's usage environment information. Furthermore, the history storage unit can provide the system with the optimal history saving method while considering the usage environment information. This ensures optimal history saving by considering the product's usage environment information. Some or all of the above processing in the history storage unit may be performed using AI, for example, or without AI. For example, the history storage unit can input the usage environment information into a generating AI, and the generating AI can save the history.

[0057] The history storage unit can select the optimal storage method by referring to the user's past self-check history when saving history. For example, the system can refer to the user's past self-check history and select the optimal storage method. The history storage unit can also provide the user with a suitable storage method based on past history. Furthermore, the history storage unit can analyze the system's past self-check history and propose the optimal storage method. This allows the system to select the optimal storage method by referring to the user's past self-check history. Some or all of the above processing in the history storage unit may be performed using AI, for example, or without AI. For example, the history storage unit can input past self-check history into a generating AI, which can then select the optimal storage method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The product troubleshooting system can also be equipped with an energy consumption monitoring unit. This unit monitors the product's energy consumption in real time and suggests efficient usage methods. For example, if a washing machine's energy consumption is high, the system might suggest using energy-saving mode. Furthermore, the energy consumption monitoring unit can analyze past energy consumption data and suggest optimal usage patterns to the user. It can also provide specific actions for the user to take to reduce energy consumption. As a result, using energy consumption monitoring improves the user's energy efficiency and reduces costs.

[0060] The product troubleshooting system can also include a parts replacement suggestion section. This section suggests replacing product parts when they are worn out. For example, if a washing machine filter is worn out, the system suggests purchasing a new filter. The parts replacement suggestion section can also display a list of parts that need replacing, making it easy for the user to replace them. Furthermore, it can provide purchase links for replacement parts, allowing the user to quickly obtain the parts. This improves product lifespan and prevents problems by utilizing parts replacement suggestions.

[0061] The product troubleshooting system may also include a user guide section. This section provides users with instructions on how to use the product correctly. For example, it might provide videos explaining the correct usage and maintenance of a washing machine. The user guide section can also provide step-by-step guidance for users using the product for the first time. Furthermore, it can offer tips and tricks to help users use the product efficiently. This improves the efficiency of user product use and prevents problems by utilizing the user guide.

[0062] The product troubleshooting system can also be equipped with an environmental adaptation unit. This unit automatically adjusts the optimal settings according to the product's operating environment. For example, if a washing machine is used in a high-humidity environment, the system automatically enhances the drying mode. The environmental adaptation unit can also adjust the maintenance frequency according to the product's operating environment. Furthermore, the environmental adaptation unit can monitor environmental information in real time as the user uses the product and suggest optimal usage methods. This means that using environmental adaptation improves the efficient use of the product and prevents problems from occurring.

[0063] The product troubleshooting system can also be equipped with a multilingual support section. This section allows users to access the system in different languages. For example, the system can support multiple languages ​​such as Japanese, English, and Chinese. Furthermore, the multilingual support section can display troubleshooting information and maintenance instructions according to the user's selected language. Additionally, the multilingual support section can enable users to input voice information in different languages, allowing the system to analyze the voice and provide appropriate information. This improves the convenience for users who speak different languages ​​and enhances the efficiency of troubleshooting through the use of multilingual support.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The shooting unit takes photos of the product using the user's camera. For example, a smartphone camera, digital camera, or tablet camera can be used. The shooting unit can also automatically adjust camera settings to obtain the optimal image. Step 2: The analysis unit analyzes the images captured by the imaging unit and automatically identifies the product. The analysis unit uses image recognition technology, deep learning, and pattern recognition technology to extract product features and identify the product by comparing them with a database. Step 3: The search unit searches for troubleshooting information and maintenance methods based on the product identified by the analysis unit. The search unit can also refer to databases and information on the internet, and provide search results based on past troubleshooting history. Step 4: The display unit displays the information acquired by the search unit on the camera image. The display unit can also display self-check points and filter cleaning methods, save the user's self-check history, and provide feedback.

[0066] (Example of form 2) The product troubleshooting system according to an embodiment of the present invention is a system that automatically identifies products using the camera of a mobile device, collects common problems, and feeds them back into the camera image. The product troubleshooting system works by having the user photograph a product with a problem using the camera of a smartphone or similar device. For example, the user can photograph problems that occur in daily life, such as plumbing issues or appliance malfunctions. This image is input into the system. Next, the product troubleshooting system analyzes the input image and automatically identifies the product. The system uses image recognition technology to extract product features and identifies the product by comparing them with a database. For example, the system analyzes an image of a washing machine and identifies a specific manufacturer and model. Based on the identified product, the product troubleshooting system searches for common problem information and maintenance methods. The system retrieves the relevant problem information and maintenance methods from the database and provides them to the user. For example, it searches for problem information and solutions related to washing machine water blockages. Finally, the product troubleshooting system displays self-check points on the camera image. The user can perform a self-check according to the displayed points and resolve the problem. For example, in the case of a washing machine water blockage, the system displays instructions on how to clean the filter, and the user cleans it accordingly. This mechanism allows the user to resolve problems through self-checks before requesting repairs from a professional. This is expected to reduce repair costs and speed up troubleshooting. Furthermore, the product troubleshooting system can save the user's self-check history, which can be used to help resolve future problems. For example, by referring to the history of past problems, it can respond quickly if similar problems recur. This allows the product troubleshooting system to automatically acquire troubleshooting information and maintenance methods when the user takes a picture of the product using a camera, and display them on the camera image.

