System

Abstract

To provide a system capable of automatically correcting typos and spelling errors in notes entered by users.SOLUTION: A system in an embodiment includes a reception unit, an analysis unit, and a display unit. The reception unit allows a user to input memos. The analysis unit analyzes the memos input via the reception unit and corrects a typo or omission. The display unit displays the memo corrected by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately address automatically correcting typos and errors in notes entered by users, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically correct typos and spelling errors in notes entered by a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a display unit. The reception unit allows a user to input a memo. The analysis unit analyzes the memo input by the reception unit and corrects typos or spelling errors. The display unit displays the memo corrected by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can automatically correct typos and spelling errors in notes entered by a user. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The memo pad application according to the embodiment of the present invention is a system in which a user inputs a memo, a generation AI always synchronizes the entire text, corrects typos, predicts conversion, and corrects typos. In the memo pad application, a user inputs a memo, and a generation AI always synchronizes the entire text, corrects typos, predicts conversion, and corrects typos. Furthermore, taking into consideration the ease of correction like handwritten notes, the generation AI automatically corrects them, thereby saving the user the trouble of correcting them. For example, a user inputs a memo. At this time, the user inputs the memo using a keyboard or touch screen. For example, it can be used in various scenes, such as taking minutes during a meeting or taking notes on ideas. Next, the generation AI always synchronizes the input memo and corrects typos. The generation AI analyzes the input text, detects typos and omissions, and corrects them. For example, it corrects "Good morning" to "Good morning". In addition, it uses a predictive conversion function to predict the word the user is about to input and assists input. For example, if "oha" is input, it is predicted to be converted to "Good morning". In addition, the generation AI also corrects typos. A typo is an input mistake, for example, "Good morning" is corrected to "Good morning". In this way, the notes entered by the user can always be kept accurate. In addition, the generation AI automatically corrects points that are easy to correct, such as handwritten notes. For example, if a user draws a double line to indicate a part that they want to correct, the generation AI automatically corrects that part. This saves the user the trouble of manually correcting it. This mechanism allows the user to always take accurate and easy-to-read notes. For example, when taking minutes during a meeting, you can take notes smoothly without worrying about typos or omissions. In addition, when taking notes on ideas, the generation AI automatically corrects them, so you can take notes without interrupting your thoughts. In this way, a memo pad app that utilizes the generation AI supports the user's note-taking and allows efficient and accurate notes to be taken. As a result, the memo pad application can automatically correct typos and typos when the user takes notes, providing accurate and easy-to-read notes.

[0029] The memo pad application according to the embodiment includes a reception unit, an analysis unit, and a display unit. The reception unit allows a user to input a memo. When the user inputs a memo, for example, a keyboard or a touch screen can be used. The reception unit can be used in various situations, such as taking minutes during a meeting or taking notes on an idea. The analysis unit uses a generation AI to analyze the memo input by the reception unit and corrects typos and spelling errors. The generation AI analyzes the input text and detects and corrects typos and spelling errors. For example, it corrects "Good morning" to "Good morning". The analysis unit can also perform predictive conversion using the generation AI. For example, when "Oha" is input, it is predicted to be converted to "Good morning". The analysis unit can also correct typos using the generation AI. A typo is an input error, and for example, it corrects "Good morning" to "Good morning". The display unit displays the memo corrected by the analysis unit. The display unit can display, for example, the corrected memo on the screen. This allows the user to always take accurate and easy-to-read notes. For example, when taking minutes during a meeting, the user can take notes smoothly without worrying about typos or spelling mistakes. In addition, when taking notes on ideas, the generation AI automatically corrects them, so the user can take notes without interrupting their thoughts. This allows the memo pad application according to the embodiment to automatically correct typos and spelling mistakes when the user takes notes, providing accurate and easy-to-read notes.

[0030] The reception unit provides an interface for the user to input a memo. When the user inputs a memo, the user can use a keyboard or a touch screen. Specifically, when inputting text using a keyboard, either a physical keyboard or a software keyboard can be used, and the user can select according to his / her preference. When using a touch screen, the user can input characters by tapping a virtual keyboard displayed on the screen. In addition, by providing a voice input function, it is also possible to convert what the user speaks into text. This allows the user to respond to cases where their hands are full or when they want to take notes quickly. The reception unit can be used in various scenes, such as when taking minutes during a meeting or when taking notes on ideas. For example, input can be made in real time to quickly record what is said during a meeting. In addition, when taking notes on an idea, the user can instantly input the content that comes to mind and look back on it later. In addition, the reception unit also has a function of temporarily saving the memo entered by the user and allowing editing or addition later. This allows the user to respond flexibly when taking notes, and to modify or add to the content as necessary.

