system

Abstract

The system according to the embodiment aims to efficiently search and organize instructions for home appliances. [Solution] A system according to an embodiment includes an analysis unit, a search unit, a download unit, a storage unit, and an organization unit. The analysis unit analyzes a photo of the home appliance to identify the model number. The search unit searches for instructions based on the model number identified by the analysis unit. The download unit downloads the instructions found by the search unit. The storage unit stores the instructions downloaded by the download unit. The organization unit organizes the instructions stored by the storage unit by category.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was time-consuming to search for instructions for home appliances, and it was difficult to find accurate instructions due to differences in model numbers.

[0005] The system according to the embodiment aims to efficiently search and organize instructions for home appliances. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a search unit, a download unit, a storage unit, and an organization unit. The analysis unit analyzes a photo of the home appliance to identify the model number. The search unit searches for instructions based on the model number identified by the analysis unit. The download unit downloads the instructions searched for by the search unit. The storage unit stores the instructions downloaded by the download unit. The organization unit organizes the instructions stored by the storage unit by category. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search and organize instructions for home appliances. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The instruction manual app according to an embodiment of the present invention is a system designed to eliminate the need to search for and retrieve manuals for home appliances. This instruction manual app automatically imports the manuals of appliances when a user takes a photo of the appliance, allowing for centralized management of all appliance manuals in the home. This allows for the organization of bulky manuals and space savings. Specifically, the system consists of the following steps: First, a user takes a photo of an appliance. Next, the app analyzes the photo to identify the appliance's model number and model. Based on the identified information, the app searches for and downloads the corresponding manual from the Internet. The downloaded manual is stored within the app, allowing the user to easily access it at any time. For example, when a user takes a photo of a refrigerator, the app identifies the refrigerator's model number and automatically downloads the corresponding manual. This eliminates the need for users to search for the refrigerator's manual and allows them to quickly access the information they need. The app also has a function to organize manuals by category, allowing users to easily search for manuals by appliance type or manufacturer. Furthermore, the app also has a function to perform text search within the manuals, allowing users to quickly find specific information. Using this app significantly reduces the effort required to search for appliance manuals and saves space. This allows the instruction manual app to efficiently manage the instruction manuals of home appliances and enable users to quickly access the information they need.

[0029] An instruction manual app according to an embodiment includes an analysis unit, a search unit, a download unit, a storage unit, and an organization unit. The analysis unit analyzes a photo of the home appliance to identify the model number. For example, the analysis unit analyzes the photo of the home appliance using an image recognition algorithm to identify the model number. The analysis unit can also extract features of the home appliance using a machine learning model to identify the model number. For example, the analysis unit receives a photo of the home appliance as input and identifies the model number using an image recognition algorithm. The analysis unit can also extract features of the home appliance using a machine learning model to identify the model number. The search unit searches for instructions based on the model number identified by the analysis unit. The search unit, for example, searches an online database to find corresponding instructions. The search unit can also search for instructions using a search engine. For example, the search unit receives the identified model number as input and searches an online database to find instructions. The search unit can also search for instructions using a search engine. The download unit downloads the instructions found by the search unit. For example, the download unit downloads the instructions by specifying a file format. The download unit can also adjust the download speed to download the instructions. For example, the download unit downloads the manual found by the search unit in a specified file format. The download unit can also download the manual by adjusting the download speed. The storage unit stores the manual downloaded by the download unit. For example, the storage unit stores the manual in a specific folder. The storage unit can also store the manual in cloud storage. For example, the storage unit stores the downloaded manual in a specific folder. The storage unit can also store the manual in cloud storage. The organizing unit organizes the manuals stored by the storage unit by category. For example, the organizing unit organizes the manuals by type of home appliance. The organizing unit can also organize the manuals by manufacturer. For example, the organizing unit organizes the stored manuals by type of home appliance. The organizing unit can also organize the manuals by manufacturer.As a result, the instruction manual app according to the embodiment can efficiently manage the instruction manuals for home appliances and enable the user to quickly access the information he or she needs.

[0030] The instruction manual app includes a scanning unit that scans a two-dimensional code (e.g., a QR code (registered trademark)) or a barcode. The scanning unit scans the two-dimensional code or barcode to identify the model number of the home appliance. The scanning unit, for example, uses a camera to scan the two-dimensional code or barcode. The scanning unit can also scan the two-dimensional code or barcode using a dedicated scanner. For example, the scanning unit can scan the two-dimensional code of the home appliance using a camera to identify the model number. The scanning unit can also scan the barcode of the home appliance using a dedicated scanner to identify the model number. In this way, the model number of the home appliance can be quickly identified by scanning the two-dimensional code or barcode. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input image data of the two-dimensional code or barcode acquired by the camera to a generation AI and cause the generation AI to identify the model number from the image data.

[0031] The instruction manual app includes an update unit that automatically updates the instructions. The update unit automatically updates the instructions. For example, the update unit periodically checks an online database and downloads the latest instructions. The update unit can also be manually triggered by a user. For example, the update unit checks an online database every week and automatically downloads the latest instructions. The update unit can also be manually triggered by a user to download the latest instructions. This allows the instructions to be automatically updated, ensuring that the latest information is always maintained. Some or all of the above-described processing in the update unit may be performed using AI, for example, or may be performed without using AI. For example, the update unit can cause a generation AI to execute a process of checking an online database.

[0032] The instruction manual app includes a priority display unit that analyzes the contents of the instruction manual and prioritizes displaying them. The priority display unit analyzes the contents of the instruction manual and prioritizes displaying important information. The priority display unit, for example, analyzes the contents of the instruction manual and extracts information of high importance. The priority display unit can also identify important information based on a user's search history or usage history. For example, the priority display unit analyzes the contents of the instruction manual and extracts and displays information of high importance. The priority display unit can also identify important information based on a user's search history or usage history and display it preferentially. This allows the user to quickly access necessary information by displaying important information preferentially. Some or all of the above-described processing in the priority display unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority display unit may cause a generation AI to execute a process of analyzing the contents of the instruction manual.

[0033] The instruction manual app includes a voice reading unit that reads the contents of the instruction manual aloud. The voice reading unit reads the contents of the instruction manual aloud. The voice reading unit reads the contents of the instruction manual aloud, for example, using voice synthesis technology. The voice reading unit can also adjust the reading speed and volume. For example, the voice reading unit reads the contents of the instruction manual aloud using voice synthesis technology. The voice reading unit can also adjust the reading speed and volume to provide a voice reading that suits the user's preferences. By reading the instructions aloud, information can be acquired without relying on vision. Some or all of the above-described processing in the voice reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice reading unit can input the contents of the instruction manual to a generation AI and have the generation AI perform voice synthesis.

[0034] When analyzing a photograph of a home appliance, the analysis unit can remove background information to improve the accuracy of the analysis. For example, the analysis unit can automatically remove background walls and furniture from the photograph of the home appliance to clarify the outline of the appliance. The analysis unit can also remove unnecessary shadows and reflections from the photograph of the home appliance to make the model number characters clearer. For example, the analysis unit can remove other objects from the photograph of the home appliance to highlight the features of the appliance. In this way, removing background information improves the accuracy of identifying the model number of the home appliance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a photograph of the home appliance to a generation AI and cause the generation AI to remove background information.

[0035] The analysis unit can take photos of the home appliance from multiple angles and integrate the analysis results to identify the model number. For example, the analysis unit can take photos of the front and back of the home appliance and integrate both pieces of information to identify the model number. The analysis unit can also take photos of the top and side of the home appliance and analyze them from multiple perspectives to identify the model number. For example, the analysis unit can take a full view of the home appliance and partial close-up photos and integrate detailed information to identify the model number. In this way, by integrating information from multiple angles, the accuracy of model number identification is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input photos of the home appliance taken from multiple angles to the generation AI and have the generation AI integrate the analysis results.

[0036] When analyzing a photo of a home appliance, the analysis unit can prioritize analysis of region-specific model numbers based on the user's location information. For example, the analysis unit prioritizes analysis of model numbers sold in a specific region based on the user's location information. The analysis unit can also prioritize analysis of region-specific models based on the user's location information. For example, the analysis unit can identify model numbers by referring to regional sales data based on the user's location information. This prioritizes analysis of region-specific model numbers, thereby improving analysis accuracy. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's location information to the generation AI and cause the generation AI to perform a prioritized analysis of region-specific model numbers.

[0037] When analyzing a photo of a home appliance, the analysis unit can improve the analysis accuracy by referring to the user's past analysis history. For example, the analysis unit prioritizes analysis of home appliances with the same model number based on the user's past analysis history. The analysis unit can also prioritize analysis of similar model numbers based on the user's past analysis history. For example, the analysis unit can learn patterns for improving analysis accuracy based on the user's past analysis history. In this way, by referring to the past analysis history, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis history into the generation AI and cause the generation AI to improve the analysis accuracy.

