System

Abstract

To provide a system that efficiently creates an operation manual of a smartphone.SOLUTION: A system according to the embodiment comprises a recognition unit, an acquisition unit, an analysis unit, a generation unit and a creation unit. The recognition unit recognizes a screen of a smartphone, and performs specific operations. The acquisition unit acquires necessary screen captures based on the screen recognized by the recognition unit. The analysis unit analyzes the screen captures acquired by the acquisition unit, and extracts information on the screen. The generation unit generates functions or operating instructions based on the information extracted by the analysis unit. The creation unit creates a manual by combining the functions or operating instructions generated by the generation unit.SELECTED DRAWING: Figure 1

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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently create smartphone operation manuals.

[0005] The system according to the embodiment aims to efficiently create smartphone operation manuals. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, an acquisition unit, an analysis unit, a generation unit, and a creation unit. The recognition unit recognizes the screen of the smartphone and performs a specific operation. The acquisition unit acquires a necessary screen capture based on the screen recognized by the recognition unit. The analysis unit analyzes the screen capture acquired by the acquisition unit and extracts information on the screen. The generation unit generates functions or operation instructions based on the information extracted by the analysis unit. The creation unit creates a manual by combining the functions or operation instructions generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently create smartphone operation manuals. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system in which a smart robot automatically operates a smartphone and acquires necessary screen captures. This system is a mechanism in which a generation AI automatically generates functions and operation instructions based on the acquired screen captures, and automatically creates a manual. For example, the smart robot touches the screen of the smartphone, launches a necessary application, and performs a specific operation. For example, the setting screen is opened and a specific setting item is selected. At this time, the smart robot uses a camera or a sensor to recognize the smartphone screen and accurately operates it. Next, the smart robot captures the smartphone screen at the time the specific operation is completed and saves it as image data. For example, a screen capture is acquired with a specific item of the setting screen displayed. The acquired screen capture is sent to the generation AI. The generation AI analyzes the screen capture and extracts information on the screen. For example, the name and description of the setting item are extracted. Next, the generation AI automatically generates functions and operation instructions. Based on the extracted information, the generation AI generates functions and operation instructions in a form that is easy for the user to understand. For example, it generates an explanation such as "If you select XX item on the setting screen, you can change the setting of △△." Finally, the generation AI automatically creates the manual. The generation AI creates the manual by combining the generated functions and operation instructions. For example, it creates a setting manual by combining multiple screen captures and their corresponding explanations. With this mechanism, it is possible to automatically create smartphone operation manuals using a smart robot and generation AI. This allows users to easily create manuals without having to perform complex operations. This allows the system to automate smartphone operations and automatically create manuals.

[0029] The system according to the embodiment includes a recognition unit, an acquisition unit, an analysis unit, a generation unit, and a creation unit. The recognition unit recognizes the screen of the smartphone and performs a specific operation. The recognition unit recognizes the screen of the smartphone using, for example, a camera and a touch sensor, and detects a specific icon or button. The recognition unit, for example, takes a picture of the screen of the smartphone with a camera and detects a specific icon or button using an image recognition technique. The recognition unit can also detect a touch operation of a user using a touch sensor and perform a specific operation. The acquisition unit acquires a necessary screen capture based on the screen recognized by the recognition unit. The acquisition unit, for example, captures the screen of the smartphone when a specific operation is completed and saves it as image data. The acquisition unit, for example, acquires the screen of the smartphone as a screenshot and saves it as image data. The acquisition unit can also acquire multiple screen captures consecutively when a specific operation is completed. The analysis unit analyzes the screen capture acquired by the acquisition unit and extracts information on the screen. The analysis unit, for example, uses image analysis technique to extract text information from the screen capture. The analysis unit extracts text information from the screen capture, for example, using OCR (optical character recognition) technology. The analysis unit can also extract information on specific icons and buttons using image analysis technology. The generation unit generates functions and operation instructions based on the information extracted by the analysis unit. The generation unit analyzes the extracted text information, for example, using natural language processing technology, and generates an explanation in a form that is easy for the user to understand. The generation unit generates an explanation based on the extracted text information, for example, using a text generation AI (for example, LLM). The generation unit can also generate explanation content including diagrams and videos based on the extracted information. The creation unit creates a manual by combining the functions and operation instructions generated by the generation unit. The creation unit creates a print manual and a digital manual simultaneously, for example, based on the generated explanation. The creation unit can also create an interactive manual, for example, based on the generated explanation. As a result, the system according to the embodiment can automate the operation of a smartphone and automatically create a manual.Some or all of the above-mentioned processes in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can create a manual using an AI model that inputs the generated explanatory text and outputs a manual.

[0030] The recognition unit recognizes the smartphone screen and performs a specific operation. The recognition unit recognizes the smartphone screen using, for example, a camera and a touch sensor, and detects specific icons and buttons. Specifically, the entire screen is photographed using the smartphone camera, and the image data is processed in real time. As an image recognition technology, a deep learning model such as a convolutional neural network (CNN) can be used. This makes it possible to detect the position and shape of icons and buttons on the screen with high accuracy. In addition, by using a touch sensor, it is possible to detect a user's touch operation and perform a specific operation. There are capacitive and infrared touch sensors, and they can detect the position and movement of the user's finger with high accuracy. This allows the recognition unit to accurately grasp the operation performed by the user on the screen and perform the necessary processing. Furthermore, the recognition unit can learn the user's operation history and predict the next and subsequent operations. For example, the efficiency of operations can be improved by predicting the next operation performed by the user based on past operation patterns and preparing in advance. This allows the recognition unit to recognize the smartphone screen with high accuracy and support the user's operation.

[0031] The acquisition unit acquires a necessary screen capture based on the screen recognized by the recognition unit. For example, the acquisition unit captures the screen of the smartphone when a specific operation is completed and saves it as image data. Specifically, the screen contents are saved as image data by utilizing a screenshot function of the smartphone. The acquisition unit can detect the timing when a specific operation is completed and capture the screen at that moment. For example, a screenshot is acquired at the moment when a user presses a specific button or at the moment when a specific screen is displayed. The acquisition unit can also acquire multiple screen captures in succession. This allows the flow of operations and changes in the screen to be recorded in detail. Furthermore, the acquisition unit can save the captured image data in cloud storage and share it with other devices and systems. This allows the acquisition unit to efficiently acquire, save, and share screen captures of the smartphone.

[0032] The analysis unit analyzes the screen capture acquired by the acquisition unit and extracts information on the screen. The analysis unit extracts text information from the screen capture using, for example, image analysis technology. Specifically, the analysis unit analyzes the text included in the screen capture using OCR (optical character recognition) technology and extracts it as digital data. OCR technology analyzes the shape and pattern of characters and converts them into corresponding character codes, and is capable of highly accurate character recognition. The analysis unit can also extract information on specific icons and buttons using image analysis technology. For example, the analysis unit uses an image recognition algorithm to identify the position and shape of icons and buttons on the screen and extracts the information as digital data. Furthermore, the analysis unit can analyze the layout and structure on the screen based on the extracted information and infer the user's operation intention. This allows the analysis unit to analyze the acquired screen capture in detail and accurately extract necessary information.

[0033] The generation unit generates function and operation instructions based on the information extracted by the analysis unit. The generation unit, for example, uses natural language processing technology to analyze the extracted text information and generate an explanation in a form that is easy for the user to understand. Specifically, the generation unit uses a text generation AI (for example, LLM) to generate an explanation based on the extracted text information. The LLM is a model that has learned a large amount of text data and can generate natural sentences. The generation unit inputs the extracted information as a prompt to the LLM and generates an appropriate explanation. For example, a specific operation explanation such as "When you press this button, a setting screen will be displayed" can be generated. The generation unit can also generate explanation content including diagrams and videos based on the extracted information. For example, it generates diagrams showing operation procedures and videos showing actual operations and provides them to the user. This allows the generation unit to provide function and operation instructions in a form that is easy for the user to understand.

[0034] The creation unit creates a manual by combining the functions and operation instructions generated by the generation unit. For example, the creation unit simultaneously creates a print manual and a digital manual based on the generated explanatory text. Specifically, the creation unit creates a manual in a form that is easy for users to use by combining the generated explanatory text, illustrations, and videos. The print manual is provided in paper form, and the digital manual is provided in PDF or HTML format. The creation unit can also create an interactive manual. For example, an interactive manual can be created in which, when a user performs a specific operation, an explanation related to the operation is displayed. Furthermore, some or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can create a manual using an AI model that takes the generated explanatory text as input and outputs a manual. This allows the creation unit to efficiently and highly accurately create a manual and provide it to the user.

