system

Abstract

The system according to this embodiment aims to prevent missed entries or confirmations of appointments by automatically reflecting information from notices and screenshots into a schedule management application. [Solution] The system according to the embodiment comprises a shooting unit, a character recognition unit, a reflection unit, and a notification unit. The shooting unit takes a picture of a notice or screenshot. The character recognition unit analyzes the image taken by the shooting unit and recognizes the characters. The reflection unit reflects the character information recognized by the character recognition unit into the schedule management application. The notification unit sends a reminder notification when the scheduled event reflected by the reflection unit approaches.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is laborious to reflect the information of notices distributed on paper or screenshots in schedule management, and there is a risk of input omission or confirmation omission of schedules.

[0005] The system according to the embodiment aims to automatically reflect the information of notice papers and screenshots in a schedule management application and prevent input omission and confirmation omission of schedules.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a shooting unit, a character recognition unit, a reflection unit, and a notification unit. The shooting unit takes a picture of a notice or screenshot. The character recognition unit analyzes the image taken by the shooting unit and recognizes the characters. The reflection unit reflects the character information recognized by the character recognition unit into a schedule management application. The notification unit sends a reminder notification when the schedule reflected by the reflection unit approaches. [Effects of the Invention]

[0007] The system according to this embodiment can automatically reflect information from notices and screenshots into a schedule management app, preventing missed entries or confirmations of appointments. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​schedule system according to an embodiment of the present invention is a system for efficiently managing schedules for notices from nurseries and elementary schools. The AI ​​schedule system allows users to take a picture of a notice or screenshot with their smartphone, and the AI ​​analyzes the image, recognizes the text, and automatically reflects it in a schedule management app. Furthermore, the AI ​​sends reminder notifications as the scheduled event approaches, preventing users from missing important appointments. This allows users to efficiently manage their schedules without forgetting important appointments. For example, the AI ​​schedule system is an extremely convenient tool for parents with children and people with busy lifestyles. For example, when a user takes a picture of a notice or screenshot with their smartphone, the AI ​​analyzes the image, recognizes the text, and automatically reflects it in a schedule management app. Furthermore, the AI ​​sends reminder notifications as the scheduled event approaches, preventing users from missing important appointments. This allows users to efficiently manage their schedules without forgetting important appointments. For example, the AI ​​schedule system is an extremely convenient tool for parents with children and people with busy lifestyles. This allows users to efficiently manage their schedules without forgetting important appointments.

[0029] The AI ​​schedule according to this embodiment comprises a shooting unit, a character recognition unit, a reflection unit, and a notification unit. The shooting unit takes pictures of notices or screenshots. The shooting unit can, for example, take pictures of notices or screenshots using a smartphone camera. It can also take pictures using a digital camera or a tablet camera. Furthermore, the shooting unit can automatically adjust the image resolution and focus to obtain the optimal image. For example, the shooting unit can take a picture of a notice using a smartphone camera and obtain the image. It can also take a screenshot using a digital camera and obtain the image. Furthermore, the shooting unit can take a picture of a notice using a tablet camera and obtain the image. The character recognition unit analyzes the image taken by the shooting unit and recognizes the characters. The character recognition unit can, for example, recognize characters in an image using OCR (optical character recognition) technology. It can also recognize handwritten characters using machine learning algorithms. Furthermore, the character recognition unit can distinguish between different fonts and handwritten characters using deep learning technology. For example, the character recognition unit can recognize printed characters using OCR technology. Furthermore, the character recognition unit can recognize handwritten characters using machine learning algorithms. In addition, the character recognition unit can distinguish between different fonts and handwritten characters using deep learning technology. The reflection unit reflects the character information recognized by the character recognition unit into the schedule management app. For example, the reflection unit can convert the recognized character information into a specific format and input it into the schedule management app. The reflection unit can also automatically reflect the recognized character information into the schedule management app. Furthermore, the reflection unit has a function to detect and adjust schedule overlaps. For example, the reflection unit converts the recognized character information into a specific format and inputs it into the schedule management app. The reflection unit can also automatically reflect the recognized character information into the schedule management app. Furthermore, the reflection unit has a function to detect and adjust schedule overlaps. The notification unit sends a reminder notification when an appointment reflected by the reflection unit is approaching.The notification unit can send reminder notifications using methods such as push notifications or email notifications. Furthermore, the notification unit can customize the timing and content of notifications based on user settings. In addition, the notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, the notification unit sends reminder notifications using push notifications. It can also send reminder notifications using email notifications. Furthermore, the notification unit can customize the timing and content of notifications based on user settings. As a result, the AI ​​schedule according to this embodiment can efficiently reflect notices and screenshots in the schedule management app and send reminder notifications, preventing users from missing appointments.

[0030] The camera unit takes pictures of notices and screenshots. For example, the camera unit can use a smartphone camera to take pictures of notices and screenshots. It can also use a digital camera or tablet camera. Furthermore, the camera unit can automatically adjust image resolution and focus to obtain the optimal image. Specifically, when using a smartphone camera, the camera app automatically recognizes the subject and sets the optimal exposure and white balance. This allows the photographer to obtain high-quality images without requiring any special operation. When using a digital camera, the camera's autofocus function focuses on the subject, and the image stabilization function reduces blur during shooting. Similarly, when using a tablet camera, the large screen of the tablet can be used to check the subject while shooting. Furthermore, the camera unit has a function to take multiple images in succession and select the clearest image. This makes it possible to obtain high-quality images even with moving subjects or in low-light environments. The camera unit immediately sends the acquired image to the character recognition unit and proceeds to the next processing step.

[0031] The character recognition unit analyzes images captured by the image capture unit and recognizes characters. For example, the character recognition unit can recognize characters within an image using OCR (Optical Character Recognition) technology. It can also recognize handwritten characters using machine learning algorithms. Furthermore, it can distinguish between different fonts and handwritten characters using deep learning technology. Specifically, OCR technology detects character regions within an image and converts each character into digital data. Machine learning algorithms recognize handwritten characters with high accuracy based on a pre-trained dataset of handwritten characters. Deep learning technology uses neural networks to learn the characteristics of different fonts and handwritten characters, enabling the identification of more complex character patterns. For example, the character recognition unit can recognize printed characters using OCR technology and handwritten characters using machine learning algorithms. It can also distinguish between different fonts and handwritten characters using deep learning technology. As a result, the character recognition unit converts the character information in the captured image into digital data with high accuracy and proceeds to the next processing step. Furthermore, the character recognition unit is equipped with contextual analysis and spell-checking functions to improve the accuracy of the recognition results. This reduces misrecognition and allows for the provision of more accurate textual information.

[0032] The reflection unit reflects the character information recognized by the character recognition unit into the schedule management app. For example, the reflection unit can convert the recognized character information into a specific format and input it into the schedule management app. Furthermore, the reflection unit can automatically reflect the recognized character information into the schedule management app. Specifically, the reflection unit divides the recognized character information into items such as date, time, location, and content, and inputs them into the corresponding fields in the schedule management app. In addition, the reflection unit has a function to detect and adjust schedule overlaps. For example, if multiple appointments are entered for the same date and time, the reflection unit notifies the user and prompts them to make adjustments. The reflection unit can also set schedule priorities and display important appointments at the top. This allows users to manage their schedules efficiently. Moreover, the reflection unit has a function to link the schedule management app with other calendar and task management apps, enabling consistent schedule management across multiple platforms. For example, the reflection unit can synchronize with external calendars and automatically update schedule information. This allows users to centrally manage their schedules across multiple devices and applications.