[0067] The product troubleshooting system according to this embodiment comprises a shooting unit, an analysis unit, a search unit, and a display unit. The shooting unit allows the user to photograph the product using a camera. When the user photographs the product using a camera, for example, a smartphone camera can be used. The shooting unit takes an image of the product using, for example, a smartphone camera. The shooting unit can also use a digital camera or a tablet camera. For example, a digital camera can be used to take a high-resolution image. Furthermore, the shooting unit can automatically adjust the camera settings to obtain the optimal image. For example, it can automatically adjust the camera's exposure and white balance. The analysis unit analyzes the image taken by the shooting unit and automatically identifies the product. The analysis unit extracts product features using, for example, image recognition technology. For example, it analyzes product features using deep learning. The analysis unit can also identify products using pattern recognition technology. For example, it identifies products based on their shape and color. Furthermore, the analysis unit identifies products by comparing them with a database. For example, it compares product features with a database to identify a specific manufacturer or model. The search unit searches for troubleshooting information and maintenance methods based on the product identified by the analysis unit. For example, the search unit retrieves relevant troubleshooting information and maintenance methods from a database. For example, it searches for troubleshooting information and solutions related to washing machine water blockages. The search unit can also search for information on the internet. For example, it refers to the product's official website and user reviews. Furthermore, the search unit can provide search results based on past troubleshooting history. For example, it may suggest solutions for similar problems that have occurred in the past. The display unit displays the information obtained by the search unit on the camera image. For example, the display unit may display self-check points on the camera image. For example, it may display instructions on how to clean the filter. The display unit can also save the user's self-check history. For example, it may record the content of past self-checks. Furthermore, the display unit can provide feedback to the user. For example, it may display advice based on the results of the self-check.As a result, the product troubleshooting system according to this embodiment can automatically acquire troubleshooting information and maintenance methods when a user takes a picture of the product using a camera, and display them on the camera image.

[0068] The photography unit allows users to photograph products using a camera. Users can use, for example, a smartphone camera to photograph the product. The photography unit can also use digital cameras or tablet cameras. For example, a digital camera can capture high-resolution images. Furthermore, the photography unit can automatically adjust camera settings to obtain optimal images. For example, it can automatically adjust the camera's exposure and white balance. The photography unit also has a function to automatically focus the camera when the user photographs the product. This allows users to easily capture clear images without special skills or knowledge. Additionally, the photography unit has a function to capture multiple images in sequence and combine them to generate high-precision images. For example, it can combine images taken from different angles of a product to generate a 3D model. This allows the analysis unit to provide information for more accurate product identification. The photography unit also has a function to automatically upload images taken by the user to the cloud, making them accessible to the analysis and search units. This eliminates the need for users to manually transfer images. Furthermore, the camera unit also has a function to provide real-time feedback on images captured by the user. For example, if a captured image is unclear, it can display a message prompting the user to retake the image. This allows users to always capture the best possible image, improving the overall accuracy and reliability of the system.

[0069] The analysis unit analyzes images captured by the imaging unit and automatically identifies products. The analysis unit extracts product features using, for example, image recognition technology. For example, it analyzes product features using deep learning. The analysis unit can also identify products using pattern recognition technology. For example, it can identify products based on their shape and color. Furthermore, the analysis unit identifies products by comparing them with a database. For example, it compares product features with a database to identify specific manufacturers and models. The analysis unit utilizes deep learning-based image recognition algorithms to extract subtle product features with high accuracy. For example, it can identify product logos and specific design patterns. This makes it possible to accurately identify different manufacturers and models even within the same product category. In addition to shape and color, the analysis unit can also analyze differences in texture and material. For example, it can distinguish between metal and plastic parts. Furthermore, the analysis unit can analyze the product's usage and the extent of damage. For example, if there are scratches or stains on the product surface, it can identify their location and size and infer the cause of the problem. Based on this information, the analysis unit comprehensively evaluates the product's condition and proposes the optimal troubleshooting method. Furthermore, the analysis unit can continuously improve its accuracy by learning from past analysis data. For example, it can improve its analysis algorithms based on user feedback, achieving more accurate product identification. This allows the analysis unit to always utilize the latest technology and data to provide highly accurate analysis.