[0035] The recognition unit can recognize the screen of the smartphone using a camera or a touch sensor and detect a specific icon or button. The recognition unit, for example, takes a picture of the screen of the smartphone with a camera and detects a specific icon or button using image recognition technology. For example, the recognition unit takes a picture of an icon or button on the screen using the front or rear camera of the smartphone and detects them using image recognition technology. The recognition unit can also detect a touch operation of a user using a touch sensor and perform a specific operation. For example, the recognition unit detects a touch operation of a user using a capacitive touch sensor and recognizes a specific icon or button. This allows the recognition unit to accurately recognize the screen of the smartphone and perform a specific operation. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input image data taken by a camera to the generation AI and cause the generation AI to detect a specific icon or button.

[0036] The acquisition unit can capture the screen of the smartphone when the operation is completed and save it as image data. For example, the acquisition unit captures the screen of the smartphone when a specific operation is completed and saves it as image data. For example, the acquisition unit acquires the screen of the smartphone as a screenshot and saves it as image data in JPEG format or PNG format. The acquisition unit can also acquire multiple screen captures continuously when a specific operation is completed. For example, the acquisition unit acquires screen captures continuously at regular intervals from the moment a specific event occurs and records the flow of the operation. This allows the acquisition unit to accurately acquire and save the necessary screen captures. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the screen capture of the smartphone to the generation AI and cause the generation AI to execute the saving of the image data.

[0037] The analysis unit can perform image analysis of the screen capture and extract specific text information. The analysis unit extracts text information from the screen capture using, for example, image analysis technology. For example, the analysis unit extracts text information from the screen capture using OCR (optical character recognition) technology. The analysis unit can also extract information of specific icons and buttons using image analysis technology. For example, the analysis unit preferentially analyzes specific colors and shapes in the screen capture and extracts important information. This allows the analysis unit to extract necessary information from the screen capture. A part 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 image data of the screen capture to the generation AI and cause the generation AI to extract text information.

[0038] The generation unit can analyze the extracted text information using a natural language processing technique and generate an explanatory sentence in a form that is easy for the user to understand. The generation unit can analyze the extracted text information using, for example, a natural language processing technique and generate an explanatory sentence in a form that is easy for the user to understand. For example, the generation unit can generate an explanatory sentence based on the extracted text information using a text generation AI (for example, LLM). The generation unit can also generate explanatory content including diagrams and videos based on the extracted information. For example, the generation unit generates explanatory content including diagrams based on the extracted text information. This allows the generation unit to generate function and operation explanations in a form that is easy for the user to understand. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the extracted text information to the generation AI and cause the generation AI to execute generation of the explanatory sentence.

[0039] The creation unit can create a manual by combining the generated functions or operation instructions. The creation unit, for example, simultaneously creates a print manual and a digital manual based on the generated explanatory text. For example, the creation unit can also create an interactive manual based on the generated explanatory text. This allows the creation unit to automatically create a manual. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can create a manual using an AI model that inputs the generated explanatory text and outputs a manual.

[0040] The recognition unit can automatically adjust the brightness and contrast of the smartphone screen to improve the recognition accuracy. For example, the recognition unit can automatically adjust the brightness of the smartphone screen to minimize the influence of external light. The recognition unit can also automatically adjust the contrast of the smartphone screen to improve visibility. Furthermore, the recognition unit can automatically adjust the color temperature of the smartphone screen to optimize the recognition accuracy. As a result, the recognition unit can improve the recognition accuracy by adjusting the brightness and contrast of the screen. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can cause the generation AI to adjust the brightness and contrast of the smartphone screen.

[0041] The recognition unit can refer to the usage history of the smartphone screen and predict and recognize a specific operation pattern. The recognition unit, for example, predicts and recognizes frequently used applications based on the past usage history. The recognition unit can also predict and recognize the next operation based on the past operation pattern. Furthermore, the recognition unit can predict and recognize an operation to be performed in a specific time period based on the past usage history. As a result, the recognition unit can improve the recognition accuracy by predicting the operation pattern based on the usage history. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the usage history data to the generation AI and cause the generation AI to execute the prediction of the operation pattern.

[0042] The recognition unit can preferentially recognize region-specific icons and buttons in consideration of the geographical location information of the smartphone screen. For example, the recognition unit preferentially recognizes region-specific application icons based on the user's current location. The recognition unit can also preferentially recognize region-specific setting items based on the user's current location. Furthermore, the recognition unit can also preferentially recognize region-specific service icons based on the user's current location. As a result, the recognition unit can preferentially recognize region-specific icons and buttons by considering the geographical location information. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input geographical location information data to the generation AI and cause the generation AI to recognize region-specific icons and buttons.

[0043] The recognition unit can preferentially recognize a specific icon or button based on the age or gender of the user of the smartphone screen. For example, the recognition unit preferentially recognizes an icon of an application suitable for the age based on the user's age. The recognition unit can also preferentially recognize a setting item suitable for the gender based on the user's gender. Furthermore, the recognition unit can also preferentially recognize an icon of a specific service based on the user's age or gender. This allows the recognition unit to preferentially recognize a specific icon or button based on the user's age or gender. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input the user's age and gender data to the generation AI and cause the generation AI to execute the determination of the priority order of icons and buttons.

[0044] The acquisition unit can continuously acquire multiple screen captures at the time when a specific operation on the smartphone screen is completed. For example, the acquisition unit continuously acquires screen captures before and after a setting change is completed. In addition, the acquisition unit can continuously acquire screen captures before and after the installation at the time when the installation of an application is completed. Furthermore, the acquisition unit can continuously acquire multiple screen captures showing the flow of the operation at the time when the specific operation is completed. Thereby, the acquisition unit can accurately record the flow of the operation by continuously acquiring screen captures at the time when the specific operation is completed. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can cause the generation AI to execute the continuous acquisition of screen captures.

[0045] The acquisition unit can capture only a specific area of ​​the smartphone screen and efficiently acquire the necessary information. The acquisition unit, for example, captures only an area where a specific item of a setting screen is displayed. The acquisition unit can also capture only an area where a specific function of an application is displayed. Furthermore, the acquisition unit can capture only an area where a specific operation is performed and efficiently acquire the necessary information. As a result, the acquisition unit can efficiently acquire the necessary information by capturing only the specific area. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can cause the generation AI to execute the capture of the specific area.

[0046] The acquisition unit can preferentially acquire highly relevant screen captures by taking into account the geographical location information of the smartphone screen. For example, the acquisition unit preferentially acquires screen captures of region-specific applications based on the user's current location. The acquisition unit can also preferentially acquire screen captures of region-specific setting items based on the user's current location. Furthermore, the acquisition unit can also preferentially acquire screen captures of region-specific services based on the user's current location. Thereby, the acquisition unit can preferentially acquire highly relevant screen captures by taking into account the geographical location information. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input geographical location information data to the generation AI and cause the generation AI to acquire highly relevant screen captures.

[0047] The acquisition unit can analyze the social media activity of the user of the smartphone screen and acquire related screen captures. The acquisition unit can acquire screen captures of related applications based on the user's social media activity, for example. The acquisition unit can also acquire screen captures of related setting items based on the user's social media activity. Furthermore, the acquisition unit can acquire screen captures of related services based on the user's social media activity. This allows the acquisition unit to efficiently acquire related screen captures by analyzing social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input social media activity data to the generation AI and cause the generation AI to acquire related screen captures.

[0048] The analysis unit can give priority to analyzing a specific color or shape when analyzing the image of the screen capture. For example, the analysis unit can give priority to analyzing a specific color in the screen capture to extract important information. The analysis unit can also give priority to analyzing a specific shape in the screen capture to extract important information. Furthermore, the analysis unit can also give priority to analyzing a specific icon or button in the screen capture to extract important information. This allows the analysis unit to efficiently extract important information by giving priority to analyzing a specific color or shape. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input image data of the screen capture to the generation AI and cause the generation AI to perform analysis of a specific color or shape.