[0031] The analysis unit uses the generation AI to analyze the memo entered by the reception unit and corrects misspellings and typos. The generation AI uses natural language processing technology to analyze the entered text, detects misspellings and typos, and corrects them. For example, it corrects "Good morning" to "Good morning". The analysis unit can also perform predictive conversion using the generation AI. For example, if "Oha" is entered, it is predicted to be converted to "Good morning". The generation AI can learn the user's input patterns and past input history, and provide more accurate predictive conversion. The analysis unit can also correct typos using the generation AI. A typo is an input mistake, for example, it corrects "Good morning" to "Good morning". The generation AI understands the context and makes appropriate corrections, so the user can take notes without worrying about misspellings and typos. The analysis unit can make these corrections in real time and reflect the correction results before the user completes the input. This allows users to take notes smoothly and eliminates the need to correct them later. Furthermore, the analysis unit regularly updates the learning data of the generation AI to keep up with the latest language trends and user input habits, ensuring high accuracy at all times.

[0032] The display unit displays the memo corrected by the analysis unit. The display unit can display the corrected memo on the screen, for example. Specifically, the memo entered by the user is corrected in real time, and the result is immediately reflected on the screen. This allows the user to always take accurate and easy-to-read notes. For example, when taking minutes during a meeting, you can take notes smoothly without worrying about typos or omissions. In addition, when taking notes on ideas, the generation AI automatically corrects them, so you can take notes without interrupting your thoughts. The display unit also has a function to adjust the font size and color so that the user can easily see the memo. This improves visibility and reduces fatigue even with long-term use. The display unit also provides a tagging function to organize the contents of the memo and a function to highlight important parts. This allows the user to quickly find the information he or she needs. In addition, the display unit also has a function to share the contents of the memo with other devices and applications. For example, you can use a cloud service to synchronize your memo and make it accessible from different devices. You can also share your memo with other users through email or a messaging app. This allows the display unit to automatically correct typos and spelling errors when the user takes notes, not only providing accurate, easy-to-read notes, but also making it easy to manage and share notes.

[0033] The analysis unit can detect and correct typos and omissions using the generation AI. The analysis unit detects and corrects typos and omissions in the input memo using the generation AI. The generation AI analyzes the input text and detects typos and omissions, for example, using natural language processing technology. For example, "Good morning" is corrected to "Good morning". In addition, the generation AI learns a large amount of text data to improve the accuracy of correcting typos and omissions. For example, the generation AI improves the correction accuracy by learning past memo data and understanding the pattern of typos and omissions. As a result, the use of the generation AI improves the correction accuracy of typos and omissions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the input memo to the generation AI and have the generation AI execute the correction of typos and omissions.

[0034] The analysis unit can detect and correct typos using the generation AI. The analysis unit detects and corrects typos in the input memo using the generation AI. The generation AI, for example, uses natural language processing technology to analyze the input text and detect typos. A typo is an input mistake, and for example, "Good morning" is corrected to "Good morning". In addition, the generation AI learns a large amount of text data to improve the accuracy of typo correction. For example, the generation AI improves the correction accuracy by learning past memo data and understanding the typo pattern. As a result, the accuracy of typo correction is improved by using the generation AI. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the input memo to the generation AI and have the generation AI execute the typo correction.

[0035] The analysis unit can perform predictive conversion using the generation AI. The analysis unit performs predictive conversion of the input memo using the generation AI. The generation AI analyzes the input text using, for example, natural language processing technology and performs predictive conversion. For example, when "oha" is input, it is predicted to be converted to "ohayou gozaimasu". In addition, the generation AI learns a large amount of text data to improve the accuracy of predictive conversion. For example, the generation AI improves the conversion accuracy by learning past memo data and grasping the pattern of predictive conversion. As a result, the accuracy of predictive conversion is improved by using the generation AI. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the input memo to the generation AI and have the generation AI execute predictive conversion.

[0036] The analysis unit can automatically correct the double-lined portion. The analysis unit uses the generation AI to automatically correct the double-lined portion as in a handwritten note. The generation AI, for example, uses natural language processing technology to analyze the input text and detect the double-lined portion. For example, when a user indicates a portion to be corrected by drawing a double line, the generation AI automatically corrects that portion. This can save the user the trouble of manually correcting the portion. In addition, the generation AI learns a large amount of text data to improve the accuracy of correcting the double line. For example, the generation AI learns past memo data and grasps the pattern of the double line to improve the correction accuracy. This can save the user the trouble of automatically correcting the portion by the generation AI, taking into account points that are easy to correct as in a handwritten note. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the input memo to the generation AI and cause the generation AI to execute correction of the double-lined portion.