[0070] The search unit searches for troubleshooting information and maintenance methods based on the product identified by the analysis unit. For example, the search unit retrieves relevant troubleshooting information and maintenance methods from a database. For instance, it searches for troubleshooting information and solutions related to washing machine clogs. The search unit can also search information on the internet, such as the product's official website or user reviews. Furthermore, the search unit can provide search results based on past troubleshooting history, such as suggesting solutions for similar problems in the past. Based on the product information provided by the analysis unit, the search unit quickly searches for relevant troubleshooting information and maintenance methods. For example, it can identify the optimal solution based on the product model number, manufacturing year, and information on specific parts. The search unit utilizes not only database information but also the latest information from the internet. For example, it can retrieve the latest maintenance guides and troubleshooting information from the product's official website and provide it to the user. It can also collect information on problems experienced by other users and their solutions by referring to user reviews and forum posts. Furthermore, the search unit suggests solutions for similar problems based on past troubleshooting history, such as identifying the most effective solution by referring to the history of problems that have occurred with the same product in the past. The search unit comprehensively evaluates this information and proposes the optimal solution to the user. Furthermore, based on user search history and feedback, the search unit continuously improves its search algorithm, enabling it to provide more accurate search results. This allows the search unit to provide quick and accurate solutions to problems users face and support product maintenance.

[0071] The display unit displays information acquired by the search unit on the camera image. For example, the display unit can display self-check points on the camera image, such as how to clean the filter. The display unit can also save the user's self-check history, recording the details of past self-checks. Furthermore, the display unit can provide feedback to the user, displaying advice based on the self-check results. The display unit provides an interface to clearly display information provided by the search unit to the user. For example, it can overlay information on the camera image to visually indicate points the user should check, allowing the user to intuitively understand the specific steps. The display unit also has a function to provide real-time feedback when the user performs a self-check. For example, when the user cleans the filter, it can sequentially display the correct procedure and guide the user to the next step according to their progress. The display unit also saves the user's self-check history for later reference, allowing the user to review past maintenance history and use it as a reference for regular maintenance. Furthermore, the display unit can provide customized advice to the user. For example, if a particular problem occurs frequently, the display unit can identify the cause and suggest preventative measures. Through these functions, the display unit provides support to help users quickly and effectively resolve product problems. Furthermore, based on user feedback, the display unit can continuously improve its display content and interface to provide a more user-friendly system. In this way, the display unit becomes a powerful tool for users to resolve product problems themselves and supports product maintenance.

[0072] The display unit can display self-check points on the camera image. For example, the display unit can overlay self-check points on the camera image. For example, it can display instructions on how to clean the filter on the camera image. The display unit can also highlight self-check points. For example, it can highlight important parts in red. Furthermore, the display unit can display self-check points with animation. For example, it can use arrows or icons to show the self-check procedure. This allows the user to confirm the self-check points on the camera image. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input self-check points into a generating AI, and the generating AI can display the self-check points on the camera image.

[0073] The search unit can retrieve relevant trouble information and maintenance methods from the database. For example, the search unit can search the database for trouble information. For example, it can retrieve trouble information regarding clogged washing machines. The search unit can also search the database for maintenance methods. For example, it can retrieve instructions on how to clean a filter. Furthermore, the search unit can search the database for past trouble history. For example, it can retrieve solutions to past troubles. This allows the search unit to retrieve trouble information and maintenance methods from the database. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the search for trouble information and maintenance methods into a generating AI, and the generating AI can output the search results.

[0074] The analysis unit can identify products by extracting product features using image recognition technology and comparing them with a database. The analysis unit can analyze product features using, for example, deep learning. For example, it can identify products based on their shape and color. The analysis unit can also identify products using pattern recognition technology. For example, it can identify products based on their logo or label. Furthermore, the analysis unit identifies products by comparing them with a database. For example, it can compare product features with a database to identify a specific manufacturer or model. This improves the accuracy of product identification using image recognition technology. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input product features into a generating AI, which can then identify the product.

[0075] The display unit may include a history storage unit that stores the user's self-check history. The display unit can, for example, record the content of the self-check performed by the user. For example, it can store the history of filter cleaning. The display unit can also store the results of the self-check. For example, it can record whether a problem was resolved as a result of the self-check. Furthermore, the display unit can store the self-check history in a database. For example, past self-check history can be stored in a database to help resolve future problems. This allows the user's self-check history to be saved. Some or all of the above-described processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the self-check history into a generating AI, and the generating AI can save the history.