[0049] The analysis unit can improve the analysis accuracy by referring to past analysis results when analyzing the image of the screen capture. The analysis unit can improve the analysis accuracy of a similar screen capture, for example, based on past analysis results. The analysis unit can also recognize a specific pattern based on past analysis results and improve the analysis accuracy. Furthermore, the analysis unit can prevent a specific error based on past analysis results and improve the analysis accuracy. Thereby, the analysis unit can improve the analysis accuracy by referring to past analysis results. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0050] The analysis unit can preferentially analyze region-specific information in consideration of the geographical location information of the screen capture. The analysis unit, for example, preferentially analyzes region-specific setting items based on the geographical location information of the screen capture. The analysis unit can also preferentially analyze information of region-specific applications based on the geographical location information of the screen capture. Furthermore, the analysis unit can also preferentially analyze information of region-specific services based on the geographical location information of the screen capture. Thereby, the analysis unit can preferentially analyze region-specific information by considering the geographical location information. A part 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 geographical location information data to the generation AI and cause the generation AI to execute analysis of the region-specific information.

[0051] The analysis unit can preferentially analyze specific information based on the age and gender of the user of the screen capture. For example, the analysis unit preferentially analyzes information suitable for the age based on the age of the user. The analysis unit can also preferentially analyze information suitable for the gender based on the gender of the user. Furthermore, the analysis unit can also preferentially analyze information of a specific service based on the age and gender of the user. This allows the analysis unit to preferentially analyze specific information based on the age and gender of the user. A part 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 age and gender data of the user to the generation AI and cause the generation AI to determine the priority order of information.

[0052] The generation unit can generate a plurality of different explanatory sentences based on the extracted text information and provide the user with options. The generation unit can generate two types of explanatory sentences, a concise explanatory sentence and a detailed explanatory sentence, based on the extracted text information, for example. The generation unit can also generate explanatory sentences using different expression methods based on the extracted text information. Furthermore, the generation unit can generate explanatory sentences from different perspectives based on the extracted text information and provide options to the user. As a result, the generation unit can provide options to the user by generating a plurality of different explanatory sentences. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the extracted text information to a generation AI and cause the generation AI to generate a plurality of different explanatory sentences.

[0053] The generating unit can generate explanatory content including diagrams and videos based on the extracted text information. The generating unit generates explanatory content including diagrams, for example, based on the extracted text information. The generating unit can also generate explanatory content including videos based on the extracted text information. Furthermore, the generating unit can generate explanatory content combining diagrams and videos based on the extracted text information. In this way, the generating unit can provide an explanation that is visually easy to understand by generating explanatory content including diagrams and videos. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the extracted text information to a generating AI and cause the generating AI to generate diagrams and videos.

[0054] The generation unit can generate explanatory text in different languages ​​based on the extracted text information. The generation unit can generate explanatory text in English, for example, based on the extracted text information. The generation unit can also generate explanatory text in Chinese based on the extracted text information. Furthermore, the generation unit can generate explanatory text in Spanish based on the extracted text information. This allows the generation unit to provide multilingual explanations by generating explanatory text in different languages. A part or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the extracted text information to a generation AI and cause the generation AI to generate explanatory text in different languages.

[0055] The generation unit can generate a customized explanatory text for a specific user group based on the extracted text information. The generation unit generates, for example, an explanatory text for beginners based on the extracted text information. The generation unit can also generate an explanatory text for advanced users based on the extracted text information. Furthermore, the generation unit can generate an explanatory text specialized for a specific industry or use based on the extracted text information. As a result, the generation unit can provide an optimal explanation for the user by generating an explanatory text customized for a specific user group. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the extracted text information to the generation AI and cause the generation AI to generate an explanatory text for a specific user group.

[0056] The creation unit can create an interactive manual based on the generated explanatory text. For example, the creation unit creates an interactive manual that allows the user to try operations based on the generated explanatory text. The creation unit can also create an interactive manual in a format in which the user answers questions based on the generated explanatory text. Furthermore, the creation unit can also create an interactive manual in a format in which the user can select options based on the generated explanatory text. In this way, the creation unit can provide a manual in a format in which the user can try operations by creating an interactive manual. Part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the generated explanatory text to a generation AI and cause the generation AI to create an interactive manual.

[0057] The creation unit can simultaneously create a print manual and a digital manual based on the generated explanatory text. The creation unit, for example, creates a print manual based on the generated explanatory text. The creation unit can also create a digital manual based on the generated explanatory text. Furthermore, the creation unit can simultaneously create both print and digital manuals based on the generated explanatory text. As a result, the creation unit can provide a manual that meets the needs of the user by simultaneously creating both print and digital manuals. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the generated explanatory text to a generation AI and cause the generation AI to execute the creation of print and digital manuals.

[0058] The creation unit can create a manual optimized for a specific device based on the generated explanatory text. The creation unit can create a manual optimized for a smartphone based on the generated explanatory text, for example. The creation unit can also create a manual optimized for a tablet based on the generated explanatory text. Furthermore, the creation unit can create a manual optimized for a personal computer based on the generated explanatory text. As a result, the creation unit can provide a manual that is easy for users to use by creating a manual optimized for a specific device. A part or all of the above-mentioned processing in the creation unit can be performed using, for example, AI, or can be performed without using AI. For example, the creation unit can input the generated explanatory text to a generation AI and cause the generation AI to create a manual optimized for a specific device.

[0059] The creation unit can create a manual specialized for a specific industry or use based on the generated explanatory text. For example, the creation unit creates a manual for the medical industry based on the generated explanatory text. The creation unit can also create a manual for the education industry based on the generated explanatory text. Furthermore, the creation unit can create a manual for the manufacturing industry based on the generated explanatory text. As a result, the creation unit can provide an optimal manual for the user by creating a manual specialized for a specific industry or use. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the generated explanatory text to a generation AI and cause the generation AI to create a manual specialized for a specific industry or use.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The recognition unit can recognize a user's voice command and perform a specific operation based on the voice command. For example, when a user gives a voice command such as "Open the settings screen," the recognition unit analyzes this command using voice recognition technology and automatically opens the smartphone's settings screen. When a user gives a command such as "Turn on Wi-Fi," the recognition unit can automatically turn on the Wi-Fi setting. Furthermore, when a user gives a command such as "Install an app," the recognition unit can automatically install the specified application. This allows the recognition unit to operate the smartphone more intuitively using voice commands.

[0062] When analyzing a screen capture image, the analysis unit can improve the analysis accuracy by referring to the operation history of a specific user. For example, setting items that the user has frequently used in the past are analyzed preferentially to extract important information. In addition, it is also possible to recognize specific patterns based on the past operation history and improve the analysis accuracy. Furthermore, it is also possible to prevent specific errors and improve the analysis accuracy based on the past operation history. In this way, the analysis unit can improve the analysis accuracy by referring to the operation history of the user.

[0063] The creation unit can create a manual including a simulation function that allows the user to try out operations based on the generated explanatory text. For example, a simulation can be provided that allows the user to try out operations on a setting screen, allowing the user to practice before performing actual operations. It is also possible to allow the user to experience the installation procedure of an application through a simulation. Furthermore, it is also possible to allow the user to check the effects of specific setting changes through a simulation. In this way, by creating a manual that includes a simulation function, the creation unit can provide a manual in a format that allows the user to try out operations.

[0064] The acquisition unit can capture only a specific area of ​​the smartphone screen and efficiently acquire the required information. For example, only an area where a specific item of a setting screen is displayed can be captured. Also, only an area where a specific function of an application is displayed can be captured. Furthermore, only an area where a specific operation is being performed can be captured and the required information can be efficiently acquired. In this way, the acquisition unit can efficiently acquire the required information by capturing only the specific area.

[0065] The generation unit can generate explanatory text in different languages ​​based on the extracted text information. For example, an explanatory text can be generated in English based on the extracted text information. Also, an explanatory text can be generated in Chinese based on the extracted text information. Furthermore, an explanatory text can be generated in Spanish based on the extracted text information. In this way, the generation unit can provide an explanatory text in multiple languages ​​by generating explanatory text in different languages.