[0038] When performing a search, the search unit can simultaneously search multiple databases on the Internet and integrate the results. For example, the search unit can simultaneously search the official website of a home appliance manufacturer and a third-party database and integrate the results. The search unit can also simultaneously search domestic and international databases to provide the most relevant results. For example, the search unit can simultaneously use multiple search engines and integrate and display the results. This allows for simultaneous searching of multiple databases to provide more information. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input multiple databases into a generation AI and have the generation AI integrate the search results.

[0039] During the search, the search unit can also search for related instructions taking into account the similarity of the model number. For example, the search unit prioritizes searching for instructions with a partial matching model number. The search unit can also search for related instructions based on the similarity of the model number. For example, the search unit can search for instructions from the same series that have a partial difference in model number. This allows related instructions to be provided by taking into account the similarity of the model number. Some or all of the above-described processing in the search unit may be performed using, or without, AI. For example, the search unit can input the similarity of the model number into the generation AI and cause the generation AI to search for related instructions.

[0040] During a search, the search unit can improve the accuracy of search results by referring to the user's past search history. For example, the search unit may prioritize searching for home appliances with the same model number based on the user's past search history. The search unit may also prioritize searching for similar model numbers based on the user's past search history. For example, the search unit may learn patterns for improving search accuracy based on the user's past search history. As a result, the accuracy of search results is improved by referring to the past search history. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the user's past search history into a generation AI and cause the generation AI to improve search accuracy.

[0041] The search unit can automatically select the language of the instructions based on the user's language setting during a search. The search unit automatically selects the language of the instructions based on, for example, the language setting of the user's device. The search unit can also provide a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the search unit can provide the instructions in that language. This enables more appropriate information to be provided by selecting the language of the instructions based on the user's language setting. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's language setting into a generation AI and have the generation AI select the language of the instructions.

[0042] The download unit can automatically convert the file format of the instruction manual when downloading and save it. For example, the download unit automatically converts the PDF format of the instruction manual into a format suitable for the user's device and saves it. The download unit can also automatically convert the file format of the instruction manual based on a user specification and save it. For example, the download unit can automatically convert the file format of the instruction manual into a format suitable for cloud storage and save it. By automatically converting the file format, the instruction manual can be saved in a format suitable for the user's device. Some or all of the above-mentioned processing in the download unit may be performed using, or without, AI, for example. For example, the download unit can input the file format of the instruction manual into a generation AI and have the generation AI convert the file format.

[0043] The download unit can compress the size of the instruction manual when downloading it and save it. For example, the download unit can compress the file size of the instruction manual to save storage space on the device. The download unit can also compress the file size of the instruction manual to save cloud storage space. For example, the download unit can compress the file size of the instruction manual to shorten the download time. This allows storage space to be saved by compressing the file size. Some or all of the above-described processing in the download unit may be performed using AI, for example, or may be performed without using AI. For example, the download unit can input the file size of the instruction manual to a generation AI and have the generation AI compress the file size.

[0044] When downloading, the download unit can select the optimal download method taking into account the user's network environment. For example, if the user is in a high-speed network environment, the download unit selects the fastest download method. Furthermore, if the user is in a slow network environment, the download unit can also select a stable download method. For example, if the user is using mobile data, the download unit can select a download method that minimizes data usage. This improves download efficiency by selecting the optimal download method according to the network environment. Some or all of the above-described processing in the download unit can be performed using, for example, AI, or without AI. For example, the download unit can input the user's network environment data into the generation AI and cause the generation AI to select the optimal download method.

[0045] When downloading, the download unit can select a storage location taking into account the storage status of the user's device. For example, if the user's device has limited storage, the download unit stores the data in cloud storage. Alternatively, if the user's device has sufficient storage, the download unit can store the data in local storage. For example, the download unit can select an optimal storage location depending on the storage status of the user's device. This enables efficient use of storage by selecting an optimal storage location depending on the device's storage status. Some or all of the above-described processing in the download unit may be performed using, for example, AI, or may be performed without using AI. For example, the download unit can input storage status data of the user's device into the generation AI and have the generation AI select an optimal storage location.

[0046] The storage unit can automatically generate and add metadata for the manual when storing the manual. The storage unit automatically generates and adds metadata such as the title, author, and publication date of the manual. The storage unit can also automatically generate and add metadata such as the category, tags, and keywords of the manual. For example, the storage unit can automatically generate and add metadata such as the file format, size, and creation date of the manual. This makes it easier to manage the manual by automatically generating metadata. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can cause a generation AI to generate metadata for the manual.

[0047] The storage unit performs version management of the instructions when saving them, and can also save past versions. For example, when a new version of the instructions is added, the storage unit also saves past versions. The storage unit can also manage the version history of the instructions, allowing users to access past versions. For example, the storage unit can perform version management of the instructions and record the change history. In this way, past versions can be accessed by performing version management. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can have the generation AI perform version management of the instructions.

[0048] When saving, the storage unit can save the instructions in cooperation with the user's cloud storage service. The storage unit, for example, automatically saves the instructions in the user's cloud storage service. The storage unit can also save the instructions in a cloud storage service specified by the user. For example, the storage unit can also automatically create a backup of the instructions in cooperation with the user's cloud storage service. This makes it easy to back up the instructions by coordinating with the cloud storage service. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can cause the generation AI to execute the coordination with the cloud storage service.

[0049] When saving, the storage unit can select the optimal storage method taking into account the storage status of the user's device. For example, if the storage of the user's device is low, the storage unit stores the data in cloud storage. Furthermore, if the storage of the user's device is sufficient, the storage unit can also store the data in local storage. For example, the storage unit can select the optimal storage method depending on the storage status of the user's device. This enables efficient use of storage by providing the optimal storage method depending on the storage status of the device. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input storage status data of the user's device to the generation AI and have the generation AI select the optimal storage method.

[0050] The organizing unit can automatically generate and classify categories for the manuals when organizing them. The organizing unit automatically generates and classifies categories based on, for example, the content of the manuals. The organizing unit can also automatically generate and classify categories based on the model number or manufacturer of the manuals. For example, the organizing unit can automatically generate and classify categories based on the intended use of the manuals. This makes it easier to manage the manuals by automatically generating categories. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can cause a generation AI to generate categories for the manuals.

[0051] When organizing, the organizing unit can adjust the display order based on the importance of the manuals. For example, the organizing unit displays the most important manuals at the top based on how frequently the manuals are used. The organizing unit can also adjust the display order based on the importance of the contents of the manuals. For example, the organizing unit can display the most important manuals at the top based on how frequently the manuals are updated. This allows quick access to necessary information by providing a display order based on importance. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can cause the generating AI to evaluate the importance of the manuals.

[0052] When organizing, the organizing unit can select the optimal organizing method by referring to the user's past organizing history. For example, the organizing unit prioritizes organizing instructions in the same category based on the user's past organizing history. The organizing unit can also prioritize organizing similar categories based on the user's past organizing history. For example, the organizing unit can also suggest the optimal organizing method based on the user's past organizing history. By referring to the past organizing history, the accuracy of organization is improved. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the user's past organizing history into the generation AI and cause the generation AI to select the optimal organizing method.

[0053] When organizing, the organizing unit can adjust the display method taking into account the screen size of the user's device. For example, if the user is using a smartphone, the organizing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the organizing unit can also provide a display method optimized for a large screen. For example, if the user is using a desktop, the organizing unit can also provide a display method that includes detailed information. This improves visibility by providing a display method that matches the screen size of the device. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input screen size data of the user's device to the generation AI and cause the generation AI to select the optimal display method.

[0054] The scanning unit can automatically detect the position of a 2D code or barcode during scanning and scan it. The scanning unit, for example, automatically detects the position of a 2D code or barcode on a home appliance and scans it. The scanning unit can also automatically detect the position of a 2D code or barcode on a home appliance to improve scanning accuracy. For example, the scanning unit can automatically detect the position of a 2D code or barcode on a home appliance and improve scanning speed. This improves scanning accuracy by automatically detecting the position of the 2D code or barcode. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can cause a generating AI to detect the position of the 2D code or barcode.

[0055] The scanning unit can simultaneously scan and analyze multiple 2D codes or barcodes during scanning. For example, the scanning unit simultaneously scans and analyzes multiple 2D codes or barcodes on home appliances. The scanning unit can also simultaneously scan multiple 2D codes or barcodes on home appliances to improve analysis accuracy. For example, the scanning unit can simultaneously scan multiple 2D codes or barcodes on home appliances to improve analysis speed. Thus, by simultaneously scanning multiple 2D codes or barcodes, scanning efficiency is improved. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can cause a generation AI to scan and analyze multiple 2D codes or barcodes.