[0076] The history storage unit can save past trouble histories to help resolve future problems. For example, the history storage unit can record the details of past troubles. For example, it can save trouble histories related to washing machine water blockages. The history storage unit can also save methods for resolving troubles. For example, it can record methods for cleaning filters. Furthermore, the history storage unit can save the date and time when troubles occurred. For example, it can record the date and time when troubles occurred. By saving past trouble histories, it can be used to help resolve future problems. Some or all of the above processing in the history storage unit may be performed using AI, for example, or without AI. For example, the history storage unit can input trouble histories into a generating AI, and the generating AI can save the history.

[0077] The camera unit can estimate the user's emotions and adjust the timing of the shot based on the estimated emotions. For example, if the user is anxious, the system will automatically take a picture at the optimal time. The camera unit can also wait until the user presses the shutter button if the user is relaxed. Furthermore, if the user is feeling anxious, the system can display a guide message to prompt the user to take a picture. This allows for optimal timing of the shot by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input user emotion data into a generative AI, which can then adjust the timing of the shot.

[0078] The camera unit can automatically adjust the angle and distance of the product during shooting to acquire the optimal image. For example, the system can automatically adjust the angle of the product and shoot from the optimal viewpoint. The camera unit can also automatically adjust the distance to the product to acquire a clear image. Furthermore, the camera unit can shoot from multiple angles so that the system can capture both the overall image and details of the product in a balanced way. This allows for the acquisition of the optimal image by automatically adjusting the angle and distance of the product. Some or all of the above processing in the camera unit may be performed using AI, for example, or not using AI. For example, the camera unit can input the adjustment of the product's angle and distance to a generating AI, which can then acquire the optimal image.

[0079] The imaging unit can apply filters to highlight specific parts of a product during imaging. For example, the imaging unit may apply a specific color filter to highlight important parts of the product. The imaging unit may also apply an edge detection filter to highlight problem areas on the product. Furthermore, the imaging unit may apply a zoom filter to enlarge specific parts of the product. This makes important parts clearer by highlighting specific parts of the product. Some or all of the above processing in the imaging unit may be performed using AI, for example, or not using AI. For example, the imaging unit can input the application of a filter to highlight specific parts of the product to a generating AI, which can then apply the filter.

[0080] The camera unit can estimate the user's emotions and determine the priority of products to photograph based on the estimated emotions. For example, if the user is anxious, the system will prioritize photographing the most important products. If the user is relaxed, the system can photograph products in order. Furthermore, if the user is feeling uneasy, the system can prioritize photographing products that are likely to cause problems. This allows for the priority of photographing important products by determining the priority of products to photograph based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input user emotion data into a generative AI, which can then determine the priority of products to photograph.

[0081] The camera unit can automatically select the optimal shooting settings by referring to the user's past shooting history during shooting. For example, the system can analyze the user's past shooting history and automatically select the optimal exposure setting. The system can also automatically set the optimal white balance based on the user's past shooting history. Furthermore, the system can automatically select the optimal focus setting by referring to the user's past shooting history. In this way, the optimal shooting settings can be automatically selected by referring to the user's past shooting history. Some or all of the above processes in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the user's past shooting history into a generating AI, which can then select the optimal shooting settings.

[0082] The shooting unit can set optimal shooting conditions by considering the user's surrounding environment during shooting. For example, the system can detect ambient brightness and automatically set the optimal exposure. The system can also detect ambient sound and apply noise reduction. Furthermore, the system can detect ambient temperature and optimize camera settings. In this way, optimal shooting conditions can be set by considering the user's surrounding environment. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input ambient environmental information into a generating AI, which can then set the optimal shooting conditions.

[0083] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is anxious, the system can improve the accuracy of the analysis and provide results quickly. The analysis unit can also perform a more detailed analysis if the user is relaxed. Furthermore, if the user is feeling uneasy, the system can provide more reliable analysis results. In this way, by adjusting the accuracy of the analysis based on the user's emotions, the system can provide optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI, which can then adjust the accuracy of the analysis.

[0084] The analysis unit can automatically detect and identify abnormal parts of a product during analysis. For example, the analysis unit can perform image analysis to automatically detect abnormal parts of the product. The analysis unit can also identify the abnormal parts and notify the user. Furthermore, the analysis unit can provide detailed information about the abnormal parts and suggest repair methods to the user. This enables a rapid response by automatically detecting and identifying abnormal parts of the product. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the detection and identification of abnormal parts into a generating AI, which can then detect and identify the abnormal parts.

[0085] The analysis unit can identify the cause of an anomaly by referring to the product's usage history during analysis. For example, the system analyzes the product's usage history and identifies the cause of the anomaly. The analysis unit can also evaluate the frequency of anomalies based on the usage history. Furthermore, the analysis unit can refer to the usage history and explain the cause of the anomaly to the user. In this way, the cause of an anomaly can be identified by referring to the product's usage history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the analysis of the usage history and the identification of the cause of the anomaly into a generating AI, which can then identify the cause of the anomaly.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, the optimal display can be achieved by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the display method of the analysis results.