[0066] The creation unit can create a manual optimized for a specific device based on the generated explanatory text. For example, a manual optimized for a smartphone can be created based on the generated explanatory text. Also, a manual optimized for a tablet can be created based on the generated explanatory text. Furthermore, a manual optimized for a PC can be created based on the generated explanatory text. In this way, the creation unit can provide a manual that is easy for the user to use by creating a manual optimized for a specific device.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The recognition unit recognizes the smartphone screen and performs a specific operation. The recognition unit uses a camera and touch sensor to recognize the smartphone screen and detects specific icons and buttons. For example, the smartphone screen is photographed with a camera and specific icons and buttons are detected using image recognition technology. It is also possible to detect the user's touch operation using a touch sensor and perform a specific operation. Step 2: The acquisition unit acquires the necessary screen capture based on the screen recognized by the recognition unit. The acquisition unit captures the smartphone screen when the specific operation is completed and saves it as image data. For example, the smartphone screen is acquired as a screenshot and saved as image data. It is also possible to acquire multiple screen captures consecutively when the specific operation is completed. Step 3: The analysis unit analyzes the screen capture acquired by the acquisition unit and extracts information on the screen. The analysis unit extracts text information from the screen capture using image analysis technology. For example, the analysis unit extracts text information from the screen capture using OCR (Optical Character Recognition) technology. The analysis unit can also extract information on specific icons and buttons using image analysis technology. Step 4: The generation unit generates function and operation explanations based on the information extracted by the analysis unit. The generation unit analyzes the extracted text information using natural language processing technology and generates explanatory text in a form that is easy for the user to understand. For example, the generation unit generates explanatory text based on the extracted text information using a text generation AI (e.g., LLM). It is also possible to generate explanatory content including diagrams and videos based on the extracted information. Step 5: The creation unit creates a manual by combining the functions and operation instructions generated by the generation unit. The creation unit simultaneously creates a print manual and a digital manual based on the generated explanatory text. For example, an interactive manual can be created based on the generated explanatory text. Some or all of the processing in the creation unit may be performed using AI, or may be performed without using AI. For example, a manual can be created using an AI model that takes the generated explanatory text as input and outputs a manual.

[0069] (Example 2) The system according to the embodiment of the present invention is a system in which a smart robot automatically operates a smartphone and acquires necessary screen captures. This system is a mechanism in which a generation AI automatically generates functions and operation instructions based on the acquired screen captures, and automatically creates a manual. For example, the smart robot touches the screen of the smartphone, launches a necessary application, and performs a specific operation. For example, the setting screen is opened and a specific setting item is selected. At this time, the smart robot uses a camera or a sensor to recognize the smartphone screen and accurately operates it. Next, the smart robot captures the smartphone screen at the time the specific operation is completed and saves it as image data. For example, a screen capture is acquired with a specific item of the setting screen displayed. The acquired screen capture is sent to the generation AI. The generation AI analyzes the screen capture and extracts information on the screen. For example, the name and description of the setting item are extracted. Next, the generation AI automatically generates functions and operation instructions. Based on the extracted information, the generation AI generates functions and operation instructions in a form that is easy for the user to understand. For example, it generates an explanation such as "If you select XX item on the setting screen, you can change the setting of △△." Finally, the generation AI automatically creates the manual. The generation AI creates the manual by combining the generated functions and operation instructions. For example, it creates a setting manual by combining multiple screen captures and their corresponding explanations. With this mechanism, it is possible to automatically create smartphone operation manuals using a smart robot and generation AI. This allows users to easily create manuals without having to perform complex operations. This allows the system to automate smartphone operations and automatically create manuals.

[0070] The system according to the embodiment includes a recognition unit, an acquisition unit, an analysis unit, a generation unit, and a creation unit. The recognition unit recognizes the screen of the smartphone and performs a specific operation. The recognition unit recognizes the screen of the smartphone using, for example, a camera and a touch sensor, and detects a specific icon or button. The recognition unit, for example, takes a picture of the screen of the smartphone with a camera and detects a specific icon or button using an image recognition technique. The recognition unit can also detect a touch operation of a user using a touch sensor and perform a specific operation. The acquisition unit acquires a necessary screen capture based on the screen recognized by the recognition unit. The acquisition unit, for example, captures the screen of the smartphone when a specific operation is completed and saves it as image data. The acquisition unit, for example, acquires the screen of the smartphone as a screenshot and saves it as image data. The acquisition unit can also acquire multiple screen captures consecutively when a specific operation is completed. The analysis unit analyzes the screen capture acquired by the acquisition unit and extracts information on the screen. The analysis unit, for example, uses image analysis technique to extract text information from the screen capture. The analysis unit extracts text information from the screen capture, for example, using OCR (optical character recognition) technology. The analysis unit can also extract information on specific icons and buttons using image analysis technology. The generation unit generates functions and operation instructions based on the information extracted by the analysis unit. The generation unit analyzes the extracted text information, for example, using natural language processing technology, and generates an explanation in a form that is easy for the user to understand. The generation unit generates an explanation based on the extracted text information, for example, using a text generation AI (for example, LLM). The generation unit can also generate explanation content including diagrams and videos based on the extracted information. The creation unit creates a manual by combining the functions and operation instructions generated by the generation unit. The creation unit creates a print manual and a digital manual simultaneously, for example, based on the generated explanation. The creation unit can also create an interactive manual, for example, based on the generated explanation. As a result, the system according to the embodiment can automate the operation of a smartphone and automatically create a manual.Some or all of the above-mentioned processes in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can create a manual using an AI model that inputs the generated explanatory text and outputs a manual.

[0071] The recognition unit recognizes the smartphone screen and performs a specific operation. The recognition unit recognizes the smartphone screen using, for example, a camera and a touch sensor, and detects specific icons and buttons. Specifically, the entire screen is photographed using the smartphone camera, and the image data is processed in real time. As an image recognition technology, a deep learning model such as a convolutional neural network (CNN) can be used. This makes it possible to detect the position and shape of icons and buttons on the screen with high accuracy. In addition, by using a touch sensor, it is possible to detect a user's touch operation and perform a specific operation. There are capacitive and infrared touch sensors, and they can detect the position and movement of the user's finger with high accuracy. This allows the recognition unit to accurately grasp the operation performed by the user on the screen and perform the necessary processing. Furthermore, the recognition unit can learn the user's operation history and predict the next and subsequent operations. For example, the efficiency of operations can be improved by predicting the next operation performed by the user based on past operation patterns and preparing in advance. This allows the recognition unit to recognize the smartphone screen with high accuracy and support the user's operation.

[0072] The acquisition unit acquires a necessary screen capture based on the screen recognized by the recognition unit. For example, the acquisition unit captures the screen of the smartphone when a specific operation is completed and saves it as image data. Specifically, the screen contents are saved as image data by utilizing a screenshot function of the smartphone. The acquisition unit can detect the timing when a specific operation is completed and capture the screen at that moment. For example, a screenshot is acquired at the moment when a user presses a specific button or at the moment when a specific screen is displayed. The acquisition unit can also acquire multiple screen captures in succession. This allows the flow of operations and changes in the screen to be recorded in detail. Furthermore, the acquisition unit can save the captured image data in cloud storage and share it with other devices and systems. This allows the acquisition unit to efficiently acquire, save, and share screen captures of the smartphone.

[0073] The analysis unit analyzes the screen capture acquired by the acquisition unit and extracts information on the screen. The analysis unit extracts text information from the screen capture using, for example, image analysis technology. Specifically, the analysis unit analyzes the text included in the screen capture using OCR (optical character recognition) technology and extracts it as digital data. OCR technology analyzes the shape and pattern of characters and converts them into corresponding character codes, and is capable of highly accurate character recognition. The analysis unit can also extract information on specific icons and buttons using image analysis technology. For example, the analysis unit uses an image recognition algorithm to identify the position and shape of icons and buttons on the screen and extracts the information as digital data. Furthermore, the analysis unit can analyze the layout and structure on the screen based on the extracted information and infer the user's operation intention. This allows the analysis unit to analyze the acquired screen capture in detail and accurately extract necessary information.

[0074] The generation unit generates function and operation instructions based on the information extracted by the analysis unit. The generation unit, for example, uses natural language processing technology to analyze the extracted text information and generate an explanation in a form that is easy for the user to understand. Specifically, the generation unit uses a text generation AI (for example, LLM) to generate an explanation based on the extracted text information. The LLM is a model that has learned a large amount of text data and can generate natural sentences. The generation unit inputs the extracted information as a prompt to the LLM and generates an appropriate explanation. For example, a specific operation explanation such as "When you press this button, a setting screen will be displayed" can be generated. The generation unit can also generate explanation content including diagrams and videos based on the extracted information. For example, it generates diagrams showing operation procedures and videos showing actual operations and provides them to the user. This allows the generation unit to provide function and operation instructions in a form that is easy for the user to understand.

[0075] The creation unit creates a manual by combining the functions and operation instructions generated by the generation unit. For example, the creation unit simultaneously creates a print manual and a digital manual based on the generated explanatory text. Specifically, the creation unit creates a manual in a form that is easy for users to use by combining the generated explanatory text, illustrations, and videos. The print manual is provided in paper form, and the digital manual is provided in PDF or HTML format. The creation unit can also create an interactive manual. For example, an interactive manual can be created in which, when a user performs a specific operation, an explanation related to the operation is displayed. Furthermore, some or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can create a manual using an AI model that takes the generated explanatory text as input and outputs a manual. This allows the creation unit to efficiently and highly accurately create a manual and provide it to the user.