[0033] The notification unit sends reminder notifications as appointments reflected by the reflection unit approach. The notification unit can send reminder notifications using methods such as push notifications or email notifications. The notification unit can also customize the timing and content of notifications based on user settings. Specifically, the notification unit can send reminder notifications based on user-set times and can send multiple notifications for important appointments. Furthermore, the notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is feeling stressed, the notification unit will only notify about important appointments and postpone other notifications. The notification unit can also learn the user's past behavior patterns and suggest the optimal notification timing. This allows users to manage their schedules without stress. In addition, the notification unit can work with voice assistants to provide reminder notifications by voice. For example, it can use a smart speaker to provide voice reminders for appointments, allowing users to check their schedules hands-free. This enables the notification unit to provide users with quick and reliable reminder notifications, preventing them from missing appointments.

[0034] The camera unit can take pictures of notices and screenshots using a smartphone. For example, the camera unit can take a picture of a notice using the smartphone's camera and acquire the image. The camera unit can also take a screenshot using the smartphone's camera and acquire the image. Furthermore, the camera unit can take pictures of notices and screenshots using the smartphone's camera and acquire the images. This makes it easy to take pictures of notices and screenshots using a smartphone. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input image data acquired with the smartphone's camera into a generating AI and have the generating AI perform analysis of the image data.

[0035] The character recognition unit can analyze captured images and recognize characters. For example, the character recognition unit can recognize characters in an image using OCR (Optical Character Recognition) technology. Furthermore, the character recognition unit can recognize handwritten characters using machine learning algorithms. In addition, the character recognition unit can distinguish between different fonts and handwritten characters using deep learning technology. For example, the character recognition unit can recognize printed characters using OCR technology. Furthermore, the character recognition unit can recognize handwritten characters using machine learning algorithms. Furthermore, the character recognition unit can distinguish between different fonts and handwritten characters using deep learning technology. This allows for accurate recognition of characters from captured images. Some or all of the above-described processes in the character recognition unit may be performed using AI, for example, or without AI. For example, the character recognition unit can input captured image data into a generating AI and have the generating AI perform character recognition.

[0036] The reflection unit can automatically reflect recognized text information into the schedule management app. For example, the reflection unit can convert recognized text information into a specific format and input it into the schedule management app. The reflection unit can also automatically reflect recognized text information into the schedule management app. Furthermore, the reflection unit has a function to detect and adjust schedule overlaps. For example, the reflection unit can convert recognized text information into a specific format and input it into the schedule management app. Furthermore, the reflection unit can also automatically reflect recognized text information into the schedule management app. Furthermore, the reflection unit has a function to detect and adjust schedule overlaps. This eliminates the need for manual input by automatically reflecting recognized text information into the schedule management app. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input recognized text information into a generation AI and have the generation AI perform the reflection into the schedule management app.

[0037] The notification unit can send reminder notifications as an event approaches. The notification unit can send reminder notifications using methods such as push notifications or email notifications. Furthermore, the notification unit can customize the timing and content of notifications based on user settings. In addition, the notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, the notification unit can send reminder notifications using push notifications. The notification unit can also send reminder notifications using email notifications. Furthermore, the notification unit can customize the timing and content of notifications based on user settings. This helps prevent missed appointments by sending reminder notifications as events approach. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the timing of sending reminder notifications into a generating AI and have the generating AI execute the sending of notifications.

[0038] The shooting unit can automatically adjust the lighting and angle to obtain the optimal image. For example, the shooting unit's AI can detect the ambient light level and automatically adjust the optimal exposure setting. The shooting unit's AI can also detect the image angle and automatically adjust the optimal shooting angle. Furthermore, the shooting unit's AI can detect the subject's position and automatically adjust the optimal focus. For example, the shooting unit's AI can detect the ambient light level and automatically adjust the optimal exposure setting. The shooting unit's AI can also detect the image angle and automatically adjust the optimal shooting angle. Furthermore, the shooting unit's AI can detect the subject's position and automatically adjust the optimal focus. This allows for the acquisition of the optimal image by automatically adjusting the lighting and angle. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input ambient light data into a generating AI and have the generating AI perform the exposure setting adjustment.

[0039] The camera unit can capture multiple images in sequence and select the sharpest one. For example, the camera unit can use AI to perform continuous shooting and automatically select the sharpest image. The camera unit can also use AI to perform continuous shooting and automatically select the image with the least blur. Furthermore, the camera unit can use AI to perform continuous shooting and automatically select the image with the optimal color balance. This allows for the selection of the sharpest image by capturing multiple images in sequence. Some or all of the above processing in the camera unit may be performed using AI, or without AI. For example, the camera unit can input the continuously captured image data into a generating AI and have the generating AI select the sharpest image.

[0040] The camera unit can prioritize capturing highly relevant notifications based on the user's location information. For example, if the user is in a specific location, the camera unit will prioritize capturing notifications related to that location. Furthermore, if the user is on the move, the camera unit can prioritize capturing notifications related to their current location. Additionally, if the user is participating in a specific event, the camera unit can prioritize capturing notifications related to that event. This ensures that important information is not missed by prioritizing the capture of highly relevant notifications based on the user's location information. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the user's location data into a generating AI and have the generating AI select highly relevant notifications.

[0041] The shooting unit can analyze the user's past shooting history and suggest the optimal shooting method. For example, the shooting unit can suggest the optimal shooting method based on images the user has taken in the past. Furthermore, the shooting unit can suggest the optimal shooting settings based on the user's past shooting history. In addition, the shooting unit can analyze the user's past shooting history and suggest the optimal shooting timing. This allows the shooting unit to suggest the optimal shooting method by analyzing the user's past shooting history. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input the user's past shooting history data into a generating AI and have the generating AI suggest the optimal shooting method.

[0042] The character recognition unit can apply algorithms to distinguish between different fonts and handwritten characters. For example, the AI ​​can distinguish between different fonts and perform accurate character recognition. The character recognition unit can also have the AI ​​distinguish between handwritten characters and perform accurate character recognition. Furthermore, the AI ​​can simultaneously distinguish between different fonts and handwritten characters and perform accurate character recognition. This improves the accuracy of character recognition by distinguishing between different fonts and handwritten characters. Some or all of the above-described processes in the character recognition unit may be performed using AI, for example, or without AI. For example, the character recognition unit can input data of different fonts and handwritten characters into a generating AI and have the generating AI perform the identification.

[0043] The character recognition unit can improve recognition accuracy by removing noise from images. For example, the character recognition unit can use AI to detect and remove noise from images to improve character recognition accuracy. The character recognition unit can also use AI to detect the background of an image and remove noise to improve character recognition accuracy. Furthermore, the character recognition unit can use AI to adjust the contrast of an image and remove noise to improve character recognition accuracy. For example, the character recognition unit can use AI to detect and remove noise from images to improve character recognition accuracy. Furthermore, the character recognition unit can use AI to detect the background of an image and remove noise to improve character recognition accuracy. Furthermore, the character recognition unit can use AI to adjust the contrast of an image and remove noise to improve character recognition accuracy. In this way, removing noise from images improves character recognition accuracy. Some or all of the above processing in the character recognition unit may be performed using AI, for example, or without AI. For example, the character recognition unit can input image noise data into a generating AI and have the generating AI perform noise reduction.