[0087] The analysis unit can improve the accuracy of its analysis by referring to the product's manufacturer and model information during the analysis process. For example, the system can refer to the product's manufacturer information to improve the accuracy of the analysis. The analysis unit can also improve the accuracy of its analysis based on the product's model information. Furthermore, the analysis unit can refer to the manufacturer and model information to perform analysis for specific problems. This improves the accuracy of the analysis by referring to the product's manufacturer and model information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input manufacturer and model information into a generating AI, which can then improve the accuracy of the analysis.

[0088] The analysis unit can correct the analysis results by taking into account the product's usage environment information during the analysis. For example, the system may refer to the product's usage environment information and correct the analysis results. The analysis unit can also improve the reliability of the analysis results based on the usage environment information. Furthermore, the system may consider the usage environment information and identify the cause of the anomaly. This improves the reliability of the analysis results by considering the product's usage environment information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the usage environment information into a generating AI, and the generating AI can correct the analysis results.

[0089] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit can provide a simple and highly visible display. If the user is relaxed, the search unit can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, the search unit can provide a concise display. This allows for optimal display by adjusting the search results display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI, which can then adjust how search results are displayed.

[0090] The search unit can prioritize displaying troubleshooting information related to specific parts of a product during a search. For example, the system can prioritize displaying troubleshooting information related to specific parts of a product. The search unit can also prioritize displaying troubleshooting information related to parts of high user interest. Furthermore, the search unit can prioritize displaying troubleshooting information related to critical parts of a product. This allows for the rapid provision of important information by prioritizing the display of troubleshooting information related to specific parts of a product. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the priority display of troubleshooting information related to specific parts into a generating AI, and the generating AI can prioritize displaying the troubleshooting information.

[0091] The search unit can suggest the optimal maintenance method by referring to the product's usage history during a search. For example, the system can refer to the product's usage history and suggest the optimal maintenance method. The search unit can also suggest the frequency of maintenance based on the usage history. Furthermore, the search unit can refer to the system's usage history and suggest maintenance methods for specific problems. In this way, the optimal maintenance method can be suggested by referring to the product's usage history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the usage history into a generating AI, and the generating AI can suggest the optimal maintenance method.

[0092] The search unit can estimate the user's emotions and prioritize search results based on those emotions. For example, if the user is anxious, the system will prioritize displaying the most important trouble information. If the user is relaxed, the system can also display trouble information in order. Furthermore, if the user is feeling uneasy, the system can prioritize displaying reliable trouble information. This allows for the rapid provision of important information by prioritizing search results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input user emotion data into a generative AI, which can then determine the priority of search results.

[0093] The search unit can display optimal troubleshooting information by referencing the product's manufacturer and model information during a search. For example, the system can refer to the product's manufacturer information and display the optimal troubleshooting information. The search unit can also display optimal troubleshooting information based on the product's model information. Furthermore, the search unit can refer to the manufacturer and model information and display information for a specific problem. This allows the system to display optimal troubleshooting information by referencing the product's manufacturer and model information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input manufacturer and model information into a generating AI, which can then display the optimal troubleshooting information.

[0094] The search unit can suggest the optimal maintenance method when searching, taking into account the product's usage environment information. For example, the system can refer to the product's usage environment information and suggest the optimal maintenance method. The search unit can also suggest the frequency of maintenance based on the usage environment information. Furthermore, the search unit can refer to the system's usage environment information and suggest maintenance methods for specific problems. In this way, the optimal maintenance method can be suggested by considering the product's usage environment information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the usage environment information into a generating AI, and the generating AI can suggest the optimal maintenance method.

[0095] The display unit can estimate the user's emotions and adjust the displayed content based on the estimated emotions. For example, if the user is tense, the display unit can provide a simple and highly visible display method. If the user is relaxed, the display unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a display method that gets straight to the point. This allows for optimal display by adjusting the displayed content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI, and the generative AI can adjust the displayed content.

[0096] The display unit can highlight specific parts of the product to indicate points for self-checking. For example, the system can highlight important parts of the product to indicate points for self-checking. The display unit can also highlight problem areas and show the user how to repair them. Furthermore, the system can enlarge specific parts of the product to provide details of the self-check. This makes the points for self-checking clearer by highlighting specific parts of the product. Some or all of the above processing in the display unit may be performed using AI, for example, or not. For example, the display unit can input the highlighting of specific parts into a generating AI, and the generating AI can indicate points for self-checking.