[0076] The recognition unit can recognize the screen of the smartphone using a camera or a touch sensor and detect a specific icon or button. The recognition unit, for example, takes a picture of the screen of the smartphone with a camera and detects a specific icon or button using image recognition technology. For example, the recognition unit takes a picture of an icon or button on the screen using the front or rear camera of the smartphone and detects them using image recognition technology. The recognition unit can also detect a touch operation of a user using a touch sensor and perform a specific operation. For example, the recognition unit detects a touch operation of a user using a capacitive touch sensor and recognizes a specific icon or button. This allows the recognition unit to accurately recognize the screen of the smartphone and perform a specific operation. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input image data taken by a camera to the generation AI and cause the generation AI to detect a specific icon or button.

[0077] The acquisition unit can capture the screen of the smartphone when the operation is completed and save it as image data. For example, the acquisition unit captures the screen of the smartphone when a specific operation is completed and saves it as image data. For example, the acquisition unit acquires the screen of the smartphone as a screenshot and saves it as image data in JPEG format or PNG format. The acquisition unit can also acquire multiple screen captures continuously when a specific operation is completed. For example, the acquisition unit acquires screen captures continuously at regular intervals from the moment a specific event occurs and records the flow of the operation. This allows the acquisition unit to accurately acquire and save the necessary screen captures. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the screen capture of the smartphone to the generation AI and cause the generation AI to execute the saving of the image data.

[0078] The analysis unit can perform image analysis of the screen capture and extract specific text information. The analysis unit extracts text information from the screen capture using, for example, image analysis technology. For example, the analysis unit extracts text information from the screen capture using OCR (optical character recognition) technology. The analysis unit can also extract information of specific icons and buttons using image analysis technology. For example, the analysis unit preferentially analyzes specific colors and shapes in the screen capture and extracts important information. This allows the analysis unit to extract necessary information from the screen capture. A part 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 image data of the screen capture to the generation AI and cause the generation AI to extract text information.

[0079] The generation unit can analyze the extracted text information using a natural language processing technique and generate an explanatory sentence in a form that is easy for the user to understand. The generation unit can analyze the extracted text information using, for example, a natural language processing technique and generate an explanatory sentence in a form that is easy for the user to understand. For example, the generation unit can generate an explanatory sentence based on the extracted text information using a text generation AI (for example, LLM). The generation unit can also generate explanatory content including diagrams and videos based on the extracted information. For example, the generation unit generates explanatory content including diagrams based on the extracted text information. This allows the generation unit to generate function and operation explanations in a form that is easy for the user to understand. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the extracted text information to the generation AI and cause the generation AI to execute generation of the explanatory sentence.

[0080] The creation unit can create a manual by combining the generated functions or operation instructions. The creation unit, for example, simultaneously creates a print manual and a digital manual based on the generated explanatory text. For example, the creation unit can also create an interactive manual based on the generated explanatory text. This allows the creation unit to automatically create a manual. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can create a manual using an AI model that inputs the generated explanatory text and outputs a manual.

[0081] The recognition unit can estimate the user's emotion and adjust the recognition accuracy based on the estimated user's emotion. For example, when the user is stressed, the recognition unit increases the recognition accuracy to prevent erroneous operation. Also, when the user is relaxed, the recognition unit can perform an operation with normal recognition accuracy. Furthermore, when the user is in a hurry, the recognition unit can prioritize the recognition speed and perform an operation quickly. In this way, the recognition unit can prevent erroneous operation by adjusting the recognition accuracy according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The recognition unit can automatically adjust the brightness and contrast of the smartphone screen to improve the recognition accuracy. For example, the recognition unit can automatically adjust the brightness of the smartphone screen to minimize the influence of external light. The recognition unit can also automatically adjust the contrast of the smartphone screen to improve visibility. Furthermore, the recognition unit can automatically adjust the color temperature of the smartphone screen to optimize the recognition accuracy. As a result, the recognition unit can improve the recognition accuracy by adjusting the brightness and contrast of the screen. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can cause the generation AI to adjust the brightness and contrast of the smartphone screen.

[0083] The recognition unit can refer to the usage history of the smartphone screen and predict and recognize a specific operation pattern. The recognition unit, for example, predicts and recognizes frequently used applications based on the past usage history. The recognition unit can also predict and recognize the next operation based on the past operation pattern. Furthermore, the recognition unit can predict and recognize an operation to be performed in a specific time period based on the past usage history. As a result, the recognition unit can improve the recognition accuracy by predicting the operation pattern based on the usage history. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the usage history data to the generation AI and cause the generation AI to execute the prediction of the operation pattern.

[0084] The recognition unit can estimate the user's emotion and determine the priority of icons and buttons to be recognized based on the estimated user's emotion. For example, when the user is stressed, the recognition unit prioritizes recognition of important icons and buttons. In addition, when the user is relaxed, the recognition unit can also recognize icons and buttons with normal priority. Furthermore, when the user is in a hurry, the recognition unit can also prioritize recognition of frequently used icons and buttons. In this way, the recognition unit can improve the efficiency of operation by determining the priority of icons and buttons to be recognized according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of icons and buttons.

[0085] The recognition unit can preferentially recognize region-specific icons and buttons in consideration of the geographical location information of the smartphone screen. For example, the recognition unit preferentially recognizes region-specific application icons based on the user's current location. The recognition unit can also preferentially recognize region-specific setting items based on the user's current location. Furthermore, the recognition unit can also preferentially recognize region-specific service icons based on the user's current location. As a result, the recognition unit can preferentially recognize region-specific icons and buttons by considering the geographical location information. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input geographical location information data to the generation AI and cause the generation AI to recognize region-specific icons and buttons.

[0086] The recognition unit can preferentially recognize a specific icon or button based on the age or gender of the user of the smartphone screen. For example, the recognition unit preferentially recognizes an icon of an application suitable for the age based on the user's age. The recognition unit can also preferentially recognize a setting item suitable for the gender based on the user's gender. Furthermore, the recognition unit can also preferentially recognize an icon of a specific service based on the user's age or gender. This allows the recognition unit to preferentially recognize a specific icon or button based on the user's age or gender. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input the user's age and gender data to the generation AI and cause the generation AI to execute the determination of the priority order of icons and buttons.

[0087] The acquisition unit can estimate the user's emotion and adjust the timing of the screen capture based on the estimated user's emotion. For example, when the user is stressed, the acquisition unit acquires the screen capture immediately after an important operation is completed. Also, when the user is relaxed, the acquisition unit can acquire the screen capture at a normal timing. Furthermore, when the user is in a hurry, the acquisition unit can acquire the screen capture before the operation is completed. In this way, the acquisition unit can acquire important information without missing it by adjusting the timing of the screen capture according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of the screen capture.

[0088] The acquisition unit can continuously acquire multiple screen captures at the time when a specific operation on the smartphone screen is completed. For example, the acquisition unit continuously acquires screen captures before and after a setting change is completed. In addition, the acquisition unit can continuously acquire screen captures before and after the installation at the time when the installation of an application is completed. Furthermore, the acquisition unit can continuously acquire multiple screen captures showing the flow of the operation at the time when the specific operation is completed. Thereby, the acquisition unit can accurately record the flow of the operation by continuously acquiring screen captures at the time when the specific operation is completed. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can cause the generation AI to execute the continuous acquisition of screen captures.

[0089] The acquisition unit can capture only a specific area of ​​the smartphone screen and efficiently acquire the necessary information. The acquisition unit, for example, captures only an area where a specific item of a setting screen is displayed. The acquisition unit can also capture only an area where a specific function of an application is displayed. Furthermore, the acquisition unit can capture only an area where a specific operation is performed and efficiently acquire the necessary information. As a result, the acquisition unit can efficiently acquire the necessary information by capturing only the specific area. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can cause the generation AI to execute the capture of the specific area.

[0090] The acquisition unit can estimate the user's emotion and determine the priority of screen captures to be acquired based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring important screen captures. In addition, when the user is relaxed, the acquisition unit can also acquire screen captures with normal priority. Furthermore, when the user is in a hurry, the acquisition unit can also prioritize acquiring frequently used screen captures. In this way, the acquisition unit can prioritize acquiring important information by determining the priority of screen captures according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the screen captures.