[0044] The character recognition unit can be equipped with the ability to recognize multiple languages ​​simultaneously. For example, the AI ​​can simultaneously recognize multiple languages ​​and perform accurate character recognition. Furthermore, the AI ​​can identify characters from different languages ​​and perform accurate character recognition. Additionally, the AI ​​can simultaneously recognize multiple languages ​​and provide a translation function. This enables multilingual character recognition by recognizing multiple languages ​​simultaneously. Some or all of the above-described processes in the character recognition unit may be performed using AI, or without AI. For example, the character recognition unit can input multiple language data into a generating AI and have the generating AI perform the recognition.

[0045] The character recognition unit can also recognize shapes and icons within an image and supplement the information. For example, the character recognition unit can use AI to identify shapes within an image and supplement the character recognition information. The character recognition unit can also use AI to identify icons within an image and supplement the character recognition information. Furthermore, the character recognition unit can use AI to simultaneously identify shapes and icons within an image and supplement the character recognition information. For example, the character recognition unit can use AI to identify shapes within an image and supplement the character recognition information. Furthermore, the character recognition unit can use AI to identify icons within an image and supplement the character recognition information. Furthermore, the character recognition unit can use AI to simultaneously identify shapes and icons within an image and supplement the character recognition information. In this way, character recognition information can be supplemented by recognizing shapes and icons within an image. Some or all of the above processing in the character recognition unit may be performed using AI, for example, or without AI. For example, the character recognition unit can input data of shapes and icons within an image into a generating AI and have the generating AI perform the recognition.

[0046] The reflection unit can be equipped with a function to automatically detect and adjust schedule overlaps. For example, the reflection unit can use AI to detect schedule overlaps and automatically adjust them. The reflection unit can also use AI to detect schedule overlaps and notify the user to prompt adjustment. Furthermore, the reflection unit can use AI to detect schedule overlaps and suggest an optimal schedule. For example, the reflection unit can use AI to detect schedule overlaps and automatically adjust them. Furthermore, the reflection unit can use AI to detect schedule overlaps and notify the user to prompt adjustment. Furthermore, the reflection unit can use AI to detect schedule overlaps and suggest an optimal schedule. This makes schedule management more efficient by automatically detecting and adjusting schedule overlaps. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input schedule data into a generating AI and have the generating AI perform the detection and adjustment of overlaps.

[0047] The display unit can show schedules in different colors and icons depending on their category. For example, the display unit can use AI to identify schedule categories and display them in different colors. Alternatively, the display unit can use AI to identify schedule categories and display them in different icons. Furthermore, the display unit can use AI to identify schedule categories and display them in a combination of colors and icons. This improves visibility by displaying schedules in different colors and icons depending on their category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input schedule category data into a generating AI and have the generating AI determine the display method.

[0048] The synchronization unit can add functionality to synchronize with the user's other calendar apps. For example, the synchronization unit can use AI to synchronize with other calendar apps and automatically reflect schedules. The synchronization unit can also use AI to synchronize with other calendar apps and prevent schedule overlaps. Furthermore, the synchronization unit can use AI to synchronize with other calendar apps and suggest an optimal schedule. For example, the synchronization unit can use AI to synchronize with other calendar apps and automatically reflect schedules. Furthermore, the synchronization unit can use AI to synchronize with other calendar apps and prevent schedule overlaps. Furthermore, the synchronization unit can use AI to synchronize with other calendar apps and suggest an optimal schedule. This enables centralized schedule management by synchronizing with other calendar apps. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input data from other calendar apps into a generating AI and have the generating AI perform the synchronization.

[0049] The reflection unit can analyze the user's past schedule history and propose the optimal schedule. For example, the reflection unit can use AI to analyze past schedule history and propose the optimal schedule. The reflection unit can also use AI to analyze past schedule history and propose a schedule that avoids duplication. Furthermore, the reflection unit can use AI to analyze past schedule history and propose an efficient schedule. For example, the reflection unit can use AI to analyze past schedule history and propose the optimal schedule. Furthermore, the reflection unit can use AI to analyze past schedule history and propose a schedule that avoids duplication. Furthermore, the reflection unit can use AI to analyze past schedule history and propose an efficient schedule. This makes it possible to propose an efficient schedule by analyzing past schedule history. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input past schedule history data into a generating AI and have the generating AI execute an optimal schedule proposal.

[0050] The notification unit can add a function to customize notification content according to the user's preferences. For example, the notification unit can use AI to analyze the user's past notification history and provide notification content tailored to their preferences. The notification unit can also use AI to customize notification content based on the user's settings. Furthermore, the notification unit can use AI to analyze the user's behavior patterns and provide optimal notification content. For example, the notification unit can use AI to analyze the user's past notification history and provide notification content tailored to their preferences. Furthermore, the notification unit can use AI to customize notification content based on the user's settings. Furthermore, the notification unit can use AI to analyze the user's behavior patterns and provide optimal notification content. This allows the notification unit to provide the most suitable notifications for the user by customizing notification content according to their preferences. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's notification history data into a generating AI and have the generating AI perform the customization of notification content.

[0051] The notification unit can allow users to select from multiple notification methods. For example, the notification unit can use AI to select the optimal notification method based on the user's settings. Alternatively, the notification unit can use AI to analyze the user's past notification history and suggest the optimal notification method. Furthermore, the notification unit can use AI to analyze the user's behavior patterns and provide the optimal notification method. This allows for the provision of the most suitable notification method for each user situation by offering multiple options. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input the user's notification history data into a generating AI and have the generating AI select the notification method.

[0052] The notification unit can select the optimal notification method based on the user's location information. For example, if the user is in a specific location, the notification unit can select a notification method appropriate for that location. Furthermore, if the user is on the move, the notification unit can select a notification method appropriate for the current location. In addition, if the user is participating in a specific event, the notification unit can select a notification method appropriate for that event. This allows for the provision of appropriate notifications by selecting the optimal notification method based on the user's location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's location data into a generating AI and have the generating AI select the notification method.

[0053] The notification unit can analyze the user's past notification history and propose the optimal notification timing. For example, the notification unit can use AI to analyze the user's past notification history and propose the optimal notification timing. The notification unit can also use AI to analyze the user's behavior patterns and provide the optimal notification timing. Furthermore, the notification unit can use AI to propose the optimal notification timing based on the user's settings. For example, the notification unit can use AI to analyze the user's past notification history and propose the optimal notification timing. Furthermore, the notification unit can use AI to analyze the user's behavior patterns and provide the optimal notification timing. Furthermore, the notification unit can use AI to propose the optimal notification timing based on the user's settings. This allows the notification unit to propose the optimal notification timing by analyzing past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's notification history data into a generating AI and have the generating AI execute the notification timing proposal.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The AI ​​scheduler can prioritize displaying highly relevant notifications based on the user's location. For example, if the user is in a specific location, notifications related to that location will be prioritized. Similarly, if the user is on the move, notifications related to their current location will be prioritized. Furthermore, if the user is participating in a specific event, notifications related to that event will be prioritized. This ensures that important information is not missed by prioritizing highly relevant notifications based on the user's location.