[0056] The scanning unit can select the optimal scanning method during scanning, taking into account the camera performance of the user's device. For example, the scanning unit selects the optimal scanning method based on the camera performance of the user's device. The scanning unit can also improve scanning accuracy based on the camera performance of the user's device. For example, the scanning unit can also improve scanning speed based on the camera performance of the user's device. This improves scanning accuracy by providing the optimal scanning method according to the camera performance of the device. Some or all of the above-described processing in the scanning unit can be performed using AI, for example, or without AI. For example, the scanning unit can input camera performance data of the user's device to the generation AI and cause the generation AI to select the optimal scanning method.

[0057] When scanning, the scanning unit can improve scanning accuracy by referring to the user's past scanning history. For example, the scanning unit prioritizes scanning home appliances with the same model number based on the user's past scanning history. The scanning unit can also prioritize scanning similar model numbers based on the user's past scanning history. For example, the scanning unit can learn patterns for improving scanning accuracy based on the user's past scanning history. In this way, scanning accuracy is improved by referring to the past scanning history. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the user's past scanning history into the generation AI and cause the generation AI to improve scanning accuracy.

[0058] The update unit can automatically record and save the change history of the instruction manual when updating. For example, the update unit can automatically record the change history of the instruction manual, allowing the user to access past versions. The update unit can also automatically record the change history of the instruction manual, allowing the user to check the changes. For example, the update unit can automatically record the change history of the instruction manual, allowing the user to manage the change history. In this way, by automatically recording the change history, past versions can also be accessed. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit can cause the generation AI to record the change history of the instruction manual.

[0059] The update unit can notify the user when a new version of the manual is available during an update. For example, the update unit notifies the user when a new version of the manual is available. The update unit can also automatically suggest to the user to download the new version when it is available. For example, the update unit can notify the user of the update content when a new version of the manual is available. This allows the user to always maintain the latest information by notifying when a new version is available. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can cause the generation AI to notify the user of the new version.

[0060] During an update, the update unit can select the optimal update method taking into account the user's network environment. For example, if the user is in a high-speed network environment, the update unit selects the fastest update method. Furthermore, if the user is in a slow network environment, the update unit can also select a stable update method. For example, if the user is using mobile data, the update unit can select an update method that minimizes data usage. This improves update efficiency by providing the optimal update method according to the network environment. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the user's network environment data into the generation AI and cause the generation AI to select the optimal update method.

[0061] During an update, the update unit can manage update data taking into account the storage status of the user's device. For example, if the user's device has limited storage, the update unit stores the update data in cloud storage. Furthermore, if the user's device has sufficient storage, the update unit can also store the update data in local storage. For example, the update unit can select an optimal update data management method depending on the storage status of the user's device. This enables efficient use of storage by providing an optimal update data management method depending on the device's storage status. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input storage status data of the user's device to the generation AI and cause the generation AI to perform optimal update data management.

[0062] The priority display unit can adjust the display order based on the importance of the manuals during priority display. The priority display unit can display the most important manuals at the top based on, for example, how frequently the manuals are used. The priority display unit can also adjust the display order based on the importance of the contents of the manuals. For example, the priority display unit can display the most important manuals at the top based on how frequently the manuals are updated. This allows quick access to necessary information by providing a display order based on importance. Some or all of the above-mentioned processing in the priority display unit can be performed using, for example, AI, or can be performed without using AI. For example, the priority display unit can cause a generation AI to evaluate the importance of the manuals.

[0063] The priority display unit can adjust the display order based on the frequency of use of the manuals during priority display. For example, the priority display unit can display frequently used manuals at the top based on the frequency of use of the manuals. The priority display unit can also display less frequently used manuals at the bottom based on the frequency of use of the manuals. For example, the priority display unit can adjust the optimal display order based on the frequency of use of the manuals. This allows quick access to necessary information by providing a display order based on the frequency of use. Some or all of the above-mentioned processing in the priority display unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority display unit can input frequency of use data of the manuals into a generation AI and have the generation AI adjust the optimal display order.

[0064] During priority display, the priority display unit can select the optimal display method by referring to the user's past display history. The priority display unit, for example, prioritizes displaying instructions of the same category based on the user's past display history. The priority display unit can also prioritize displaying similar categories based on the user's past display history. For example, the priority display unit can also suggest the optimal display method based on the user's past display history. This improves the accuracy of the display by referring to the past display history. Some or all of the above-described processing in the priority display unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority display unit can input the user's past display history into a generation AI and cause the generation AI to select the optimal display method.

[0065] The priority display unit can adjust the display method during priority display, taking into account the screen size of the user's device. For example, if the user is using a smartphone, the priority display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the priority display unit can also provide a display method optimized for a large screen. For example, if the user is using a desktop, the priority display unit can also provide a display method that includes detailed information. This improves visibility by providing a display method that matches the device's screen size. Some or all of the above-described processing in the priority display unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority display unit can input screen size data of the user's device to the generation AI and cause the generation AI to select the optimal display method.

[0066] The voice reading unit can emphasize important parts of the instructions when reading aloud. For example, the voice reading unit can emphasize important parts of the instructions and read them slowly. The voice reading unit can also emphasize important parts of the instructions and read them at a higher volume. For example, the voice reading unit can emphasize important parts of the instructions and read them in a specific tone. This allows for quick access to necessary information by emphasizing important parts when reading them. Some or all of the above-described processing in the voice reading unit may be performed using AI, for example, or may be performed without using AI. For example, the voice reading unit can have a generating AI execute the emphasis on important parts of the instructions.

[0067] The voice reading unit can summarize and read out the contents of the instructions when reading out loud. For example, the voice reading unit can summarize the contents of the instructions and read them concisely. The voice reading unit can also summarize the contents of the instructions and read them out while emphasizing important points. For example, the voice reading unit can summarize the contents of the instructions and read them out in a way that is easy for the user to understand. This allows the necessary information to be accessed quickly by summarizing and reading out the contents. Some or all of the above-mentioned processing in the voice reading unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice reading unit can have a generation AI execute a summary of the instructions.

[0068] The text-to-speech unit can automatically select a text-to-speech language based on the user's language setting when reading aloud. The text-to-speech unit automatically selects a text-to-speech language based on, for example, the language setting of the user's device. The text-to-speech unit can also provide a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the text-to-speech unit can read in that language. This makes it possible to provide more appropriate information by providing a text-to-speech language based on the language setting. Some or all of the above-described processing in the text-to-speech unit may be performed using, or without, AI, for example. For example, the text-to-speech unit can input the user's language setting data into a generation AI and have the generation AI select the optimal text-to-speech language.

[0069] When reading aloud, the text-to-speech unit can read at an optimal volume taking into account the volume setting of the user's device. The text-to-speech unit can read at an optimal volume based on, for example, the volume setting of the user's device. The text-to-speech unit can also automatically adjust the volume based on the volume setting of the user's device. For example, the text-to-speech unit can optimize the volume based on the volume setting of the user's device. This makes it possible to provide more appropriate information by providing an optimal volume according to the volume setting of the device. Some or all of the above-described processing in the text-to-speech unit may be performed using, or without, AI, for example. For example, the text-to-speech unit can input volume setting data of the user's device to the generation AI and cause the generation AI to adjust the volume to the optimal level.

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

[0071] The instruction manual app may also include a voice recognition unit that recognizes user voice commands. For example, when a user issues a voice command such as "Show me the refrigerator manual," the voice recognition unit analyzes the command and automatically displays the corresponding manual. Furthermore, when a user asks a question about a specific function or setting, the voice recognition unit can extract and provide an answer to the question from the manual. For example, when a user asks, "How do I set the eco mode on my washing machine?", the voice recognition unit analyzes the question and displays the relevant part of the manual. This allows a user to access the manual without using their hands by using voice commands.

[0072] The instruction manual app can provide region-specific information based on the user's location information. For example, if the user is in a specific region, it will prioritize displaying instructions for home appliances sold in that region. Also, if the user is traveling, it can provide instructions for home appliances used in the region to which the user is traveling. For example, when a user is traveling abroad and uses local home appliances, it can provide instructions written in the local language. This makes it possible to provide information based on the user's location information.

[0073] The instruction app can suggest the optimal shooting method taking into account the camera performance of the user's device. For example, if the user's device camera is high-performance, it can suggest taking detailed photos. Also, if the user's device camera is low-performance, it can suggest taking photos from an appropriate distance or angle. For example, if the user is taking photos in a dark place, it can suggest using a flash. This improves the accuracy of analysis by providing the optimal shooting method according to the device's camera performance.