[0091] The acquisition unit can preferentially acquire highly relevant screen captures by taking into account the geographical location information of the smartphone screen. For example, the acquisition unit preferentially acquires screen captures of region-specific applications based on the user's current location. The acquisition unit can also preferentially acquire screen captures of region-specific setting items based on the user's current location. Furthermore, the acquisition unit can also preferentially acquire screen captures of region-specific services based on the user's current location. Thereby, the acquisition unit can preferentially acquire highly relevant screen captures by taking into account the geographical location information. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input geographical location information data to the generation AI and cause the generation AI to acquire highly relevant screen captures.

[0092] The acquisition unit can analyze the social media activity of the user of the smartphone screen and acquire related screen captures. The acquisition unit can acquire screen captures of related applications based on the user's social media activity, for example. The acquisition unit can also acquire screen captures of related setting items based on the user's social media activity. Furthermore, the acquisition unit can acquire screen captures of related services based on the user's social media activity. This allows the acquisition unit to efficiently acquire related screen captures by analyzing social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input social media activity data to the generation AI and cause the generation AI to acquire related screen captures.

[0093] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit increases the analysis accuracy and prevents erroneous analysis. In addition, when the user is relaxed, the analysis unit can also perform analysis with normal analysis accuracy. Furthermore, when the user is in a hurry, the analysis unit can prioritize analysis speed and perform analysis quickly. In this way, the analysis unit can prevent erroneous analysis by adjusting the analysis accuracy according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to adjust the analysis accuracy.

[0094] The analysis unit can give priority to analyzing a specific color or shape when analyzing the image of the screen capture. For example, the analysis unit can give priority to analyzing a specific color in the screen capture to extract important information. The analysis unit can also give priority to analyzing a specific shape in the screen capture to extract important information. Furthermore, the analysis unit can also give priority to analyzing a specific icon or button in the screen capture to extract important information. This allows the analysis unit to efficiently extract important information by giving priority to analyzing a specific color or shape. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input image data of the screen capture to the generation AI and cause the generation AI to perform analysis of a specific color or shape.

[0095] The analysis unit can improve the analysis accuracy by referring to past analysis results when analyzing the image of the screen capture. The analysis unit can improve the analysis accuracy of a similar screen capture, for example, based on past analysis results. The analysis unit can also recognize a specific pattern based on past analysis results and improve the analysis accuracy. Furthermore, the analysis unit can prevent a specific error based on past analysis results and improve the analysis accuracy. Thereby, the analysis unit can improve the analysis accuracy by referring to past analysis results. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0096] The analysis unit can estimate the user's emotion and determine the priority of information to be analyzed based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit prioritizes analyzing important information. In addition, when the user is relaxed, the analysis unit can also analyze information with normal priority. Furthermore, when the user is in a hurry, the analysis unit can prioritize analyzing frequently used information. In this way, the analysis unit can prioritize analyzing important information by determining the priority of information to be analyzed according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0097] The analysis unit can preferentially analyze region-specific information in consideration of the geographical location information of the screen capture. The analysis unit, for example, preferentially analyzes region-specific setting items based on the geographical location information of the screen capture. The analysis unit can also preferentially analyze information of region-specific applications based on the geographical location information of the screen capture. Furthermore, the analysis unit can also preferentially analyze information of region-specific services based on the geographical location information of the screen capture. Thereby, the analysis unit can preferentially analyze region-specific information by considering the geographical location information. A part 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 geographical location information data to the generation AI and cause the generation AI to execute analysis of the region-specific information.

[0098] The analysis unit can preferentially analyze specific information based on the age and gender of the user of the screen capture. For example, the analysis unit preferentially analyzes information suitable for the age based on the age of the user. The analysis unit can also preferentially analyze information suitable for the gender based on the gender of the user. Furthermore, the analysis unit can also preferentially analyze information of a specific service based on the age and gender of the user. This allows the analysis unit to preferentially analyze specific information based on the age and gender of the user. A part 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 age and gender data of the user to the generation AI and cause the generation AI to determine the priority order of information.

[0099] The generation unit can estimate the user's emotion and adjust the expression method of the explanatory text to be generated based on the estimated user's emotion. For example, when the user is feeling stressed, the generation unit generates a concise and easy-to-understand explanatory text. Furthermore, when the user is relaxed, the generation unit can also generate a detailed explanatory text. Furthermore, when the user is in a hurry, the generation unit can generate a short explanatory text that focuses on the main points. In this way, the generation unit can generate an explanatory text that is easy for the user to understand by adjusting the expression method of the explanatory text according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the explanatory text.

[0100] The generation unit can generate a plurality of different explanatory sentences based on the extracted text information and provide the user with options. The generation unit can generate two types of explanatory sentences, a concise explanatory sentence and a detailed explanatory sentence, based on the extracted text information, for example. The generation unit can also generate explanatory sentences using different expression methods based on the extracted text information. Furthermore, the generation unit can generate explanatory sentences from different perspectives based on the extracted text information and provide options to the user. As a result, the generation unit can provide options to the user by generating a plurality of different explanatory sentences. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the extracted text information to a generation AI and cause the generation AI to generate a plurality of different explanatory sentences.

[0101] The generating unit can generate explanatory content including diagrams and videos based on the extracted text information. The generating unit generates explanatory content including diagrams, for example, based on the extracted text information. The generating unit can also generate explanatory content including videos based on the extracted text information. Furthermore, the generating unit can generate explanatory content combining diagrams and videos based on the extracted text information. In this way, the generating unit can provide an explanation that is visually easy to understand by generating explanatory content including diagrams and videos. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the extracted text information to a generating AI and cause the generating AI to generate diagrams and videos.

[0102] The generation unit can estimate the user's emotion and adjust the length of the explanatory text to be generated based on the estimated user's emotion. For example, when the user is stressed, the generation unit generates a short explanatory text that is to the point. The generation unit can also generate a detailed explanatory text when the user is relaxed. Furthermore, when the user is in a hurry, the generation unit can generate a concise explanatory text that can be quickly understood. In this way, the generation unit can provide an explanatory text of an optimal length for the user by adjusting the length of the explanatory text according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the explanatory text.

[0103] The generation unit can generate explanatory text in different languages ​​based on the extracted text information. The generation unit can generate explanatory text in English, for example, based on the extracted text information. The generation unit can also generate explanatory text in Chinese based on the extracted text information. Furthermore, the generation unit can generate explanatory text in Spanish based on the extracted text information. This allows the generation unit to provide multilingual explanations by generating explanatory text in different languages. A part or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the extracted text information to a generation AI and cause the generation AI to generate explanatory text in different languages.

[0104] The generation unit can generate a customized explanatory text for a specific user group based on the extracted text information. The generation unit generates, for example, an explanatory text for beginners based on the extracted text information. The generation unit can also generate an explanatory text for advanced users based on the extracted text information. Furthermore, the generation unit can generate an explanatory text specialized for a specific industry or use based on the extracted text information. As a result, the generation unit can provide an optimal explanation for the user by generating an explanatory text customized for a specific user group. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the extracted text information to the generation AI and cause the generation AI to generate an explanatory text for a specific user group.

[0105] The creation unit can estimate the user's emotion and adjust the configuration of the manual based on the estimated user's emotion. For example, when the user is feeling stressed, the creation unit creates a manual with a concise and easy-to-understand configuration. Furthermore, when the user is relaxed, the creation unit can also create a manual with a configuration including detailed information. Furthermore, when the user is in a hurry, the creation unit can also create a manual with a short configuration that focuses on the main points. In this way, the creation unit can provide a manual that is easy for the user to understand by adjusting the configuration of the manual according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the configuration of the manual.

[0106] The creation unit can create an interactive manual based on the generated explanatory text. For example, the creation unit creates an interactive manual that allows the user to try operations based on the generated explanatory text. The creation unit can also create an interactive manual in a format in which the user answers questions based on the generated explanatory text. Furthermore, the creation unit can also create an interactive manual in a format in which the user can select options based on the generated explanatory text. In this way, the creation unit can provide a manual in a format in which the user can try operations by creating an interactive manual. Part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the generated explanatory text to a generation AI and cause the generation AI to create an interactive manual.

[0107] The creation unit can simultaneously create a print manual and a digital manual based on the generated explanatory text. The creation unit, for example, creates a print manual based on the generated explanatory text. The creation unit can also create a digital manual based on the generated explanatory text. Furthermore, the creation unit can simultaneously create both print and digital manuals based on the generated explanatory text. As a result, the creation unit can provide a manual that meets the needs of the user by simultaneously creating both print and digital manuals. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the generated explanatory text to a generation AI and cause the generation AI to execute the creation of print and digital manuals.