[0056] AI scheduling can analyze a user's past scheduling history and provide optimal schedule suggestions. For example, it can suggest efficient schedules based on past scheduling history. It can also analyze past scheduling history and suggest schedules that avoid duplication. Furthermore, it can suggest schedules tailored to the user's preferences based on past scheduling history. In this way, by analyzing past scheduling history, it becomes possible to provide efficient and user-optimized schedule suggestions.

[0057] AI scheduling can be enhanced with the ability to synchronize with other calendar apps. For example, it can synchronize with other calendar apps to automatically reflect schedules. It can also synchronize with other calendar apps to prevent schedule overlaps. Furthermore, it can synchronize with other calendar apps to suggest the optimal schedule. This allows for centralized schedule management by synchronizing with other calendar apps.

[0058] The AI ​​scheduling system can analyze a user's past notification history and suggest the optimal notification timing. For example, it can suggest the optimal timing based on past notification history. It can also analyze user behavior patterns and provide optimal notification timing. Furthermore, it can suggest the optimal timing based on user settings. In this way, by analyzing past notification history, it can suggest the optimal notification timing.

[0059] The AI ​​scheduling system can select the optimal notification method based on the user's location. For example, if the user is in a specific location, it can select a notification method appropriate for that location. It can also select a notification method appropriate for the user's current location if the user is on the move. Furthermore, if the user is participating in a specific event, it can select a notification method appropriate for that event. This allows for the provision of appropriate notifications by selecting the optimal notification method based on the user's location.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The camera unit takes a picture of the notice or screenshot. For example, it can be taken using a smartphone camera, digital camera, or tablet camera. Furthermore, the camera unit automatically adjusts the image resolution and focus to obtain the optimal image. Step 2: The character recognition unit analyzes the image captured by the image capture unit and recognizes the characters. For example, it can recognize printed characters and handwritten characters using OCR (optical character recognition) technology, machine learning algorithms, and deep learning technology. Step 3: The reflection unit reflects the character information recognized by the character recognition unit into the schedule management app. For example, it has a function to convert the recognized character information into a specific format and input it into the schedule management app, and to detect and adjust schedule overlaps. Step 4: The notification unit sends a reminder notification when the scheduled event reflected by the reflection unit approaches. For example, reminder notifications can be sent using push notifications or email notifications, and the timing and content of the notifications can be customized based on the user's settings.

[0062] (Example of form 2) The AI ​​schedule system according to an embodiment of the present invention is a system for efficiently managing schedules for notices from nurseries and elementary schools. The AI ​​schedule system allows users to take a picture of a notice or screenshot with their smartphone, and the AI ​​analyzes the image, recognizes the text, and automatically reflects it in a schedule management app. Furthermore, the AI ​​sends reminder notifications as the scheduled event approaches, preventing users from missing important appointments. This allows users to efficiently manage their schedules without forgetting important appointments. For example, the AI ​​schedule system is an extremely convenient tool for parents with children and people with busy lifestyles. For example, when a user takes a picture of a notice or screenshot with their smartphone, the AI ​​analyzes the image, recognizes the text, and automatically reflects it in a schedule management app. Furthermore, the AI ​​sends reminder notifications as the scheduled event approaches, preventing users from missing important appointments. This allows users to efficiently manage their schedules without forgetting important appointments. For example, the AI ​​schedule system is an extremely convenient tool for parents with children and people with busy lifestyles. This allows users to efficiently manage their schedules without forgetting important appointments.

[0063] The AI ​​schedule according to this embodiment comprises a shooting unit, a character recognition unit, a reflection unit, and a notification unit. The shooting unit takes pictures of notices or screenshots. The shooting unit can, for example, take pictures of notices or screenshots using a smartphone camera. It can also take pictures using a digital camera or a tablet camera. Furthermore, the shooting unit can automatically adjust the image resolution and focus to obtain the optimal image. For example, the shooting unit can take a picture of a notice using a smartphone camera and obtain the image. It can also take a screenshot using a digital camera and obtain the image. Furthermore, the shooting unit can take a picture of a notice using a tablet camera and obtain the image. The character recognition unit analyzes the image taken by the shooting unit and recognizes the characters. The character recognition unit can, for example, recognize characters in an image using OCR (optical character recognition) technology. It can also recognize handwritten characters using machine learning algorithms. Furthermore, the character recognition unit can distinguish between different fonts and handwritten characters using deep learning technology. For example, the character recognition unit can recognize printed characters using OCR technology. Furthermore, the character recognition unit can recognize handwritten characters using machine learning algorithms. In addition, the character recognition unit can distinguish between different fonts and handwritten characters using deep learning technology. The reflection unit reflects the character information recognized by the character recognition unit into the schedule management app. For example, the reflection unit can convert the recognized character information into a specific format and input it into the schedule management app. The reflection unit can also automatically reflect the recognized character information into the schedule management app. Furthermore, the reflection unit has a function to detect and adjust schedule overlaps. For example, the reflection unit converts the recognized character information into a specific format and inputs it into the schedule management app. The reflection unit can also automatically reflect the recognized character information into the schedule management app. Furthermore, the reflection unit has a function to detect and adjust schedule overlaps. The notification unit sends a reminder notification when an appointment reflected by the reflection unit is approaching.The notification unit can send reminder notifications using methods such as push notifications or email notifications. Furthermore, the notification unit can customize the timing and content of notifications based on user settings. In addition, the notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, the notification unit sends reminder notifications using push notifications. It can also send reminder notifications using email notifications. Furthermore, the notification unit can customize the timing and content of notifications based on user settings. As a result, the AI ​​schedule according to this embodiment can efficiently reflect notices and screenshots in the schedule management app and send reminder notifications, preventing users from missing appointments.

[0064] The camera unit takes pictures of notices and screenshots. For example, the camera unit can use a smartphone camera to take pictures of notices and screenshots. It can also use a digital camera or tablet camera. Furthermore, the camera unit can automatically adjust image resolution and focus to obtain the optimal image. Specifically, when using a smartphone camera, the camera app automatically recognizes the subject and sets the optimal exposure and white balance. This allows the photographer to obtain high-quality images without requiring any special operation. When using a digital camera, the camera's autofocus function focuses on the subject, and the image stabilization function reduces blur during shooting. Similarly, when using a tablet camera, the large screen of the tablet can be used to check the subject while shooting. Furthermore, the camera unit has a function to take multiple images in succession and select the clearest image. This makes it possible to obtain high-quality images even with moving subjects or in low-light environments. The camera unit immediately sends the acquired image to the character recognition unit and proceeds to the next processing step.

[0065] The character recognition unit analyzes images captured by the image capture unit and recognizes characters. For example, the character recognition unit can recognize characters within an image using OCR (Optical Character Recognition) technology. It can also recognize handwritten characters using machine learning algorithms. Furthermore, it can distinguish between different fonts and handwritten characters using deep learning technology. Specifically, OCR technology detects character regions within an image and converts each character into digital data. Machine learning algorithms recognize handwritten characters with high accuracy based on a pre-trained dataset of handwritten characters. Deep learning technology uses neural networks to learn the characteristics of different fonts and handwritten characters, enabling the identification of more complex character patterns. For example, the character recognition unit can recognize printed characters using OCR technology and handwritten characters using machine learning algorithms. It can also distinguish between different fonts and handwritten characters using deep learning technology. As a result, the character recognition unit converts the character information in the captured image into digital data with high accuracy and proceeds to the next processing step. Furthermore, the character recognition unit is equipped with contextual analysis and spell-checking functions to improve the accuracy of the recognition results. This reduces misrecognition and allows for the provision of more accurate textual information.