[0074] The instruction manual app can provide optimal search results by referring to the user's past search history. For example, it can prioritize displaying instructions for home appliances that the user has previously searched for. It can also display related instructions based on keywords that the user has previously searched for. For example, it can display related instructions based on the series of home appliances that the user has previously searched for. In this way, by referring to the user's past search history, the accuracy of search results can be improved.

[0075] The instruction manual app can suggest the optimal storage method taking into account the storage situation of the user's device. For example, if the storage of the user's device is low, it can suggest saving to cloud storage. Also, if the storage of the user's device is sufficient, it can suggest saving to local storage. For example, it can suggest saving frequently accessed instructions to the user in local storage and saving less frequently used instructions to cloud storage. This allows for efficient use of storage by providing the optimal storage method according to the storage situation of the device.

[0076] The processing flow of the first embodiment will be briefly explained below.

[0077] Step 1: The analysis unit analyzes a photo of the home appliance to identify the model number. The analysis unit uses an image recognition algorithm or machine learning model to extract the features of the home appliance and identify the model number. For example, it receives a photo of the home appliance as input and uses an image recognition algorithm to identify the model number. Step 2: The search unit searches for the instruction manual based on the model number identified by the analysis unit. The search unit finds the relevant instruction manual using an online database or search engine. For example, it receives the identified model number as input and searches an online database to find the instruction manual. Step 3: The download unit downloads the manuals found by the search unit. The download unit can specify the file format of the manuals to download and adjust the download speed. For example, the download unit downloads the manuals found by the search unit in the specified file format. Step 4: The storage unit stores the instruction manual downloaded by the download unit. The storage unit can store the instruction manual in a specific folder or in cloud storage. For example, the storage unit stores the downloaded instruction manual in a specific folder. Step 5: The organizing unit organizes the instructions stored by the storage unit by category. The organizing unit can organize the instructions by type of home appliance or by manufacturer. For example, the stored instructions are organized by type of home appliance.

[0078] (Example 2) The instruction manual app according to an embodiment of the present invention is a system designed to eliminate the need to search for and retrieve manuals for home appliances. This instruction manual app automatically imports the manuals of appliances when a user takes a photo of the appliance, allowing for centralized management of all appliance manuals in the home. This allows for the organization of bulky manuals and space savings. Specifically, the system consists of the following steps: First, a user takes a photo of an appliance. Next, the app analyzes the photo to identify the appliance's model number and model. Based on the identified information, the app searches for and downloads the corresponding manual from the Internet. The downloaded manual is stored within the app, allowing the user to easily access it at any time. For example, when a user takes a photo of a refrigerator, the app identifies the refrigerator's model number and automatically downloads the corresponding manual. This eliminates the need for users to search for the refrigerator's manual and allows them to quickly access the information they need. The app also has a function to organize manuals by category, allowing users to easily search for manuals by appliance type or manufacturer. Furthermore, the app also has a function to perform text search within the manuals, allowing users to quickly find specific information. Using this app significantly reduces the effort required to search for appliance manuals and saves space. This allows the instruction manual app to efficiently manage the instruction manuals of home appliances and enable users to quickly access the information they need.

[0079] An instruction manual app according to an embodiment includes an analysis unit, a search unit, a download unit, a storage unit, and an organization unit. The analysis unit analyzes a photo of the home appliance to identify the model number. For example, the analysis unit analyzes the photo of the home appliance using an image recognition algorithm to identify the model number. The analysis unit can also extract features of the home appliance using a machine learning model to identify the model number. For example, the analysis unit receives a photo of the home appliance as input and identifies the model number using an image recognition algorithm. The analysis unit can also extract features of the home appliance using a machine learning model to identify the model number. The search unit searches for instructions based on the model number identified by the analysis unit. The search unit, for example, searches an online database to find corresponding instructions. The search unit can also search for instructions using a search engine. For example, the search unit receives the identified model number as input and searches an online database to find instructions. The search unit can also search for instructions using a search engine. The download unit downloads the instructions found by the search unit. For example, the download unit downloads the instructions by specifying a file format. The download unit can also adjust the download speed to download the instructions. For example, the download unit downloads the manual found by the search unit in a specified file format. The download unit can also download the manual by adjusting the download speed. The storage unit stores the manual downloaded by the download unit. For example, the storage unit stores the manual in a specific folder. The storage unit can also store the manual in cloud storage. For example, the storage unit stores the downloaded manual in a specific folder. The storage unit can also store the manual in cloud storage. The organizing unit organizes the manuals stored by the storage unit by category. For example, the organizing unit organizes the manuals by type of home appliance. The organizing unit can also organize the manuals by manufacturer. For example, the organizing unit organizes the stored manuals by type of home appliance. The organizing unit can also organize the manuals by manufacturer.As a result, the instruction manual app according to the embodiment can efficiently manage the instruction manuals for home appliances and enable the user to quickly access the information he or she needs.

[0080] The instruction manual app includes a scanning unit that scans a two-dimensional code (e.g., a QR code) or a barcode. The scanning unit scans the two-dimensional code or barcode to identify the model number of the home appliance. The scanning unit, for example, uses a camera to scan the two-dimensional code or barcode. The scanning unit can also scan the two-dimensional code or barcode using a dedicated scanner. For example, the scanning unit can scan the two-dimensional code of the home appliance using a camera to identify the model number. The scanning unit can also scan the barcode of the home appliance using a dedicated scanner to identify the model number. In this way, the model number of the home appliance can be quickly identified by scanning the two-dimensional code or barcode. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input image data of the two-dimensional code or barcode acquired by the camera to a generation AI and cause the generation AI to identify the model number from the image data.

[0081] The instruction manual app includes an update unit that automatically updates the instructions. The update unit automatically updates the instructions. For example, the update unit periodically checks an online database and downloads the latest instructions. The update unit can also be manually triggered by a user. For example, the update unit checks an online database every week and automatically downloads the latest instructions. The update unit can also be manually triggered by a user to download the latest instructions. This allows the instructions to be automatically updated, ensuring that the latest information is always maintained. Some or all of the above-described processing in the update unit may be performed using AI, for example, or may be performed without using AI. For example, the update unit can cause a generation AI to execute a process of checking an online database.

[0082] The instruction manual app includes a priority display unit that analyzes the contents of the instruction manual and prioritizes displaying them. The priority display unit analyzes the contents of the instruction manual and prioritizes displaying important information. The priority display unit, for example, analyzes the contents of the instruction manual and extracts information of high importance. The priority display unit can also identify important information based on a user's search history or usage history. For example, the priority display unit analyzes the contents of the instruction manual and extracts and displays information of high importance. The priority display unit can also identify important information based on a user's search history or usage history and display it preferentially. This allows the user to quickly access necessary information by displaying important information preferentially. Some or all of the above-described processing in the priority display unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority display unit may cause a generation AI to execute a process of analyzing the contents of the instruction manual.

[0083] The instruction manual app includes a voice reading unit that reads the contents of the instruction manual aloud. The voice reading unit reads the contents of the instruction manual aloud. The voice reading unit reads the contents of the instruction manual aloud, for example, using voice synthesis technology. The voice reading unit can also adjust the reading speed and volume. For example, the voice reading unit reads the contents of the instruction manual aloud using voice synthesis technology. The voice reading unit can also adjust the reading speed and volume to provide a voice reading that suits the user's preferences. By reading the instructions aloud, information can be acquired without relying on vision. Some or all of the above-described processing in the voice reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice reading unit can input the contents of the instruction manual to a generation AI and have the generation AI perform voice synthesis.

[0084] The analysis unit can estimate the user's emotions and optimize the accuracy of the analysis based on the estimated user emotions. For example, when the user is stressed, the analysis unit can increase the accuracy of the analysis and provide results quickly. Furthermore, when the user is relaxed, the analysis unit can maintain the accuracy of the analysis at normal levels and provide detailed information. For example, when the user is in a hurry, the analysis unit can increase the accuracy of the analysis and provide results quickly. This allows for adjusting the analysis accuracy according to the user's emotions to provide more appropriate analysis results. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0085] When analyzing a photograph of a home appliance, the analysis unit can remove background information to improve the accuracy of the analysis. For example, the analysis unit can automatically remove background walls and furniture from the photograph of the home appliance to clarify the outline of the appliance. The analysis unit can also remove unnecessary shadows and reflections from the photograph of the home appliance to make the model number characters clearer. For example, the analysis unit can remove other objects from the photograph of the home appliance to highlight the features of the appliance. In this way, removing background information improves the accuracy of identifying the model number of the home appliance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a photograph of the home appliance to a generation AI and cause the generation AI to remove background information.