[0108] The creation unit can estimate the user's emotion and determine the priority of the manual based on the estimated user's emotion. For example, when the user is feeling stressed, the creation unit creates an important manual with priority. In addition, when the user is relaxed, the creation unit can also create a manual with normal priority. Furthermore, when the user is in a hurry, the creation unit can also create a frequently used manual with priority. In this way, the creation unit can create an important manual with priority by determining the priority of the manual according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the manual.

[0109] The creation unit can create a manual optimized for a specific device based on the generated explanatory text. The creation unit can create a manual optimized for a smartphone based on the generated explanatory text, for example. The creation unit can also create a manual optimized for a tablet based on the generated explanatory text. Furthermore, the creation unit can create a manual optimized for a personal computer based on the generated explanatory text. As a result, the creation unit can provide a manual that is easy for users to use by creating a manual optimized for a specific device. A part or all of the above-mentioned processing in the creation unit can be performed using, for example, AI, or can be performed without using AI. For example, the creation unit can input the generated explanatory text to a generation AI and cause the generation AI to create a manual optimized for a specific device.

[0110] The creation unit can create a manual specialized for a specific industry or use based on the generated explanatory text. For example, the creation unit creates a manual for the medical industry based on the generated explanatory text. The creation unit can also create a manual for the education industry based on the generated explanatory text. Furthermore, the creation unit can create a manual for the manufacturing industry based on the generated explanatory text. As a result, the creation unit can provide an optimal manual for the user by creating a manual specialized for a specific industry or use. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the generated explanatory text to a generation AI and cause the generation AI to create a manual specialized for a specific industry or use.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The recognition unit can recognize a user's voice command and perform a specific operation based on the voice command. For example, when a user gives a voice command such as "Open the settings screen," the recognition unit analyzes this command using voice recognition technology and automatically opens the smartphone's settings screen. When a user gives a command such as "Turn on Wi-Fi," the recognition unit can automatically turn on the Wi-Fi setting. Furthermore, when a user gives a command such as "Install an app," the recognition unit can automatically install the specified application. This allows the recognition unit to operate the smartphone more intuitively using voice commands.

[0113] The acquisition unit can estimate the user's emotion and adjust the resolution of the screen capture based on the estimated user's emotion. For example, when the user is stressed, the acquisition unit can acquire a high-resolution screen capture to provide detailed information. When the user is relaxed, the acquisition unit can also acquire a normal-resolution screen capture. Furthermore, when the user is in a hurry, the acquisition unit can acquire a low-resolution screen capture to provide information quickly. In this way, the acquisition unit can provide optimal information for the user by adjusting the resolution of the screen capture according to the user's emotion.

[0114] When analyzing a screen capture image, the analysis unit can improve the analysis accuracy by referring to the operation history of a specific user. For example, setting items that the user has frequently used in the past are analyzed preferentially to extract important information. In addition, it is also possible to recognize specific patterns based on the past operation history and improve the analysis accuracy. Furthermore, it is also possible to prevent specific errors and improve the analysis accuracy based on the past operation history. In this way, the analysis unit can improve the analysis accuracy by referring to the operation history of the user.

[0115] The generation unit can estimate the user's emotions and adjust the tone of the generated explanatory text based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate explanatory text in a gentle tone to reduce the user's anxiety. If the user is relaxed, the generation unit can also generate explanatory text in a normal tone. Furthermore, if the user is in a hurry, the generation unit can also generate explanatory text in a concise and direct tone. In this way, the generation unit can provide an optimal explanation for the user by adjusting the tone of the explanatory text according to the user's emotions.

[0116] The creation unit can create a manual including a simulation function that allows the user to try out operations based on the generated explanatory text. For example, a simulation can be provided that allows the user to try out operations on a setting screen, allowing the user to practice before performing actual operations. It is also possible to allow the user to experience the installation procedure of an application through a simulation. Furthermore, it is also possible to allow the user to check the effects of specific setting changes through a simulation. In this way, by creating a manual that includes a simulation function, the creation unit can provide a manual in a format that allows the user to try out operations.

[0117] The recognition unit can estimate the user's emotion and adjust the difficulty of the operation based on the estimated user's emotion. For example, when the user is stressed, the recognition unit can provide easy operations preferentially to reduce the burden on the user. When the user is relaxed, the recognition unit can provide normal operations. Furthermore, when the user is in a hurry, the recognition unit can provide operations that can be completed quickly preferentially. In this way, the recognition unit can provide an optimal operation experience for the user by adjusting the difficulty of the operation according to the user's emotion.

[0118] The acquisition unit can capture only a specific area of ​​the smartphone screen and efficiently acquire the required information. For example, only an area where a specific item of a setting screen is displayed can be captured. Also, only an area where a specific function of an application is displayed can be captured. Furthermore, only an area where a specific operation is being performed can be captured and the required information can be efficiently acquired. In this way, the acquisition unit can efficiently acquire the required information by capturing only the specific area.

[0119] The analysis unit can estimate the user's emotions and determine the priority of information to be analyzed based on the estimated user's emotions. For example, when the user is feeling stressed, important information is analyzed with priority. When the user is relaxed, information can be analyzed with normal priority. Furthermore, when the user is in a hurry, frequently used information can be analyzed with priority. In this way, the analysis unit can prioritize important information by determining the priority of information to be analyzed according to the user's emotions.

[0120] The generation unit can generate explanatory text in different languages ​​based on the extracted text information. For example, an explanatory text can be generated in English based on the extracted text information. Also, an explanatory text can be generated in Chinese based on the extracted text information. Furthermore, an explanatory text can be generated in Spanish based on the extracted text information. In this way, the generation unit can provide an explanatory text in multiple languages ​​by generating explanatory text in different languages.

[0121] The creation unit can create a manual optimized for a specific device based on the generated explanatory text. For example, a manual optimized for a smartphone can be created based on the generated explanatory text. Also, a manual optimized for a tablet can be created based on the generated explanatory text. Furthermore, a manual optimized for a PC can be created based on the generated explanatory text. In this way, the creation unit can provide a manual that is easy for users to use by creating a manual optimized for a specific device.

[0122] The process flow of the second embodiment will be briefly described below.

[0123] Step 1: The recognition unit recognizes the smartphone screen and performs a specific operation. The recognition unit uses a camera and touch sensor to recognize the smartphone screen and detects specific icons and buttons. For example, the smartphone screen is photographed with a camera and specific icons and buttons are detected using image recognition technology. It is also possible to detect the user's touch operation using a touch sensor and perform a specific operation. Step 2: The acquisition unit acquires the necessary screen capture based on the screen recognized by the recognition unit. The acquisition unit captures the smartphone screen when the specific operation is completed and saves it as image data. For example, the smartphone screen is acquired as a screenshot and saved as image data. It is also possible to acquire multiple screen captures consecutively when the specific operation is completed. Step 3: The analysis unit analyzes the screen capture acquired by the acquisition unit and extracts information on the screen. The analysis unit extracts text information from the screen capture using image analysis technology. For example, the analysis unit extracts text information from the screen capture using OCR (Optical Character Recognition) technology. The analysis unit can also extract information on specific icons and buttons using image analysis technology. Step 4: The generation unit generates function and operation explanations based on the information extracted by the analysis unit. The generation unit analyzes the extracted text information using natural language processing technology and generates explanatory text in a form that is easy for the user to understand. For example, the generation unit generates explanatory text based on the extracted text information using a text generation AI (e.g., LLM). It is also possible to generate explanatory content including diagrams and videos based on the extracted information. Step 5: The creation unit creates a manual by combining the functions and operation instructions generated by the generation unit. The creation unit simultaneously creates a print manual and a digital manual based on the generated explanatory text. For example, an interactive manual can be created based on the generated explanatory text. Some or all of the processing in the creation unit may be performed using AI, or may be performed without using AI. For example, a manual can be created using an AI model that takes the generated explanatory text as input and outputs a manual.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0125] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0126] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements including the above-mentioned recognition unit, acquisition unit, analysis unit, generation unit, and creation unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the recognition unit recognizes the screen of the smartphone using the camera 42 and touch sensor of the smart device 14, and detects a specific icon or button. The acquisition unit captures the screen of the smartphone by the control unit 46A of the smart device 14 at the time when a specific operation is completed, and saves it as image data. The analysis unit analyzes the acquired screen capture by the specific processing unit 290 of the data processing device 12, and extracts information on the screen. The generation unit generates a function or operation explanation based on the extracted information by the specific processing unit 290 of the data processing device 12. The creation unit creates a manual based on the explanation generated by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0131] 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.

[0132] 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 the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] 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 performed in a secure state.