[0066] The reflection unit reflects the character information recognized by the character recognition unit into the schedule management app. For example, the reflection unit can convert the recognized character information into a specific format and input it into the schedule management app. Furthermore, the reflection unit can automatically reflect the recognized character information into the schedule management app. Specifically, the reflection unit divides the recognized character information into items such as date, time, location, and content, and inputs them into the corresponding fields in the schedule management app. In addition, the reflection unit has a function to detect and adjust schedule overlaps. For example, if multiple appointments are entered for the same date and time, the reflection unit notifies the user and prompts them to make adjustments. The reflection unit can also set schedule priorities and display important appointments at the top. This allows users to manage their schedules efficiently. Moreover, the reflection unit has a function to link the schedule management app with other calendar and task management apps, enabling consistent schedule management across multiple platforms. For example, the reflection unit can synchronize with external calendars and automatically update schedule information. This allows users to centrally manage their schedules across multiple devices and applications.

[0067] The notification unit sends reminder notifications as appointments reflected by the reflection unit approach. The notification unit can send reminder notifications using methods such as push notifications or email notifications. The notification unit can also customize the timing and content of notifications based on user settings. Specifically, the notification unit can send reminder notifications based on user-set times and can send multiple notifications for important appointments. Furthermore, the notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is feeling stressed, the notification unit will only notify about important appointments and postpone other notifications. The notification unit can also learn the user's past behavior patterns and suggest the optimal notification timing. This allows users to manage their schedules without stress. In addition, the notification unit can work with voice assistants to provide reminder notifications by voice. For example, it can use a smart speaker to provide voice reminders for appointments, allowing users to check their schedules hands-free. This enables the notification unit to provide users with quick and reliable reminder notifications, preventing them from missing appointments.

[0068] The camera unit can take pictures of notices and screenshots using a smartphone. For example, the camera unit can take a picture of a notice using the smartphone's camera and acquire the image. The camera unit can also take a screenshot using the smartphone's camera and acquire the image. Furthermore, the camera unit can take pictures of notices and screenshots using the smartphone's camera and acquire the images. This makes it easy to take pictures of notices and screenshots using a smartphone. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input image data acquired with the smartphone's camera into a generating AI and have the generating AI perform analysis of the image data.

[0069] The character recognition unit can analyze captured images and recognize characters. For example, the character recognition unit can recognize characters in an image using OCR (Optical Character Recognition) technology. Furthermore, the character recognition unit can recognize handwritten characters using machine learning algorithms. In addition, the character recognition unit can distinguish between different fonts and handwritten characters using deep learning technology. For example, the character recognition unit can recognize printed characters using OCR technology. Furthermore, the character recognition unit can recognize handwritten characters using machine learning algorithms. Furthermore, the character recognition unit can distinguish between different fonts and handwritten characters using deep learning technology. This allows for accurate recognition of characters from captured images. Some or all of the above-described processes in the character recognition unit may be performed using AI, for example, or without AI. For example, the character recognition unit can input captured image data into a generating AI and have the generating AI perform character recognition.

[0070] The reflection unit can automatically reflect recognized text information into the schedule management app. For example, the reflection unit can convert recognized text information into a specific format and input it into the schedule management app. The reflection unit can also automatically reflect recognized text information into the schedule management app. Furthermore, the reflection unit has a function to detect and adjust schedule overlaps. For example, the reflection unit can convert recognized text information into a specific format and input it into the schedule management app. Furthermore, the reflection unit can also automatically reflect recognized text information into the schedule management app. Furthermore, the reflection unit has a function to detect and adjust schedule overlaps. This eliminates the need for manual input by automatically reflecting recognized text information into the schedule management app. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input recognized text information into a generation AI and have the generation AI perform the reflection into the schedule management app.

[0071] The notification unit can send reminder notifications as an event approaches. The notification unit can send reminder notifications using methods such as push notifications or email notifications. Furthermore, the notification unit can customize the timing and content of notifications based on user settings. In addition, the notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, the notification unit can send reminder notifications using push notifications. The notification unit can also send reminder notifications using email notifications. Furthermore, the notification unit can customize the timing and content of notifications based on user settings. This helps prevent missed appointments by sending reminder notifications as events approach. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the timing of sending reminder notifications into a generating AI and have the generating AI execute the sending of notifications.

[0072] The camera unit can estimate the user's emotions and adjust the shooting timing based on the estimated emotions. For example, if the user is stressed, the AI ​​will automatically take a picture at the optimal time. The camera unit can also take pictures according to the user's instructions if the user is relaxed. Furthermore, if the user is in a hurry, the AI ​​can take a picture quickly. For example, if the user is stressed, the AI ​​will automatically take a picture at the optimal time. The camera unit can also take pictures according to the user's instructions if the user is relaxed. Furthermore, if the user is in a hurry, the AI ​​can take a picture quickly. This allows for shooting at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the shooting unit can input user emotion data into a generating AI and have the AI ​​adjust the shooting timing.

[0073] The shooting unit can automatically adjust the lighting and angle to obtain the optimal image. For example, the shooting unit's AI can detect the ambient light level and automatically adjust the optimal exposure setting. The shooting unit's AI can also detect the image angle and automatically adjust the optimal shooting angle. Furthermore, the shooting unit's AI can detect the subject's position and automatically adjust the optimal focus. For example, the shooting unit's AI can detect the ambient light level and automatically adjust the optimal exposure setting. The shooting unit's AI can also detect the image angle and automatically adjust the optimal shooting angle. Furthermore, the shooting unit's AI can detect the subject's position and automatically adjust the optimal focus. This allows for the acquisition of the optimal image by automatically adjusting the lighting and angle. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input ambient light data into a generating AI and have the generating AI perform the exposure setting adjustment.

[0074] The camera unit can capture multiple images in sequence and select the sharpest one. For example, the camera unit can use AI to perform continuous shooting and automatically select the sharpest image. The camera unit can also use AI to perform continuous shooting and automatically select the image with the least blur. Furthermore, the camera unit can use AI to perform continuous shooting and automatically select the image with the optimal color balance. This allows for the selection of the sharpest image by capturing multiple images in sequence. Some or all of the above processing in the camera unit may be performed using AI, or without AI. For example, the camera unit can input the continuously captured image data into a generating AI and have the generating AI select the sharpest image.

[0075] The camera unit can estimate the user's emotions and determine the priority of images to capture based on the estimated emotions. For example, if the user is stressed, the camera unit will prioritize capturing important information. If the user is relaxed, the camera unit can capture all information equally. Furthermore, if the user is in a hurry, the camera unit can prioritize capturing the most important information. This allows for the prioritization of important information by determining the priority of images to capture according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input user emotion data into a generating AI, which can then determine the priority of the images to be captured.

[0076] The camera unit can prioritize capturing highly relevant notifications based on the user's location information. For example, if the user is in a specific location, the camera unit will prioritize capturing notifications related to that location. Furthermore, if the user is on the move, the camera unit can prioritize capturing notifications related to their current location. Additionally, if the user is participating in a specific event, the camera unit can prioritize capturing notifications related to that event. This ensures that important information is not missed by prioritizing the capture of highly relevant notifications based on the user's location information. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the user's location data into a generating AI and have the generating AI select highly relevant notifications.