[0086] The analysis unit can take photos of the home appliance from multiple angles and integrate the analysis results to identify the model number. For example, the analysis unit can take photos of the front and back of the home appliance and integrate both pieces of information to identify the model number. The analysis unit can also take photos of the top and side of the home appliance and analyze them from multiple perspectives to identify the model number. For example, the analysis unit can take a full view of the home appliance and partial close-up photos and integrate detailed information to identify the model number. In this way, by integrating information from multiple angles, the accuracy of model number identification is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input photos of the home appliance taken from multiple angles to the generation AI and have the generation AI integrate the analysis results.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0088] When analyzing a photo of a home appliance, the analysis unit can prioritize analysis of region-specific model numbers based on the user's location information. For example, the analysis unit prioritizes analysis of model numbers sold in a specific region based on the user's location information. The analysis unit can also prioritize analysis of region-specific models based on the user's location information. For example, the analysis unit can identify model numbers by referring to regional sales data based on the user's location information. This prioritizes analysis of region-specific model numbers, thereby improving analysis accuracy. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's location information to the generation AI and cause the generation AI to perform a prioritized analysis of region-specific model numbers.

[0089] When analyzing a photo of a home appliance, the analysis unit can improve the analysis accuracy by referring to the user's past analysis history. For example, the analysis unit prioritizes analysis of home appliances with the same model number based on the user's past analysis history. The analysis unit can also prioritize analysis of similar model numbers based on the user's past analysis history. For example, the analysis unit can learn patterns for improving analysis accuracy based on the user's past analysis history. In this way, by referring to the past analysis history, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis history into the generation AI and cause the generation AI to improve the analysis accuracy.

[0090] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. For example, if the user is feeling stressed, the search unit can display the most relevant results at the top. Furthermore, if the user is relaxed, the search unit can also display results containing detailed information at the top. For example, if the user is in a hurry, the search unit can display the most quickly accessible results at the top. This allows for more appropriate information to be provided by adjusting the display order of search results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit can be performed using, for example, an AI, or without an AI. For example, the search unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0091] When performing a search, the search unit can simultaneously search multiple databases on the Internet and integrate the results. For example, the search unit can simultaneously search the official website of a home appliance manufacturer and a third-party database and integrate the results. The search unit can also simultaneously search domestic and international databases to provide the most relevant results. For example, the search unit can simultaneously use multiple search engines and integrate and display the results. This allows for simultaneous searching of multiple databases to provide more information. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input multiple databases into a generation AI and have the generation AI integrate the search results.

[0092] During the search, the search unit can also search for related instructions taking into account the similarity of the model number. For example, the search unit prioritizes searching for instructions with a partial matching model number. The search unit can also search for related instructions based on the similarity of the model number. For example, the search unit can search for instructions from the same series that have a partial difference in model number. This allows related instructions to be provided by taking into account the similarity of the model number. Some or all of the above-described processing in the search unit may be performed using, or without, AI. For example, the search unit can input the similarity of the model number into the generation AI and cause the generation AI to search for related instructions.

[0093] The search unit can estimate the user's emotions and filter search results based on the estimated user emotions. For example, if the user is stressed, the search unit can display only the most relevant results. Furthermore, if the user is relaxed, the search unit can display results containing detailed information. For example, if the user is in a hurry, the search unit can prioritize quickly accessible results. This allows for filtering search results according to the user's emotions, thereby providing more appropriate information. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit can be performed using, for example, an AI, or without an AI. For example, the search unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0094] During a search, the search unit can improve the accuracy of search results by referring to the user's past search history. For example, the search unit may prioritize searching for home appliances with the same model number based on the user's past search history. The search unit may also prioritize searching for similar model numbers based on the user's past search history. For example, the search unit may learn patterns for improving search accuracy based on the user's past search history. As a result, the accuracy of search results is improved by referring to the past search history. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the user's past search history into a generation AI and cause the generation AI to improve search accuracy.

[0095] The search unit can automatically select the language of the instructions based on the user's language setting during a search. The search unit automatically selects the language of the instructions based on, for example, the language setting of the user's device. The search unit can also provide a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the search unit can provide the instructions in that language. This enables more appropriate information to be provided by selecting the language of the instructions based on the user's language setting. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's language setting into a generation AI and have the generation AI select the language of the instructions.

[0096] The download unit can estimate the user's emotions and determine the download priority based on the estimated user's emotions. For example, if the user is feeling stressed, the download unit can prioritize downloading the most relevant instructions. Furthermore, if the user is relaxed, the download unit can prioritize downloading instructions containing detailed information. For example, if the user is in a hurry, the download unit can prioritize downloading instructions that can be accessed quickly. This enables more appropriate information to be provided by determining the download priority based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the download unit can be performed using, for example, an AI, or without an AI. For example, the download unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0097] The download unit can automatically convert the file format of the instruction manual when downloading and save it. For example, the download unit automatically converts the PDF format of the instruction manual into a format suitable for the user's device and saves it. The download unit can also automatically convert the file format of the instruction manual based on a user specification and save it. For example, the download unit can automatically convert the file format of the instruction manual into a format suitable for cloud storage and save it. By automatically converting the file format, the instruction manual can be saved in a format suitable for the user's device. Some or all of the above-mentioned processing in the download unit may be performed using, or without, AI, for example. For example, the download unit can input the file format of the instruction manual into a generation AI and have the generation AI convert the file format.

[0098] The download unit can compress the size of the instruction manual when downloading it and save it. For example, the download unit can compress the file size of the instruction manual to save storage space on the device. The download unit can also compress the file size of the instruction manual to save cloud storage space. For example, the download unit can compress the file size of the instruction manual to shorten the download time. This allows storage space to be saved by compressing the file size. Some or all of the above-described processing in the download unit may be performed using AI, for example, or may be performed without using AI. For example, the download unit can input the file size of the instruction manual to a generation AI and have the generation AI compress the file size.

[0099] When downloading, the download unit can select the optimal download method taking into account the user's network environment. For example, if the user is in a high-speed network environment, the download unit selects the fastest download method. Furthermore, if the user is in a slow network environment, the download unit can also select a stable download method. For example, if the user is using mobile data, the download unit can select a download method that minimizes data usage. This improves download efficiency by selecting the optimal download method according to the network environment. Some or all of the above-described processing in the download unit can be performed using, for example, AI, or without AI. For example, the download unit can input the user's network environment data into the generation AI and cause the generation AI to select the optimal download method.

[0100] When downloading, the download unit can select a storage location taking into account the storage status of the user's device. For example, if the user's device has limited storage, the download unit stores the data in cloud storage. Alternatively, if the user's device has sufficient storage, the download unit can store the data in local storage. For example, the download unit can select an optimal storage location depending on the storage status of the user's device. This enables efficient use of storage by selecting an optimal storage location depending on the device's storage status. Some or all of the above-described processing in the download unit may be performed using, for example, AI, or may be performed without using AI. For example, the download unit can input storage status data of the user's device into the generation AI and have the generation AI select an optimal storage location.

[0101] The storage unit can estimate the user's emotions and adjust the storage method based on the estimated user emotions. For example, if the user is stressed, the storage unit can provide a method that allows for quick storage. The storage unit can also provide detailed storage options if the user is relaxed. For example, if the user is in a hurry, the storage unit can provide a method that allows for quick storage. This allows for more appropriate storage by providing a storage method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the storage unit can be performed using AI, for example, or without AI. For example, the storage unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0102] The storage unit can automatically generate and add metadata for the manual when storing the manual. The storage unit automatically generates and adds metadata such as the title, author, and publication date of the manual. The storage unit can also automatically generate and add metadata such as the category, tags, and keywords of the manual. For example, the storage unit can automatically generate and add metadata such as the file format, size, and creation date of the manual. This makes it easier to manage the manual by automatically generating metadata. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can cause a generation AI to generate metadata for the manual.

[0103] The storage unit performs version management of the instructions when saving them, and can also save past versions. For example, when a new version of the instructions is added, the storage unit also saves past versions. The storage unit can also manage the version history of the instructions, allowing users to access past versions. For example, the storage unit can perform version management of the instructions and record the change history. In this way, past versions can be accessed by performing version management. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can have the generation AI perform version management of the instructions.

[0104] The storage unit can estimate the user's emotions and select a storage location based on the estimated user's emotions. For example, if the user is stressed, the storage unit selects the most quickly accessible storage location. The storage unit can also provide more detailed storage options if the user is relaxed. For example, if the user is in a hurry, the storage unit can select a quickly accessible storage location. This allows for more appropriate storage by providing a storage location according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0105] When saving, the storage unit can save the instructions in cooperation with the user's cloud storage service. The storage unit, for example, automatically saves the instructions in the user's cloud storage service. The storage unit can also save the instructions in a cloud storage service specified by the user. For example, the storage unit can also automatically create a backup of the instructions in cooperation with the user's cloud storage service. This makes it easy to back up the instructions by coordinating with the cloud storage service. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can cause the generation AI to execute the coordination with the cloud storage service.