[0097] The display unit can select the optimal display method by referring to the user's past self-check history when displaying information. For example, the system can refer to the user's past self-check history and select the optimal display method. The display unit can also provide the user with a display method suitable for them based on their past history. Furthermore, the system can analyze the user's past self-check history and propose the optimal display method. This allows the system to select the optimal display method by referring to the user's past self-check history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input past self-check history into a generating AI, which can then select the optimal display method.

[0098] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is anxious, the system will prioritize displaying the most important information. If the user is relaxed, the system can also display information in order. Furthermore, if the user is feeling uneasy, the system can prioritize displaying reliable information. This allows for the rapid delivery of important information by determining the display priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user emotion data into a generative AI, which can then determine the display priority.

[0099] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, the system may refer to the user's device information and select the optimal display method. The display unit can also provide a display method that matches the screen size of the device. Furthermore, the display unit can select the optimal display method by considering the device's performance. In this way, the optimal display method can be selected by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input device information into a generating AI, and the generating AI can select the optimal display method.

[0100] The display unit can set optimal display conditions when displaying information, taking into account the user's surrounding environment. For example, the system can detect ambient brightness and set optimal display conditions. The display unit can also detect ambient sound and apply noise reduction. Furthermore, the system can detect ambient temperature and optimize display conditions. In this way, optimal display conditions can be set by taking into account the user's surrounding environment. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input ambient environmental information into a generating AI, which can then set optimal display conditions.

[0101] The history storage unit can estimate the user's emotions and adjust the history storage method based on the estimated emotions. For example, if the user is anxious, the system will automatically save the history. The history storage unit can also allow the user to choose the storage method if they are relaxed. Furthermore, if the user is feeling anxious, the system can provide a more reliable storage method. This allows for optimal storage by adjusting the history storage method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history storage unit may be performed using AI or not. For example, the history storage unit can input user emotion data into a generative AI, which can then adjust the history storage method.

[0102] The history storage unit can prioritize saving trouble history related to specific parts of a product when saving history. For example, the system can prioritize saving trouble history related to specific parts of a product. The history storage unit can also prioritize saving trouble history related to parts of high user interest. Furthermore, the history storage unit can also prioritize saving trouble history related to critical parts of a product. This allows for the rapid provision of important information by prioritizing the saving of trouble history related to specific parts of a product. Some or all of the above processing in the history storage unit may be performed using AI, for example, or without AI. For example, the history storage unit can input the saving of trouble history related to a specific part to a generating AI, and the generating AI can prioritize saving the trouble history.

[0103] The history storage unit can estimate the user's emotions and adjust the frequency of saving history based on the estimated emotions. For example, if the user is anxious, the system will save history more frequently. The history storage unit can also save history at a moderate frequency if the user is relaxed. Furthermore, if the user is feeling uneasy, the system can save history at a reliable frequency. This allows for optimal storage by adjusting the history saving frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history storage unit may be performed using AI, or not. For example, the history storage unit can input user emotion data into the generative AI, which can then adjust the history saving frequency.

[0104] The history storage unit can save the history while considering the product's usage environment information. For example, the system may refer to the product's usage environment information and save the history. The history storage unit can also adjust the history saving method based on the system's usage environment information. Furthermore, the history storage unit can provide the system with the optimal history saving method while considering the usage environment information. This ensures optimal history saving by considering the product's usage environment information. Some or all of the above processing in the history storage unit may be performed using AI, for example, or without AI. For example, the history storage unit can input the usage environment information into a generating AI, and the generating AI can save the history.

[0105] The history storage unit can select the optimal storage method by referring to the user's past self-check history when saving history. For example, the system can refer to the user's past self-check history and select the optimal storage method. The history storage unit can also provide the user with a suitable storage method based on past history. Furthermore, the history storage unit can analyze the system's past self-check history and propose the optimal storage method. This allows the system to select the optimal storage method by referring to the user's past self-check history. Some or all of the above processing in the history storage unit may be performed using AI, for example, or without AI. For example, the history storage unit can input past self-check history into a generating AI, which can then select the optimal storage method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The product troubleshooting system can also be equipped with a voice input unit. This unit allows users to describe the details of the problem verbally. For example, if a user says, "The washing machine isn't working," the system analyzes the voice and identifies the type of problem. Furthermore, the voice input unit improves convenience because it allows users to operate the system hands-free. Additionally, the voice input unit can estimate the user's emotions from their voice and display a message prompting a quick response if the user appears anxious. This improves both user convenience and the speed of troubleshooting through the use of voice input.

[0108] The product troubleshooting system can also include a remote support section. This remote support section allows users to have real-time video calls with experts when they encounter problems. For example, if a user is unable to resolve a washing machine issue, an expert can provide direct advice via video call. Furthermore, the remote support section improves troubleshooting accuracy because experts can provide specific instructions while viewing the user's camera feed. Additionally, the remote support section can estimate the user's emotions, and if the user is feeling anxious, experts can send reassuring messages. This enhances user confidence and improves the accuracy of troubleshooting through the use of remote support.