[0037] The reception unit may include an interface including a keyboard, a touch screen, or a voice input. The reception unit includes an interface for a user to input a memo. The interface includes, for example, a keyboard, a touch screen, a voice input, and the like. For example, a user can input a memo using a keyboard. Also, a user can input a memo using a touch screen. Furthermore, a user can input a memo using a voice input. For example, when a user inputs a memo using a voice input, the voice can be converted into text using a voice recognition technology. This improves user convenience by supporting various input methods. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input a voice input to a generation AI and cause the generation AI to convert the voice data into text data.

[0038] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit uses the generation AI to analyze the user's past input history and suggest the optimal input method. The generation AI, for example, analyzes past input data and identifies input methods frequently used by the user. For example, it preferentially suggests input methods (keyboard, touch screen, voice input, etc.) that the user has frequently used in the past. The generation AI can also analyze past input contents and suggest related input methods. For example, it analyzes the contents of notes that the user has input in the past and suggests related input methods. Furthermore, the generation AI can predict and suggest an input method to be used in a specific time period from the past input history. For example, if a user frequently uses voice input in a specific time period, the generation AI suggests voice input in that time period. This improves input efficiency by suggesting the optimal input method based on the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input data to the generation AI and cause the generation AI to execute a suggestion of the optimal input method.

[0039] The reception unit can automatically switch the input mode based on the user's current activity status at the time of input. The reception unit uses the generation AI to analyze the user's current activity status at the time of input and automatically switch the input mode. The generation AI, for example, analyzes the user's activity log and identifies the current activity status. For example, when the user is in a meeting, the voice input mode is prioritized so that the user can take notes quickly. The generation AI can also analyze the user's movement status and switch the input mode. For example, when the user is moving, the touch screen input mode is prioritized so that the user can operate with one hand. Furthermore, the generation AI can also analyze the user's relaxation status and switch the input mode. For example, when the user is relaxed, the keyboard input mode is prioritized so that the user can take detailed notes. This improves the convenience of input by switching the input mode according to the user's activity status. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's activity log to the generation AI and cause the generation AI to switch the input mode.

[0040] The reception unit can provide a highly relevant input method preferentially based on the geographical location information of the user at the time of input. The reception unit uses the generation AI to analyze the geographical location information of the user at the time of input, and provides a highly relevant input method preferentially. The generation AI, for example, analyzes GPS data and identifies the user's current geographical location. For example, when the user is in an office, keyboard input is provided preferentially. The generation AI can also switch the input method based on the geographical location information of the user. For example, when the user is out, voice input is provided preferentially. Furthermore, the generation AI can also suggest an input method based on the geographical location information of the user. For example, when the user is at home, touch screen input is provided preferentially. This improves the convenience of input by providing the optimal input method based on the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input GPS data to the generation AI and cause the generation AI to execute a suggestion of the optimal input method.

[0041] The reception unit can analyze the user's social media activity at the time of input and suggest a related input method. The reception unit uses the generation AI to analyze the user's social media activity at the time of input and suggest a related input method. The generation AI, for example, analyzes the content of the user's social media posts and identifies frequently used words and phrases. For example, the generation AI suggests an input method based on the words and phrases the user frequently uses on social media. The generation AI can also suggest an input method related to a specific topic based on the user's social media activity. For example, if a user frequently posts on a specific topic, an input method related to the topic is suggested. Furthermore, the generation AI can also analyze the user's social media activity and suggest an optimal input method. For example, the generation AI can analyze the user's social media activity and suggest an optimal input method. As a result, input efficiency is improved by suggesting an optimal input method based on social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input social media posting data to the generation AI and cause the generation AI to execute a suggestion of an optimal input method.

[0042] The analysis unit can adjust the level of detail of the correction based on the importance of the note during analysis. The analysis unit uses the generation AI to evaluate the importance of the note during analysis and adjust the level of detail of the correction. The generation AI, for example, analyzes the frequency of occurrence of keywords in the input note and evaluates the importance of the note. For example, in the case of an important note, detailed correction is performed. The generation AI can also adjust the level of detail of the correction based on the importance specified by the user. For example, in the case of a general note, a concise correction is performed. Furthermore, the generation AI can analyze the contents of the note and adjust the level of detail of the correction based on the importance. For example, in the case of a note with low importance, a minimum correction is performed. As a result, appropriate correction can be performed by adjusting the level of detail of the correction according to the importance of the note. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the note to the generation AI and cause the generation AI to adjust the level of detail of the correction.