[0106] When saving, the storage unit can select the optimal storage method taking into account the storage status of the user's device. For example, if the storage of the user's device is low, the storage unit stores the data in cloud storage. Furthermore, if the storage of the user's device is sufficient, the storage unit can also store the data in local storage. For example, the storage unit can select the optimal storage method depending on the storage status of the user's device. This enables efficient use of storage by providing the optimal storage method depending on the storage status of the device. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input storage status data of the user's device to the generation AI and have the generation AI select the optimal storage method.

[0107] The organizing unit can estimate the user's emotions and adjust the organizing method based on the estimated user emotions. For example, if the user is feeling stressed, the organizing unit can provide a simple and quick organizing method. The organizing unit can also provide detailed organizing options if the user is relaxed. For example, if the user is in a hurry, the organizing unit can provide a quick organizing method. This allows for more appropriate organizing by providing an organizing method that suits the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the organizing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the organizing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0108] The organizing unit can automatically generate and classify categories for the manuals when organizing them. The organizing unit automatically generates and classifies categories based on, for example, the content of the manuals. The organizing unit can also automatically generate and classify categories based on the model number or manufacturer of the manuals. For example, the organizing unit can automatically generate and classify categories based on the intended use of the manuals. This makes it easier to manage the manuals by automatically generating categories. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can cause a generation AI to generate categories for the manuals.

[0109] When organizing, the organizing unit can adjust the display order based on the importance of the manuals. For example, the organizing unit displays the most important manuals at the top based on how frequently the manuals are used. The organizing unit can also adjust the display order based on the importance of the contents of the manuals. For example, the organizing unit can display the most important manuals at the top based on how frequently the manuals are updated. This allows quick access to necessary information by providing a display order based on importance. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can cause the generating AI to evaluate the importance of the manuals.

[0110] The organizing unit can estimate the user's emotions and adjust the display method of the organized results based on the estimated user emotions. For example, if the user is feeling stressed, the organizing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the organizing unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the organizing unit can provide a display method that focuses on the main points. This enables more appropriate information to be provided by providing a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the organizing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the organizing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0111] When organizing, the organizing unit can select the optimal organizing method by referring to the user's past organizing history. For example, the organizing unit prioritizes organizing instructions in the same category based on the user's past organizing history. The organizing unit can also prioritize organizing similar categories based on the user's past organizing history. For example, the organizing unit can also suggest the optimal organizing method based on the user's past organizing history. By referring to the past organizing history, the accuracy of organization is improved. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the user's past organizing history into the generation AI and cause the generation AI to select the optimal organizing method.

[0112] When organizing, the organizing unit can adjust the display method taking into account the screen size of the user's device. For example, if the user is using a smartphone, the organizing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the organizing unit can also provide a display method optimized for a large screen. For example, if the user is using a desktop, the organizing unit can also provide a display method that includes detailed information. This improves visibility by providing a display method that matches the screen size of the device. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input screen size data of the user's device to the generation AI and cause the generation AI to select the optimal display method.

[0113] The scanning unit can estimate the user's emotions and adjust the scanning accuracy based on the estimated user emotions. For example, if the user is stressed, the scanning unit can increase the scanning accuracy and provide results quickly. Furthermore, if the user is relaxed, the scanning unit can maintain the scanning accuracy at normal levels and provide more detailed information. For example, if the user is in a hurry, the scanning unit can increase the scanning accuracy and provide results quickly. This allows for more appropriate scanning by providing scanning accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scanning unit can be performed using, for example, an AI, or without an AI. For example, the scanning unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0114] The scanning unit can automatically detect the position of a 2D code or barcode during scanning and scan it. The scanning unit, for example, automatically detects the position of a 2D code or barcode on a home appliance and scans it. The scanning unit can also automatically detect the position of a 2D code or barcode on a home appliance to improve scanning accuracy. For example, the scanning unit can automatically detect the position of a 2D code or barcode on a home appliance and improve scanning speed. This improves scanning accuracy by automatically detecting the position of the 2D code or barcode. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can cause a generating AI to detect the position of the 2D code or barcode.

[0115] The scanning unit can simultaneously scan and analyze multiple 2D codes or barcodes during scanning. For example, the scanning unit simultaneously scans and analyzes multiple 2D codes or barcodes on home appliances. The scanning unit can also simultaneously scan multiple 2D codes or barcodes on home appliances to improve analysis accuracy. For example, the scanning unit can simultaneously scan multiple 2D codes or barcodes on home appliances to improve analysis speed. Thus, by simultaneously scanning multiple 2D codes or barcodes, scanning efficiency is improved. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can cause a generation AI to scan and analyze multiple 2D codes or barcodes.

[0116] The scanning unit can estimate the user's emotions and adjust the display method of the scan results based on the estimated user emotions. For example, if the user is feeling stressed, the scanning unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the scanning unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the scanning unit can provide a display method that focuses on the main points. This enables more appropriate information to be provided by providing a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scanning unit can be performed using, for example, an AI, or without an AI. For example, the scanning unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0117] The scanning unit can select the optimal scanning method during scanning, taking into account the camera performance of the user's device. For example, the scanning unit selects the optimal scanning method based on the camera performance of the user's device. The scanning unit can also improve scanning accuracy based on the camera performance of the user's device. For example, the scanning unit can also improve scanning speed based on the camera performance of the user's device. This improves scanning accuracy by providing the optimal scanning method according to the camera performance of the device. Some or all of the above-described processing in the scanning unit can be performed using AI, for example, or without AI. For example, the scanning unit can input camera performance data of the user's device to the generation AI and cause the generation AI to select the optimal scanning method.

[0118] When scanning, the scanning unit can improve scanning accuracy by referring to the user's past scanning history. For example, the scanning unit prioritizes scanning home appliances with the same model number based on the user's past scanning history. The scanning unit can also prioritize scanning similar model numbers based on the user's past scanning history. For example, the scanning unit can learn patterns for improving scanning accuracy based on the user's past scanning history. In this way, scanning accuracy is improved by referring to the past scanning history. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the user's past scanning history into the generation AI and cause the generation AI to improve scanning accuracy.

[0119] The update unit can estimate the user's emotions and adjust the update frequency based on the estimated user emotions. For example, if the user is feeling stressed, the update unit can increase the update frequency to provide quick results. Furthermore, if the user is relaxed, the update unit can maintain the update frequency at a normal level and provide detailed information. For example, if the user is in a hurry, the update unit can increase the update frequency to provide quick results. This enables more appropriate updates by providing an update frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the update unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0120] The update unit can automatically record and save the change history of the instruction manual when updating. For example, the update unit can automatically record the change history of the instruction manual, allowing the user to access past versions. The update unit can also automatically record the change history of the instruction manual, allowing the user to check the changes. For example, the update unit can automatically record the change history of the instruction manual, allowing the user to manage the change history. In this way, by automatically recording the change history, past versions can also be accessed. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit can cause the generation AI to record the change history of the instruction manual.

[0121] The update unit can notify the user when a new version of the manual is available during an update. For example, the update unit notifies the user when a new version of the manual is available. The update unit can also automatically suggest to the user to download the new version when it is available. For example, the update unit can notify the user of the update content when a new version of the manual is available. This allows the user to always maintain the latest information by notifying when a new version is available. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can cause the generation AI to notify the user of the new version.

[0122] The update unit can estimate the user's emotion and adjust the display method of the update results based on the estimated user emotion. For example, if the user is feeling stressed, the update unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the update unit can also provide a display method including detailed information. For example, if the user is in a hurry, the update unit can provide a display method that focuses on the main points. This enables more appropriate information to be provided by providing a display method that corresponds to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the update unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0123] During an update, the update unit can select the optimal update method taking into account the user's network environment. For example, if the user is in a high-speed network environment, the update unit selects the fastest update method. Furthermore, if the user is in a slow network environment, the update unit can also select a stable update method. For example, if the user is using mobile data, the update unit can select an update method that minimizes data usage. This improves update efficiency by providing the optimal update method according to the network environment. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the user's network environment data into the generation AI and cause the generation AI to select the optimal update method.

[0124] During an update, the update unit can manage update data taking into account the storage status of the user's device. For example, if the user's device has limited storage, the update unit stores the update data in cloud storage. Furthermore, if the user's device has sufficient storage, the update unit can also store the update data in local storage. For example, the update unit can select an optimal update data management method depending on the storage status of the user's device. This enables efficient use of storage by providing an optimal update data management method depending on the device's storage status. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input storage status data of the user's device to the generation AI and cause the generation AI to perform optimal update data management.