[0135] Fig. 4 shows an example of 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.

[0136] 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.

[0137] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0141] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements including the above-mentioned recognition unit, acquisition unit, analysis unit, generation unit, and creation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit recognizes the screen of the smartphone using the camera 42 and touch sensor of the smart glasses 214, and detects a specific icon or button. The acquisition unit captures the screen of the smartphone by the control unit 46A of the smart glasses 214 at the time when a specific operation is completed, and saves it as image data. The analysis unit analyzes the acquired screen capture by the specific processing unit 290 of the data processing device 12, and extracts information on the screen. The generation unit generates a function or operation explanation based on the extracted information by the specific processing unit 290 of the data processing device 12. The creation unit creates a manual based on the explanation generated by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0147] 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.

[0148] 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 the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0150] 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 performed in a secure state.

[0151] 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.

[0152] 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.

[0153] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0155] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] 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 voice indicating a user input for 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.

[0157] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements including the above-mentioned recognition unit, acquisition unit, analysis unit, generation unit, and creation unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the recognition unit recognizes the screen of the smartphone using the camera 42 and touch sensor of the headset type terminal 314, and detects a specific icon or button. The acquisition unit captures the screen of the smartphone by the control unit 46A of the headset type terminal 314 at the time when a specific operation is completed, and saves it as image data. The analysis unit analyzes the acquired screen capture by the specific processing unit 290 of the data processing device 12, and extracts information on the screen. The generation unit generates a function or operation explanation based on the extracted information by the specific processing unit 290 of the data processing device 12. The creation unit creates a manual based on the explanation generated by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0163] 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.

[0164] 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 the voice according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0166] 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 performed in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0168] 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.

[0169] 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.

[0170] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0171] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0172] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0173] 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 a voice indicating a user input for 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.

[0174] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0175] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0176] Each of the multiple elements including the above-mentioned recognition unit, acquisition unit, analysis unit, generation unit, and creation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit recognizes the screen of the smartphone using the camera 42 and touch sensor of the robot 414 and detects a specific icon or button. The acquisition unit captures the screen of the smartphone by the control unit 46A of the robot 414 at the time when a specific operation is completed, and saves it as image data. The analysis unit analyzes the acquired screen capture by the specific processing unit 290 of the data processing device 12 and extracts information on the screen. The generation unit generates functions and operation instructions based on the extracted information by the specific processing unit 290 of the data processing device 12. The creation unit creates a manual based on the explanation generated by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0178] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0180] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0182] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0184] 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.

[0185] 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 Universal Serial Bus (USB) 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.

[0186] In addition, 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 upon request from the data processing device 12.

[0187] 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.

[0188] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0189] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0190] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0191] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0193] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0195] (Appendix 1) A recognition unit that recognizes the smartphone screen and performs specific operations; an acquisition unit that acquires a necessary screen capture based on the screen recognized by the recognition unit; an analysis unit that analyzes the screen capture acquired by the acquisition unit and extracts information on the screen; a generation unit that generates a function or operation instruction based on the information extracted by the analysis unit; a creation unit that creates a manual by combining the functions or operation instructions generated by the creation unit; A system characterized by: (Appendix 2) The recognition unit is Using a camera or touch sensor to recognize the smartphone screen and detect specific icons or buttons 2. The system of claim 1, (Appendix 3) The acquisition unit is Once the operation is complete, the smartphone screen is captured and saved as image data. 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze screen captures and extract specific text information 2. The system of claim 1, (Appendix 5) The generation unit is The extracted text information is analyzed using natural language processing technology to generate an explanation that is easy for the user to understand. 2. The system of claim 1, (Appendix 6) The creation unit is Create a manual by combining the generated function or operation instructions. 2. The system of claim 1, (Appendix 7) The recognition unit is Estimate the user's emotion and adjust the recognition accuracy based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The recognition unit is Automatically adjusts the brightness and contrast of your smartphone screen to improve recognition accuracy 2. The system of claim 1, (Appendix 9) The recognition unit is Refer to the usage history of the smartphone screen and predict and recognize specific operation patterns 2. The system of claim 1, (Appendix 10) The recognition unit is Estimate the user's emotions and prioritize icons and buttons based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The recognition unit is Taking into account the geographic location of the smartphone screen, it prioritizes recognition of region-specific icons and buttons 2. The system of claim 1, (Appendix 12) The recognition unit is Preferentially recognize certain icons and buttons on a smartphone screen based on the age and gender of the user 2. The system of claim 1, (Appendix 13) The acquisition unit is To estimate a user's emotion and adjust the timing of screen capture based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The acquisition unit is Capture multiple screen captures in succession when a specific operation on the smartphone screen is completed 2. The system of claim 1, (Appendix 15) The acquisition unit is Capture only specific areas of your smartphone screen to efficiently obtain the information you need 2. The system of claim 1, (Appendix 16) The acquisition unit is To estimate a user's emotion and determine a priority order for screen captures to be acquired based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The acquisition unit is Prioritize relevant screen captures by taking into account the geographic location of the phone screen 2. The system of claim 1, (Appendix 18) The acquisition unit is Analyze users' social media activity on their smartphone screens and capture relevant screen captures 2. The system of claim 1, (Appendix 19) The analysis unit is Estimate user emotions and adjust analysis accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The analysis unit is When analyzing screen capture images, give priority to analyzing specific colors or shapes 2. The system of claim 1, (Appendix 21) The analysis unit is When analyzing screen capture images, improve the accuracy of the analysis by referring to past analysis results. 2. The system of claim 1, (Appendix 22) The analysis unit is Estimate the user's emotions and determine the priority of information to be analyzed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The analysis unit is Consider the geographic location of screen captures to prioritize analysis of region-specific information 2. The system of claim 1, (Appendix 24) The analysis unit is Prioritize analysis of certain information based on the age and gender of the screen capture user 2. The system of claim 1, (Appendix 25) The generation unit is Estimate the user's emotions and adjust the way the explanation is generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 26) The generation unit is Based on the extracted text information, multiple different explanations are generated and the user is provided with options. 2. The system of claim 1, (Appendix 27) The generation unit is Generate explanatory content, including illustrations and videos, based on the extracted text information 2. The system of claim 1, (Appendix 28) The generation unit is Estimate the user's emotions and adjust the length of the explanation text to be generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The generation unit is Generate descriptions in different languages ​​based on the extracted text information 2. The system of claim 1, (Appendix 30) The generation unit is Generate customized descriptions for specific user groups based on the extracted text information 2. The system of claim 1, (Appendix 31) The creation unit is Estimate the user's emotions and adjust the manual's structure based on the estimated user emotions. 2. The system of claim 1, (Appendix 32) The creation unit is Create an interactive manual based on the generated text 2. The system of claim 1, (Appendix 33) The creation unit is Based on the generated explanatory text, a printed manual and a digital manual are created simultaneously. 2. The system of claim 1, (Appendix 34) The creation unit is Estimate user emotions and prioritize manuals based on the estimated user emotions 2. The system of claim 1, (Appendix 35) The creation unit is Create manuals optimized for specific devices based on the generated descriptions 2. The system of claim 1, (Appendix 36) The creation unit is Create manuals specialized for specific industries or applications based on the generated descriptions. 2. The system of claim 1, [Explanation of symbols]

[0196] 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. A recognition unit that recognizes the smartphone screen and performs specific operations; an acquisition unit that acquires a necessary screen capture based on the screen recognized by the recognition unit; an analysis unit that analyzes the screen capture acquired by the acquisition unit and extracts information on the screen; a generation unit that generates a function or operation instruction based on the information extracted by the analysis unit; a creation unit that creates a manual by combining the functions or operation instructions generated by the creation unit; A system characterized by:

2. The recognition unit is Using a camera or touch sensor to recognize the smartphone screen and detect specific icons or buttons 2. The system of claim 1 .

3. The acquisition unit is Once the operation is complete, the smartphone screen is captured and saved as image data.

2. The system of claim 1 .

4. The analysis unit is Analyze screen captures and extract specific text information 2. The system of claim 1 .

5. The generation unit is The extracted text information is analyzed using natural language processing technology to generate an explanation that is easy for the user to understand.

2. The system of claim 1 .

6. The creation unit is Create a manual by combining the generated function or operation instructions.

2. The system of claim 1 .

7. The recognition unit is Estimate the user's emotion and adjust the recognition accuracy based on the estimated user's emotion.

2. The system of claim 1 .

8. The recognition unit is Automatically adjusts the brightness and contrast of your smartphone screen to improve recognition accuracy 2. The system of claim 1 .

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