[0077] The shooting unit can analyze the user's past shooting history and suggest the optimal shooting method. For example, the shooting unit can suggest the optimal shooting method based on images the user has taken in the past. Furthermore, the shooting unit can suggest the optimal shooting settings based on the user's past shooting history. In addition, the shooting unit can analyze the user's past shooting history and suggest the optimal shooting timing. This allows the shooting unit to suggest the optimal shooting method by analyzing the user's past shooting history. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input the user's past shooting history data into a generating AI and have the generating AI suggest the optimal shooting method.

[0078] The character recognition unit can estimate the user's emotions and adjust the accuracy of character recognition based on the estimated emotions. For example, if the user is stressed, the AI ​​will improve the accuracy of character recognition. The AI ​​can also maintain normal accuracy when the user is relaxed. Furthermore, if the user is in a hurry, the AI ​​can quickly adjust the accuracy of character recognition. This allows for more accurate character recognition by adjusting the accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the character recognition unit may be performed using AI, for example, or without AI. For example, the character recognition unit can input user emotion data into a generating AI and have the generating AI perform adjustments to the accuracy of character recognition.

[0079] The character recognition unit can apply algorithms to distinguish between different fonts and handwritten characters. For example, the AI ​​can distinguish between different fonts and perform accurate character recognition. The character recognition unit can also have the AI ​​distinguish between handwritten characters and perform accurate character recognition. Furthermore, the AI ​​can simultaneously distinguish between different fonts and handwritten characters and perform accurate character recognition. This improves the accuracy of character recognition by distinguishing between different fonts and handwritten characters. Some or all of the above-described processes in the character recognition unit may be performed using AI, for example, or without AI. For example, the character recognition unit can input data of different fonts and handwritten characters into a generating AI and have the generating AI perform the identification.

[0080] The character recognition unit can improve recognition accuracy by removing noise from images. For example, the character recognition unit can use AI to detect and remove noise from images to improve character recognition accuracy. The character recognition unit can also use AI to detect the background of an image and remove noise to improve character recognition accuracy. Furthermore, the character recognition unit can use AI to adjust the contrast of an image and remove noise to improve character recognition accuracy. For example, the character recognition unit can use AI to detect and remove noise from images to improve character recognition accuracy. Furthermore, the character recognition unit can use AI to detect the background of an image and remove noise to improve character recognition accuracy. Furthermore, the character recognition unit can use AI to adjust the contrast of an image and remove noise to improve character recognition accuracy. In this way, removing noise from images improves character recognition accuracy. Some or all of the above processing in the character recognition unit may be performed using AI, for example, or without AI. For example, the character recognition unit can input image noise data into a generating AI and have the generating AI perform noise reduction.

[0081] The character recognition unit can estimate the user's emotions and determine the priority of characters to recognize based on the estimated emotions. For example, if the user is stressed, the character recognition unit will prioritize recognizing important characters. If the user is relaxed, the character recognition unit can recognize all characters equally. Furthermore, if the user is in a hurry, the character recognition unit can prioritize recognizing the most important characters. For example, if the user is stressed, the character recognition unit will prioritize recognizing important characters. If the user is relaxed, the character recognition unit can recognize all characters equally. Furthermore, if the user is in a hurry, the character recognition unit can prioritize recognizing the most important characters. This allows for the priority of recognizing important characters by determining the priority of characters to recognize according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the character recognition unit may be performed using AI, for example, or without AI. For example, the character recognition unit can input user emotion data into the generating AI, which can then determine the priority order of characters to recognize.

[0082] The character recognition unit can be equipped with the ability to recognize multiple languages ​​simultaneously. For example, the AI ​​can simultaneously recognize multiple languages ​​and perform accurate character recognition. Furthermore, the AI ​​can identify characters from different languages ​​and perform accurate character recognition. Additionally, the AI ​​can simultaneously recognize multiple languages ​​and provide a translation function. This enables multilingual character recognition by recognizing multiple languages ​​simultaneously. Some or all of the above-described processes in the character recognition unit may be performed using AI, or without AI. For example, the character recognition unit can input multiple language data into a generating AI and have the generating AI perform the recognition.

[0083] The character recognition unit can also recognize shapes and icons within an image and supplement the information. For example, the character recognition unit can use AI to identify shapes within an image and supplement the character recognition information. The character recognition unit can also use AI to identify icons within an image and supplement the character recognition information. Furthermore, the character recognition unit can use AI to simultaneously identify shapes and icons within an image and supplement the character recognition information. For example, the character recognition unit can use AI to identify shapes within an image and supplement the character recognition information. Furthermore, the character recognition unit can use AI to identify icons within an image and supplement the character recognition information. Furthermore, the character recognition unit can use AI to simultaneously identify shapes and icons within an image and supplement the character recognition information. In this way, character recognition information can be supplemented by recognizing shapes and icons within an image. Some or all of the above processing in the character recognition unit may be performed using AI, for example, or without AI. For example, the character recognition unit can input data of shapes and icons within an image into a generating AI and have the generating AI perform the recognition.

[0084] The reflection unit can estimate the user's emotions and adjust the priority of the schedule to be reflected based on the estimated emotions. For example, if the user is stressed, the reflection unit will prioritize reflecting important schedules. Also, if the user is relaxed, the reflection unit can reflect all schedules equally. Furthermore, if the user is in a hurry, the reflection unit can prioritize reflecting the most important schedules. For example, if the user is stressed, the reflection unit will prioritize reflecting important schedules. Also, if the user is relaxed, the reflection unit can reflect all schedules equally. Furthermore, if the user is in a hurry, the reflection unit can prioritize reflecting the most important schedules. This allows important schedules to be prioritized by adjusting the schedule priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input user emotion data into the generating AI and have the generating AI adjust the schedule priorities.

[0085] The reflection unit can be equipped with a function to automatically detect and adjust schedule overlaps. For example, the reflection unit can use AI to detect schedule overlaps and automatically adjust them. The reflection unit can also use AI to detect schedule overlaps and notify the user to prompt adjustment. Furthermore, the reflection unit can use AI to detect schedule overlaps and suggest an optimal schedule. For example, the reflection unit can use AI to detect schedule overlaps and automatically adjust them. Furthermore, the reflection unit can use AI to detect schedule overlaps and notify the user to prompt adjustment. Furthermore, the reflection unit can use AI to detect schedule overlaps and suggest an optimal schedule. This makes schedule management more efficient by automatically detecting and adjusting schedule overlaps. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input schedule data into a generating AI and have the generating AI perform the detection and adjustment of overlaps.

[0086] The display unit can show schedules in different colors and icons depending on their category. For example, the display unit can use AI to identify schedule categories and display them in different colors. Alternatively, the display unit can use AI to identify schedule categories and display them in different icons. Furthermore, the display unit can use AI to identify schedule categories and display them in a combination of colors and icons. This improves visibility by displaying schedules in different colors and icons depending on their category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input schedule category data into a generating AI and have the generating AI determine the display method.