[0125] The priority display unit can estimate the user's emotions and adjust the priority display criteria based on the estimated user emotions. For example, when the user is stressed, the priority display unit can prioritize displaying the most relevant instructions. Furthermore, when the user is relaxed, the priority display unit can prioritize displaying instructions containing detailed information. For example, when the user is in a hurry, the priority display unit can prioritize displaying instructions that can be accessed quickly. This allows for more appropriate information to be provided by providing priority display criteria according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the priority display unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the priority display unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0126] The priority display unit can adjust the display order based on the importance of the manuals during priority display. The priority display unit can display the most important manuals at the top based on, for example, how frequently the manuals are used. The priority display unit can also adjust the display order based on the importance of the contents of the manuals. For example, the priority display unit can display the most important manuals at the top based on how frequently the manuals are updated. This allows quick access to necessary information by providing a display order based on importance. Some or all of the above-mentioned processing in the priority display unit can be performed using, for example, AI, or can be performed without using AI. For example, the priority display unit can cause a generation AI to evaluate the importance of the manuals.

[0127] The priority display unit can adjust the display order based on the frequency of use of the manuals during priority display. For example, the priority display unit can display frequently used manuals at the top based on the frequency of use of the manuals. The priority display unit can also display less frequently used manuals at the bottom based on the frequency of use of the manuals. For example, the priority display unit can adjust the optimal display order based on the frequency of use of the manuals. This allows quick access to necessary information by providing a display order based on the frequency of use. Some or all of the above-mentioned processing in the priority display unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority display unit can input frequency of use data of the manuals into a generation AI and have the generation AI adjust the optimal display order.

[0128] The priority display unit can estimate the user's emotions and adjust the display method of the priority display results based on the estimated user emotions. For example, when the user is stressed, the priority display unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the priority display unit can also provide a display method including detailed information. For example, when the user is in a hurry, the priority display unit can also provide a display method that focuses on the main points. This enables more appropriate information to be provided by providing a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the priority display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the priority display unit can input the user's emotion data into the generation AI and have the generation AI execute emotion estimation.

[0129] During priority display, the priority display unit can select the optimal display method by referring to the user's past display history. The priority display unit, for example, prioritizes displaying instructions of the same category based on the user's past display history. The priority display unit can also prioritize displaying similar categories based on the user's past display history. For example, the priority display unit can also suggest the optimal display method based on the user's past display history. This improves the accuracy of the display by referring to the past display history. Some or all of the above-described processing in the priority display unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority display unit can input the user's past display history into a generation AI and cause the generation AI to select the optimal display method.

[0130] The priority display unit can adjust the display method during priority display, taking into account the screen size of the user's device. For example, if the user is using a smartphone, the priority display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the priority display unit can also provide a display method optimized for a large screen. For example, if the user is using a desktop, the priority display unit can also provide a display method that includes detailed information. This improves visibility by providing a display method that matches the device's screen size. Some or all of the above-described processing in the priority display unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority display unit can input screen size data of the user's device to the generation AI and cause the generation AI to select the optimal display method.

[0131] The text-to-speech unit can estimate the user's emotions and adjust the text-to-speech speed based on the estimated user's emotions. For example, if the user is stressed, the text-to-speech unit can read at a slower speed. Furthermore, if the user is relaxed, the text-to-speech unit can also read at a normal speed. For example, if the user is in a hurry, the text-to-speech unit can also read at a faster speed. This allows for more appropriate information provision by providing a reading speed that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the text-to-speech unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the text-to-speech unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0132] The voice reading unit can emphasize important parts of the instructions when reading aloud. For example, the voice reading unit can emphasize important parts of the instructions and read them slowly. The voice reading unit can also emphasize important parts of the instructions and read them at a higher volume. For example, the voice reading unit can emphasize important parts of the instructions and read them in a specific tone. This allows for quick access to necessary information by emphasizing important parts when reading them. Some or all of the above-described processing in the voice reading unit may be performed using AI, for example, or may be performed without using AI. For example, the voice reading unit can have a generating AI execute the emphasis on important parts of the instructions.

[0133] The voice reading unit can summarize and read out the contents of the instructions when reading out loud. For example, the voice reading unit can summarize the contents of the instructions and read them concisely. The voice reading unit can also summarize the contents of the instructions and read them out while emphasizing important points. For example, the voice reading unit can summarize the contents of the instructions and read them out in a way that is easy for the user to understand. This allows the necessary information to be accessed quickly by summarizing and reading out the contents. Some or all of the above-mentioned processing in the voice reading unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice reading unit can have a generation AI execute a summary of the instructions.

[0134] The text-to-speech unit can estimate the user's emotions and adjust the tone of the text-to-speech based on the estimated user's emotions. For example, if the user is stressed, the text-to-speech unit can read in a calm tone of voice. Furthermore, if the user is relaxed, the text-to-speech unit can read in a bright tone of voice. For example, if the user is in a hurry, the text-to-speech unit can read in a quick and concise tone of voice. This allows for more appropriate information provision by providing a tone of voice that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the text-to-speech unit may be performed using, for example, AI, or without AI. For example, the text-to-speech unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0135] The text-to-speech unit can automatically select a text-to-speech language based on the user's language setting when reading aloud. The text-to-speech unit automatically selects a text-to-speech language based on, for example, the language setting of the user's device. The text-to-speech unit can also provide a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the text-to-speech unit can read in that language. This makes it possible to provide more appropriate information by providing a text-to-speech language based on the language setting. Some or all of the above-described processing in the text-to-speech unit may be performed using, or without, AI, for example. For example, the text-to-speech unit can input the user's language setting data into a generation AI and have the generation AI select the optimal text-to-speech language.

[0136] When reading aloud, the text-to-speech unit can read at an optimal volume taking into account the volume setting of the user's device. The text-to-speech unit can read at an optimal volume based on, for example, the volume setting of the user's device. The text-to-speech unit can also automatically adjust the volume based on the volume setting of the user's device. For example, the text-to-speech unit can optimize the volume based on the volume setting of the user's device. This makes it possible to provide more appropriate information by providing an optimal volume according to the volume setting of the device. Some or all of the above-described processing in the text-to-speech unit may be performed using, or without, AI, for example. For example, the text-to-speech unit can input volume setting data of the user's device to the generation AI and cause the generation AI to adjust the volume to the optimal level. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, search unit, download unit, storage unit, organization unit, scan unit, update unit, priority display unit, and voice reading unit, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit takes a photo of the home appliance using the camera 42 of the smart device 14 and identifies the model number by running an image recognition algorithm using the processor 46. The search unit searches an online database using the identification processing unit 290 of the data processing device 12 to find the corresponding instruction manual. The download unit downloads the instruction manual using the identification processing unit 290 of the data processing device 12. The storage unit saves the downloaded instruction manual in the storage 32 of the data processing device 12. The organization unit organizes the saved instruction manuals by category using the identification processing unit 290 of the data processing device 12. The scan unit scans a two-dimensional code or barcode using the camera 42 of the smart device 14 to identify the model number. The update unit periodically checks an online database using the identification processing unit 290 of the data processing device 12 to download the latest instruction manual. The priority display unit analyzes the contents of the manual using the specific processing unit 290 of the data processing device 12 and displays important information preferentially. The voice reading unit reads the contents of the manual aloud using voice synthesis technology by the processor 46 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, search unit, download unit, storage unit, organization unit, scanning unit, update unit, priority display unit, and voice reading unit, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit takes a photo of the home appliance using the camera 42 of the smart glasses 214 and identifies the model number by running an image recognition algorithm using the processor 46. The search unit searches a database on the Internet using the identification processing unit 290 of the data processing device 12 to find the corresponding instruction manual. The download unit downloads the instruction manual using the identification processing unit 290 of the data processing device 12. The storage unit saves the downloaded instruction manual in the storage 32 of the data processing device 12. The organization unit saves the saved instruction manual by category using the identification processing unit 290 of the data processing device 12. The scanning unit scans a two-dimensional code or barcode using the camera 42 of the smart glasses 214 to identify the model number. The update unit periodically checks a database on the Internet using the identification processing unit 290 of the data processing device 12 to download the latest instruction manual. The priority display unit analyzes the contents of the manual using the specific processing unit 290 of the data processing device 12 and displays important information preferentially. The voice reading unit reads the contents of the manual aloud using voice synthesis technology by the processor 46 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned analysis unit, search unit, download unit, storage unit, organization unit, scan unit, update unit, priority display unit, and voice reading unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit takes a photo of the home appliance using the camera 42 of the headset-type terminal 314 and identifies the model number by running an image recognition algorithm using the processor 46. The search unit searches a database on the Internet using the identification processing unit 290 of the data processing device 12 to find the corresponding instruction manual. The download unit downloads the instruction manual using the identification processing unit 290 of the data processing device 12. The storage unit saves the downloaded instruction manual in the storage 32 of the data processing device 12. The organization unit saves the saved instruction manual by category using the identification processing unit 290 of the data processing device 12. The scan unit scans a two-dimensional code or a barcode using the camera 42 of the headset-type terminal 314 to identify the model number. The update unit periodically checks a database on the Internet using the identification processing unit 290 of the data processing device 12 to download the latest instruction manual. The priority display unit analyzes the contents of the manual using the specific processing unit 290 of the data processing device 12 and displays important information preferentially. The voice reading unit reads the contents of the manual aloud using voice synthesis technology by the processor 46 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned analysis unit, search unit, download unit, storage unit, organization unit, scanning unit, update unit, priority display unit, and voice reading unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit takes a photo of the home appliance using the camera 42 of the robot 414 and identifies the model number by running an image recognition algorithm using the processor 46. The search unit searches a database on the Internet using the identification processing unit 290 of the data processing device 12 to find the corresponding instruction manual. The download unit downloads the instruction manual using the identification processing unit 290 of the data processing device 12. The storage unit saves the downloaded instruction manual in the storage 32 of the data processing device 12. The organization unit saves the saved instruction manual by category using the identification processing unit 290 of the data processing device 12. The scanning unit scans a two-dimensional code or barcode using the camera 42 of the robot 414 to identify the model number. The update unit periodically checks a database on the Internet using the identification processing unit 290 of the data processing device 12 to download the latest instruction manual. The priority display unit analyzes the contents of the manual using the specific processing unit 290 of the data processing device 12 and displays important information preferentially. The voice reading unit reads the contents of the manual aloud using voice synthesis technology by the processor 46 of the robot 414.