[0109] The product troubleshooting system can also include a preventative maintenance unit. This unit monitors product usage and suggests maintenance before problems occur. For example, it might suggest cleaning the filter if the washing machine has been used a certain number of times. The preventative maintenance unit can also detect abnormal product behavior and prompt the user for early maintenance. Furthermore, it can estimate the user's mood and display a message emphasizing the importance of maintenance if the user is relaxed. This allows for improved problem prevention and user peace of mind through the use of preventative maintenance.

[0110] The product troubleshooting system can also be equipped with a learning unit. This unit analyzes the user's troubleshooting history and provides information to help with future troubleshooting. For example, it can notify users who have previously cleaned their washing machine filter when the next cleaning is due. The learning unit can also learn the user's troubleshooting tendencies and suggest the optimal solution. Furthermore, it can estimate the user's emotions and suggest a concise solution if the user is feeling anxious. This learning function improves the efficiency and accuracy of user troubleshooting.

[0111] The product troubleshooting system can also include a community support section. This section allows users to share information with other users and receive advice on troubleshooting. For example, if a user posts a question about a washing machine problem, other users can suggest solutions. The community support section can also estimate the user's emotions and display encouraging messages from the community if the user is feeling anxious. Furthermore, the community support section can share the user's troubleshooting history for other users to refer to. This improves user confidence and the efficiency of troubleshooting through the use of community support.

[0112] The product troubleshooting system can also be equipped with an energy consumption monitoring unit. This unit monitors the product's energy consumption in real time and suggests efficient usage methods. For example, if a washing machine's energy consumption is high, the system might suggest using energy-saving mode. Furthermore, the energy consumption monitoring unit can analyze past energy consumption data and suggest optimal usage patterns to the user. It can also provide specific actions for the user to take to reduce energy consumption. As a result, using energy consumption monitoring improves the user's energy efficiency and reduces costs.

[0113] The product troubleshooting system can also include a parts replacement suggestion section. This section suggests replacing product parts when they are worn out. For example, if a washing machine filter is worn out, the system suggests purchasing a new filter. The parts replacement suggestion section can also display a list of parts that need replacing, making it easy for the user to replace them. Furthermore, it can provide purchase links for replacement parts, allowing the user to quickly obtain the parts. This improves product lifespan and prevents problems by utilizing parts replacement suggestions.

[0114] The product troubleshooting system may also include a user guide section. This section provides users with instructions on how to use the product correctly. For example, it might provide videos explaining the correct usage and maintenance of a washing machine. The user guide section can also provide step-by-step guidance for users using the product for the first time. Furthermore, it can offer tips and tricks to help users use the product efficiently. This improves the efficiency of user product use and prevents problems by utilizing the user guide.

[0115] The product troubleshooting system can also be equipped with an environmental adaptation unit. This unit automatically adjusts the optimal settings according to the product's operating environment. For example, if a washing machine is used in a high-humidity environment, the system automatically enhances the drying mode. The environmental adaptation unit can also adjust the maintenance frequency according to the product's operating environment. Furthermore, the environmental adaptation unit can monitor environmental information in real time as the user uses the product and suggest optimal usage methods. This means that using environmental adaptation improves the efficient use of the product and prevents problems from occurring.