[0087] The reflection unit can estimate the user's emotions and adjust the display method of the schedule based on the estimated user emotions. For example, if the user is stressed, the reflection unit can provide a simple and highly visible display method. Also, if the user is relaxed, the reflection unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the reflection unit can provide a concise display method. For example, if the user is stressed, the reflection unit can provide a simple and highly visible display method. Also, if the user is relaxed, the reflection unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the reflection unit can provide a concise display method. This improves visibility by adjusting the schedule display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input user emotion data into the generating AI and have the generating AI adjust the display method.

[0088] The synchronization unit can add functionality to synchronize with the user's other calendar apps. For example, the synchronization unit can use AI to synchronize with other calendar apps and automatically reflect schedules. The synchronization unit can also use AI to synchronize with other calendar apps and prevent schedule overlaps. Furthermore, the synchronization unit can use AI to synchronize with other calendar apps and suggest an optimal schedule. For example, the synchronization unit can use AI to synchronize with other calendar apps and automatically reflect schedules. Furthermore, the synchronization unit can use AI to synchronize with other calendar apps and prevent schedule overlaps. Furthermore, the synchronization unit can use AI to synchronize with other calendar apps and suggest an optimal schedule. This enables centralized schedule management by synchronizing with other calendar apps. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input data from other calendar apps into a generating AI and have the generating AI perform the synchronization.

[0089] The reflection unit can analyze the user's past schedule history and propose the optimal schedule. For example, the reflection unit can use AI to analyze past schedule history and propose the optimal schedule. The reflection unit can also use AI to analyze past schedule history and propose a schedule that avoids duplication. Furthermore, the reflection unit can use AI to analyze past schedule history and propose an efficient schedule. For example, the reflection unit can use AI to analyze past schedule history and propose the optimal schedule. Furthermore, the reflection unit can use AI to analyze past schedule history and propose a schedule that avoids duplication. Furthermore, the reflection unit can use AI to analyze past schedule history and propose an efficient schedule. This makes it possible to propose an efficient schedule by analyzing past schedule history. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input past schedule history data into a generating AI and have the generating AI execute an optimal schedule proposal.

[0090] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize sending important notifications. If the user is relaxed, the notification unit can also send all notifications equally. Furthermore, if the user is in a hurry, the notification unit can prioritize sending the most important notifications. For example, if the user is stressed, the notification unit will prioritize sending important notifications. If the user is relaxed, the notification unit can also send all notifications equally. Furthermore, if the user is in a hurry, the notification unit can also prioritize sending the most important notifications. This allows important notifications to be sent at the appropriate time by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the AI ​​adjust the timing of notifications.

[0091] The notification unit can add a function to customize notification content according to the user's preferences. For example, the notification unit can use AI to analyze the user's past notification history and provide notification content tailored to their preferences. The notification unit can also use AI to customize notification content based on the user's settings. Furthermore, the notification unit can use AI to analyze the user's behavior patterns and provide optimal notification content. For example, the notification unit can use AI to analyze the user's past notification history and provide notification content tailored to their preferences. Furthermore, the notification unit can use AI to customize notification content based on the user's settings. Furthermore, the notification unit can use AI to analyze the user's behavior patterns and provide optimal notification content. This allows the notification unit to provide the most suitable notifications for the user by customizing notification content according to their preferences. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's notification history data into a generating AI and have the generating AI perform the customization of notification content.

[0092] The notification unit can allow users to select from multiple notification methods. For example, the notification unit can use AI to select the optimal notification method based on the user's settings. Alternatively, the notification unit can use AI to analyze the user's past notification history and suggest the optimal notification method. Furthermore, the notification unit can use AI to analyze the user's behavior patterns and provide the optimal notification method. This allows for the provision of the most suitable notification method for each user situation by offering multiple options. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input the user's notification history data into a generating AI and have the generating AI select the notification method.

[0093] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize sending important notifications. If the user is relaxed, the notification unit can also send all notifications equally. Furthermore, if the user is in a hurry, the notification unit can prioritize sending the most important notifications. For example, if the user is stressed, the notification unit will prioritize sending important notifications. If the user is relaxed, the notification unit can also send all notifications equally. Furthermore, if the user is in a hurry, the notification unit can also prioritize sending the most important notifications. This allows important notifications to be sent preferentially by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the AI ​​determine the priority of notifications.

[0094] The notification unit can select the optimal notification method based on the user's location information. For example, if the user is in a specific location, the notification unit can select a notification method appropriate for that location. Furthermore, if the user is on the move, the notification unit can select a notification method appropriate for the current location. In addition, if the user is participating in a specific event, the notification unit can select a notification method appropriate for that event. This allows for the provision of appropriate notifications by selecting the optimal notification method based on the user's location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's location data into a generating AI and have the generating AI select the notification method.

[0095] The notification unit can analyze the user's past notification history and propose the optimal notification timing. For example, the notification unit can use AI to analyze the user's past notification history and propose the optimal notification timing. The notification unit can also use AI to analyze the user's behavior patterns and provide the optimal notification timing. Furthermore, the notification unit can use AI to propose the optimal notification timing based on the user's settings. For example, the notification unit can use AI to analyze the user's past notification history and propose the optimal notification timing. Furthermore, the notification unit can use AI to analyze the user's behavior patterns and provide the optimal notification timing. Furthermore, the notification unit can use AI to propose the optimal notification timing based on the user's settings. This allows the notification unit to propose the optimal notification timing by analyzing past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's notification history data into a generating AI and have the generating AI execute the notification timing proposal.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] AI scheduling can estimate a user's emotions and adjust schedule priorities based on those emotions. For example, if a user is stressed, important appointments will be displayed first. If the user is relaxed, all appointments can be displayed equally. Furthermore, if the user is in a hurry, the most important appointments can be displayed first. This allows users to manage their schedules without missing important appointments by adjusting schedule priorities according to their emotions.

[0098] The AI ​​scheduler can prioritize displaying highly relevant notifications based on the user's location. For example, if the user is in a specific location, notifications related to that location will be prioritized. Similarly, if the user is on the move, notifications related to their current location will be prioritized. Furthermore, if the user is participating in a specific event, notifications related to that event will be prioritized. This ensures that important information is not missed by prioritizing highly relevant notifications based on the user's location.

[0099] AI scheduling can analyze a user's past scheduling history and provide optimal schedule suggestions. For example, it can suggest efficient schedules based on past scheduling history. It can also analyze past scheduling history and suggest schedules that avoid duplication. Furthermore, it can suggest schedules tailored to the user's preferences based on past scheduling history. In this way, by analyzing past scheduling history, it becomes possible to provide efficient and user-optimized schedule suggestions.

[0100] The AI ​​scheduling system can estimate the user's emotions and adjust notification timing based on those emotions. For example, if the user is stressed, important notifications will be sent preferentially. Conversely, if the user is relaxed, all notifications can be sent evenly. Furthermore, if the user is in a hurry, the most important notifications can be sent preferentially. This allows important notifications to be sent at the right time by adjusting notification timing according to the user's emotions.

[0101] AI scheduling can be enhanced with the ability to synchronize with other calendar apps. For example, it can synchronize with other calendar apps to automatically reflect schedules. It can also synchronize with other calendar apps to prevent schedule overlaps. Furthermore, it can synchronize with other calendar apps to suggest the optimal schedule. This allows for centralized schedule management by synchronizing with other calendar apps.

[0102] The AI ​​scheduler can estimate the user's emotions and adjust how the schedule is displayed based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read display. If the user is relaxed, it can provide a display with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise display. By adjusting the schedule display according to the user's emotions, visibility is improved.