[0137] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0138] The instruction manual app may also include a voice recognition unit that recognizes user voice commands. For example, when a user issues a voice command such as "Show me the refrigerator manual," the voice recognition unit analyzes the command and automatically displays the corresponding manual. Furthermore, when a user asks a question about a specific function or setting, the voice recognition unit can extract and provide an answer to the question from the manual. For example, when a user asks, "How do I set the eco mode on my washing machine?", the voice recognition unit analyzes the question and displays the relevant part of the manual. This allows a user to access the manual without using their hands by using voice commands.

[0139] The instruction app can estimate the user's emotions and adjust the tone of the audio guide based on the estimated user emotions. For example, if the user is feeling stressed, the audio guide can explain in a calm tone. On the other hand, if the user is relaxed, the audio guide can explain in a bright tone. For example, if the user is in a hurry, the audio guide can explain in a quick and concise tone. This allows the audio guide to be provided in accordance with the user's emotions, making it possible to provide more appropriate information.

[0140] The instruction manual app can provide region-specific information based on the user's location information. For example, if the user is in a specific region, it will prioritize displaying instructions for home appliances sold in that region. Also, if the user is traveling, it can provide instructions for home appliances used in the region to which the user is traveling. For example, when a user is traveling abroad and uses local home appliances, it can provide instructions written in the local language. This makes it possible to provide information based on the user's location information.

[0141] The instruction application can estimate the user's emotions and display a summary of the instruction contents based on the estimated user's emotions. For example, if the user is feeling stressed, the application can display a concise summary of the instruction contents. Alternatively, if the user is relaxed, the application can display an instruction including detailed information. For example, if the user is in a hurry, the application can display a summary that focuses on the main points. This makes it possible to provide information according to the user's emotions.

[0142] The instruction app can suggest the optimal shooting method taking into account the camera performance of the user's device. For example, if the user's device camera is high-performance, it can suggest taking detailed photos. Also, if the user's device camera is low-performance, it can suggest taking photos from an appropriate distance or angle. For example, if the user is taking photos in a dark place, it can suggest using a flash. This improves the accuracy of analysis by providing the optimal shooting method according to the device's camera performance.

[0143] The instruction manual app can estimate the user's emotions and filter search results for instructions based on the estimated user's emotions. For example, if the user is feeling stressed, it can display only the most relevant results. On the other hand, if the user is relaxed, it can display results with more detailed information. For example, if the user is in a hurry, it can prioritize the display of quickly accessible results. This makes it possible to filter search results according to the user's emotions.

[0144] The instruction manual app can provide optimal search results by referring to the user's past search history. For example, it can prioritize displaying instructions for home appliances that the user has previously searched for. It can also display related instructions based on keywords that the user has previously searched for. For example, it can display related instructions based on the series of home appliances that the user has previously searched for. In this way, by referring to the user's past search history, the accuracy of search results can be improved.

[0145] The instruction application can estimate the user's emotions and adjust the way the instructions are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. Alternatively, if the user is relaxed, a display method including detailed information can be provided. For example, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide more appropriate information by providing a display method that corresponds to the user's emotions.

[0146] The instruction manual app can suggest the optimal storage method taking into account the storage situation of the user's device. For example, if the storage of the user's device is low, it can suggest saving to cloud storage. Also, if the storage of the user's device is sufficient, it can suggest saving to local storage. For example, it can suggest saving frequently accessed instructions to the user in local storage and saving less frequently used instructions to cloud storage. This allows for efficient use of storage by providing the optimal storage method according to the storage situation of the device.

[0147] The instruction manual app can estimate the user's emotions and adjust the update frequency of the instructions based on the estimated user's emotions. For example, if the user is feeling stressed, the update frequency can be increased to provide the latest information quickly. Alternatively, if the user is relaxed, the update frequency can be kept normal and detailed information can be provided. For example, if the user is in a hurry, the update frequency can be increased to provide the latest information quickly. This makes it possible to provide more appropriate information by providing an update frequency that corresponds to the user's emotions.

[0148] The processing flow of the second embodiment will be briefly explained below.

[0149] Step 1: The analysis unit analyzes a photo of the home appliance to identify the model number. The analysis unit uses an image recognition algorithm or machine learning model to extract the features of the home appliance and identify the model number. For example, it receives a photo of the home appliance as input and uses an image recognition algorithm to identify the model number. Step 2: The search unit searches for the instruction manual based on the model number identified by the analysis unit. The search unit finds the relevant instruction manual using an online database or search engine. For example, it receives the identified model number as input and searches an online database to find the instruction manual. Step 3: The download unit downloads the manuals found by the search unit. The download unit can specify the file format of the manuals to download and adjust the download speed. For example, the download unit downloads the manuals found by the search unit in the specified file format. Step 4: The storage unit stores the instruction manual downloaded by the download unit. The storage unit can store the instruction manual in a specific folder or in cloud storage. For example, the storage unit stores the downloaded instruction manual in a specific folder. Step 5: The organizing unit organizes the instructions stored by the storage unit by category. The organizing unit can organize the instructions by type of home appliance or by manufacturer. For example, the stored instructions are organized by type of home appliance.

[0150] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0152] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0155] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0156] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0164] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0168] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0171] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0172] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0184] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0187] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0188] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0189] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0190] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0192] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0193] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0194] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0195] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0196] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0197] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0198] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0199] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0200] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0201] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0202] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0203] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0204] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0205] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0206] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0207] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0208] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0209] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0210] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0211] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0212] 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.

[0213] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0214] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0215] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0216] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0217] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0218] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0219] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0220] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0221] [Explanation of symbols]

[0222] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis unit that analyzes photos of home appliances to identify model numbers; a search unit that searches for an instruction manual based on the model number identified by the analysis unit; a download unit that downloads the manual searched by the search unit; a storage unit for storing the instruction manual downloaded by the download unit; An organizing unit that organizes the instructions stored by the storage unit by category. A system characterized by:

2. Equipped with a scanning unit that scans two-dimensional codes or barcodes 2. The system of claim 1.

3. Equipped with an update unit that automatically updates the manual 2. The system of claim 1.

4. Equipped with a priority display section that analyzes the contents of the instruction manual and displays them preferentially 2. The system of claim 1.

5. Equipped with a voice reading section that reads out the contents of the manual 2. The system of claim 1.

6. The analysis unit Estimate user emotions and optimize analysis accuracy based on the estimated user emotions.

2. The system of claim 1.

7. The analysis unit When analyzing photos of home appliances, removing background information improves analysis accuracy.

2. The system of claim 1.

8. The analysis unit Taking photos of home appliances from multiple angles and integrating the analysis results to identify the model number 2. The system of claim 1.

9. The analysis unit Inferring user emotions and adjusting the display of analysis results based on the estimated user emotions 2. The system of claim 1.

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