[0116] The product troubleshooting system can also be equipped with a multilingual support section. This section allows users to access the system in different languages. For example, the system can support multiple languages ​​such as Japanese, English, and Chinese. Furthermore, the multilingual support section can display troubleshooting information and maintenance instructions according to the user's selected language. Additionally, the multilingual support section can enable users to input voice information in different languages, allowing the system to analyze the voice and provide appropriate information. This improves the convenience for users who speak different languages ​​and enhances the efficiency of troubleshooting.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The shooting unit takes photos of the product using the user's camera. For example, a smartphone camera, digital camera, or tablet camera can be used. The shooting unit can also automatically adjust camera settings to obtain the optimal image. Step 2: The analysis unit analyzes the images captured by the imaging unit and automatically identifies the product. The analysis unit uses image recognition technology, deep learning, and pattern recognition technology to extract product features and identify the product by comparing them with a database. Step 3: The search unit searches for troubleshooting information and maintenance methods based on the product identified by the analysis unit. The search unit can also refer to databases and information on the internet, and can provide search results based on past troubleshooting history. Step 4: The display unit displays the information acquired by the search unit on the camera image. The display unit can also display self-check points and filter cleaning methods, save the user's self-check history, and provide feedback.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the imaging unit, analysis unit, search unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the smart device 14 to capture an image of the product. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to extract product features using image recognition technology and compare them with the database 24. The search unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to obtain trouble information and maintenance methods from the database 24. The display unit uses the display 40A of the smart device 14 to display self-check points on the camera image. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the imaging unit, analysis unit, search unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the smart glasses 214 to capture an image of the product. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which extracts product features using image recognition technology and compares them with the database 24. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which obtains trouble information and maintenance methods from the database 24. The display unit uses the display of the smart glasses 214 to display self-check points on the camera image. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the imaging unit, analysis unit, search unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the headset terminal 314 to capture an image of the product. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which extracts product features using image recognition technology and compares them with the database 24. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which obtains trouble information and maintenance methods from the database 24. The display unit uses the display 343 of the headset terminal 314 to display self-check points on the camera image. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the imaging unit, analysis unit, search unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the robot 414 to capture images of the product. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which extracts product features using image recognition technology and compares them with the database 24. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which obtains trouble information and maintenance methods from the database 24. The display unit uses the display of the robot 414 to display self-check points on the camera image. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) The photography section allows users to take pictures of products using their cameras, An analysis unit analyzes the images captured by the aforementioned imaging unit and automatically identifies the product, A search unit searches for trouble information and maintenance methods based on the product identified by the analysis unit, The system includes a display unit that displays the information acquired by the search unit on the camera image. A system characterized by the following features. (Note 2) The aforementioned display unit is Self-check points are displayed on the camera image. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, Retrieve relevant troubleshooting information and maintenance methods from the database. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Product features are extracted using image recognition technology and identified by comparing them with a database. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is It includes a history storage unit that stores the user's self-check history. The system described in Appendix 1, characterized by the features described herein. (Note 6) The history storage unit is, To help resolve future problems, we save a history of past issues. The system described in Appendix 5, characterized by the features described herein. (Note 7) The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is During shooting, the system automatically adjusts the angle and distance of the product to obtain the optimal image. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is When taking a photo, apply a filter to highlight specific parts of the product. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is It estimates the user's emotions and determines the priority of products to photograph based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is During shooting, the system automatically selects the optimal shooting settings by referring to the user's past shooting history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned imaging unit is When shooting, the system takes into account the user's surrounding environment to set the optimal shooting conditions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the system automatically detects and identifies abnormal parts of the product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the cause of the anomaly is identified by referring to the product's usage history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the product manufacturer and model information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis results are corrected by taking into account the product's usage environment information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, When searching, prioritize displaying troubleshooting information related to specific parts of the product. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, During the search, the system will refer to the product's usage history to suggest the most suitable maintenance method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, When searching, the system displays the most relevant troubleshooting information by referencing the product manufacturer and model information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned search unit, When searching, we suggest the optimal maintenance method considering the product's usage environment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is It estimates the user's emotions and adjusts the displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displayed, specific parts of the product are highlighted to indicate points for self-checking. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is When displaying the information, the system will refer to the user's past self-check history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is When displaying content, the optimal display conditions are set considering the user's surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The history storage unit is, It estimates the user's emotions and adjusts how history is saved based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 32) The history storage unit is, When saving the history, prioritize saving the trouble history related to specific parts of the product. The system described in Appendix 5, characterized by the features described herein. (Note 33) The history storage unit is, The system estimates the user's emotions and adjusts the frequency of saving history based on those emotions. The system described in Appendix 5, characterized by the features described herein. (Note 34) The history storage unit is, When saving the history, the product's usage environment information is taken into consideration when saving the history. The system described in Appendix 5, characterized by the features described herein. (Note 35) The history storage unit is, When saving the history, the system refers to the user's past self-check history to select the optimal saving method. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The photography section allows users to take pictures of products using their cameras, An analysis unit analyzes the images captured by the aforementioned imaging unit and automatically identifies the product, A search unit searches for trouble information and maintenance methods based on the product identified by the analysis unit, The system includes a display unit that displays the information acquired by the search unit on the camera image. A system characterized by the following features.

2. The aforementioned display unit is Self-check points are displayed on the camera image. The system according to feature 1.

3. The aforementioned search unit, Retrieve relevant troubleshooting information and maintenance methods from the database. The system according to feature 1.

4. The aforementioned analysis unit, Product features are extracted using image recognition technology and identified by comparing them with a database. The system according to feature 1.

5. The aforementioned display unit is It includes a history storage unit that stores the user's self-check history. The system according to feature 1.

6. The history storage unit is, To help resolve future problems, we save a history of past issues. The system according to claim 5, characterized in that it is the same as described in claim 5.

7. The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system according to feature 1.

8. The aforementioned imaging unit is During shooting, the system automatically adjusts the angle and distance of the product to obtain the optimal image. The system according to feature 1.

Images