[0103] The AI ​​scheduling system can analyze a user's past notification history and suggest the optimal notification timing. For example, it can suggest the optimal timing based on past notification history. It can also analyze user behavior patterns and provide optimal notification timing. Furthermore, it can suggest the optimal timing based on user settings. In this way, by analyzing past notification history, it can suggest the optimal notification timing.

[0104] The AI ​​scheduling system can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is stressed, important notifications will be sent first. If the user is relaxed, all notifications can be sent evenly. Furthermore, if the user is in a hurry, the most important notifications can be sent first. This ensures that important notifications are sent first by prioritizing them according to the user's emotions.

[0105] The AI ​​scheduling system can select the optimal notification method based on the user's location. For example, if the user is in a specific location, it can select a notification method appropriate for that location. It can also select a notification method appropriate for the user's current location if the user is on the move. Furthermore, if the user is participating in a specific event, it can select a notification method appropriate for that event. This allows for the provision of appropriate notifications by selecting the optimal notification method based on the user's location.

[0106] AI scheduling can estimate a user's emotions and adjust schedule priorities based on those emotions. For example, if a user is stressed, important appointments will be displayed first. If the user is relaxed, all appointments can be displayed equally. Furthermore, if the user is in a hurry, the most important appointments can be displayed first. This allows users to manage their schedules without missing important appointments by adjusting schedule priorities according to their emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The camera unit takes a picture of the notice or screenshot. For example, it can be taken using a smartphone camera, digital camera, or tablet camera. Furthermore, the camera unit automatically adjusts the image resolution and focus to obtain the optimal image. Step 2: The character recognition unit analyzes the image captured by the image capture unit and recognizes the characters. For example, it can recognize printed characters and handwritten characters using OCR (optical character recognition) technology, machine learning algorithms, and deep learning technology. Step 3: The reflection unit reflects the character information recognized by the character recognition unit into the schedule management app. For example, it has a function to convert the recognized character information into a specific format and input it into the schedule management app, and to detect and adjust schedule overlaps. Step 4: The notification unit sends a reminder notification when the scheduled event reflected by the reflection unit approaches. For example, reminder notifications can be sent using push notifications or email notifications, and the timing and content of the notifications can be customized based on the user's settings.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the shooting unit, character recognition unit, reflection unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the smart device 14 to take pictures of notices or screenshots. The character recognition unit is implemented by the specific processing unit 290 of the data processing unit 12 and recognizes characters in the image using OCR technology or deep learning technology. The reflection unit is implemented by the specific processing unit 290 of the data processing unit 12 and reflects the recognized character information in the schedule management application. The notification unit is implemented by the control unit 46A of the smart device 14 and sends reminder notifications. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the shooting unit, character recognition unit, reflection unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the smart glasses 214 to capture notices or screenshots. The character recognition unit is implemented by the specific processing unit 290 of the data processing unit 12 and recognizes characters in the image using OCR technology or deep learning technology. The reflection unit is implemented by the specific processing unit 290 of the data processing unit 12 and reflects the recognized character information in the schedule management application. The notification unit is implemented by the control unit 46A of the smart glasses 214 and sends reminder notifications. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the image capture unit, character recognition unit, reflection unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the image capture unit uses the camera 42 of the headset terminal 314 to capture notices or screenshots. The character recognition unit is implemented by the specific processing unit 290 of the data processing unit 12 and recognizes characters in the image using OCR technology or deep learning technology. The reflection unit is implemented by the specific processing unit 290 of the data processing unit 12 and reflects the recognized character information in the schedule management application. The notification unit is implemented by the control unit 46A of the headset terminal 314 and sends reminder notifications. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the shooting unit, character recognition unit, reflection unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the robot 414 to take pictures of notices or screenshots. The character recognition unit is implemented by the specific processing unit 290 of the data processing unit 12 and recognizes characters in the image using OCR technology or deep learning technology. The reflection unit is implemented by the specific processing unit 290 of the data processing unit 12 and reflects the recognized character information in the schedule management application. The notification unit is implemented by the control unit 46A of the robot 414 and sends reminder notifications. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The photography team takes pictures of notices and screenshots, A character recognition unit analyzes the image captured by the aforementioned imaging unit and recognizes characters, A reflection unit that reflects the character information recognized by the character recognition unit into the schedule management application, The system includes a notification unit that sends a reminder notification when the schedule reflected by the reflection unit approaches. A system characterized by the following features. (Note 2) The aforementioned imaging unit is Take a picture of the notice or screenshot with your smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned character recognition unit, It analyzes captured images and recognizes text. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reflection unit is, The recognized text information is automatically reflected in the schedule management app. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Send a reminder notification as the scheduled event approaches. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned imaging unit is It estimates the user's emotions and adjusts the shooting timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging unit is It automatically adjusts the lighting and angle to obtain the optimal image. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is Multiple images are taken in succession, and the clearest image is selected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is It estimates the user's emotions and determines the priority of images to capture based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is Based on the user's location, the app prioritizes capturing highly relevant notifications. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is It analyzes the user's past shooting history and suggests the optimal shooting method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned character recognition unit, It estimates the user's emotions and adjusts the accuracy of character recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned character recognition unit, Apply algorithms to distinguish between different fonts and handwritten characters. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned character recognition unit, Remove image noise to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned character recognition unit, It estimates the user's emotions and determines the priority of characters to recognize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned character recognition unit, Add a feature to recognize multiple languages ​​simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned character recognition unit, It also recognizes shapes and icons within images to supplement the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reflection unit is, It estimates user sentiment and adjusts schedule priorities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reflection unit is, Add a feature to automatically detect and adjust schedule conflicts. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reflection unit is, Display different colors and icons depending on the schedule category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reflection unit is, We estimate the user's emotions and adjust how the schedule is displayed to reflect those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reflection unit is, Add the ability to sync with other calendar apps used by the user. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reflection unit is, We analyze the user's past schedule history and propose the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, Add a feature to customize notification content according to user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, Allow users to choose from multiple notification methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, The optimal notification method is selected based on the user's location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, We analyze the user's past notification history and suggest the optimal notification timing. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The photography team takes pictures of notices and screenshots, A character recognition unit analyzes the image captured by the aforementioned imaging unit and recognizes characters, A reflection unit that reflects the character information recognized by the character recognition unit into the schedule management application, The system includes a notification unit that sends a reminder notification when the schedule reflected by the reflection unit approaches. A system characterized by the following features.

2. The aforementioned imaging unit is Take a picture of the notice or screenshot with your smartphone. The system according to feature 1.

3. The aforementioned character recognition unit, It analyzes captured images and recognizes text. The system according to feature 1.

4. The aforementioned reflection unit is, The recognized text information is automatically reflected in the schedule management app. The system according to feature 1.

5. The aforementioned notification unit, Send a reminder notification as the scheduled event approaches. The system according to feature 1.

6. The aforementioned imaging unit is It estimates the user's emotions and adjusts the shooting timing based on the estimated user emotions. The system according to feature 1.

7. The aforementioned imaging unit is It automatically adjusts the lighting and angle to obtain the optimal image. The system according to feature 1.

8. The aforementioned imaging unit is Multiple images are taken in succession, and the clearest image is selected. The system according to feature 1.

Images