[0043] The analysis unit can apply different correction algorithms depending on the category of the note during analysis. The analysis unit uses the generation AI to identify the category of the note during analysis and apply different correction algorithms. The generation AI, for example, analyzes the contents of the input note and identifies the category. For example, in the case of a business note, the writing style is corrected to a formal style. In addition, the generation AI can also correct the writing style to a casual style in the case of a private note. Furthermore, in the case of an academic note, the generation AI can also appropriately correct technical terms. As a result, appropriate corrections can be made by applying a correction algorithm according to the category of the note. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the note to the generation AI and cause the generation AI to apply the correction algorithm.

[0044] The analysis unit can determine the priority of correction based on the submission time of the note during analysis. The analysis unit uses the generation AI to evaluate the submission time of the note during analysis and determine the priority of correction. The generation AI, for example, analyzes the submission deadline of the input note and identifies the submission time. For example, in the case of a note whose deadline is approaching, correction is performed preferentially. In addition, in the case of a note whose submission time is far away, the generation AI can also postpone the correction. Furthermore, in the case of a note whose submission time is unknown, the generation AI can also perform correction based on importance. For example, the generation AI prioritizes correction of notes with high importance. As a result, correction can be performed at an appropriate time by determining the priority of correction based on the submission time. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the note to the generation AI and cause the generation AI to execute the determination of the priority of correction.

[0045] The analysis unit can adjust the order of correction based on the relevance of the notes during analysis. The analysis unit uses the generation AI to evaluate the relevance of the notes during analysis and adjust the order of correction. The generation AI, for example, analyzes the contents of the input notes and evaluates the relevance. For example, it prioritizes correction of notes with high relevance. The generation AI can also postpone correction of notes with low relevance. Furthermore, in the case of notes whose relevance is unclear, the generation AI can also perform correction based on importance. For example, it prioritizes correction of notes with high importance. As a result, by adjusting the order of correction based on the relevance of the notes, appropriate correction can be performed. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the notes to the generation AI and cause the generation AI to adjust the order of correction.

[0046] The display unit can select the optimal display method by referring to the user's past display history at the time of display. The display unit uses the generation AI to analyze the user's past display history at the time of display and select the optimal display method. The generation AI, for example, analyzes past display data and identifies a display method that the user prefers to use. For example, the display method that the user prefers to use in the past is preferentially provided. The generation AI can also suggest a highly relevant display method based on the past display history. For example, a highly relevant display method is proposed from the user's past display history. Furthermore, the generation AI can also analyze the past display history and select the optimal display method. For example, the user's past display history is analyzed and the optimal display method is selected. As a result, the visibility is improved by providing the optimal display method based on the past display history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input past display data to the generation AI and cause the generation AI to select the optimal display method.

[0047] The display unit can adjust the level of detail of the display based on the importance of the note at the time of display. The display unit uses the generation AI to evaluate the importance of the note at the time of display and adjust the level of detail of the display. The generation AI, for example, analyzes the frequency of occurrence of keywords of the input note and evaluates the importance of the note. For example, in the case of an important note, detailed information is displayed. The generation AI can also adjust the level of detail of the display based on the importance specified by the user. For example, in the case of a general note, concise information is displayed. Furthermore, the generation AI can analyze the contents of the note and adjust the level of detail of the display based on the importance. For example, in the case of a note with low importance, the minimum information is displayed. As a result, the level of detail of the display is adjusted according to the importance of the note, so that appropriate information is displayed. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the importance of the note to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0048] The display unit can select the optimal display method in consideration of the device information of the user at the time of display. The display unit uses the generation AI to analyze the device information of the user at the time of display and select the optimal display method. The generation AI, for example, analyzes the type and screen size of the user's device and identifies the optimal display method. For example, if the user is using a smartphone, a display method that matches the screen size is provided. In addition, if the user is using a tablet, the generation AI can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the generation AI can also provide a simple and highly visible display method. As a result, visibility is improved by providing the optimal display method based on the device information. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input device information to the generation AI and cause the generation AI to select the optimal display method.

[0049] The display unit can adjust the display order based on the relevance of the notes at the time of display. The display unit uses the generation AI to evaluate the relevance of the notes at the time of display and adjust the display order. The generation AI, for example, analyzes the contents of the inputted notes and evaluates the relevance. For example, it preferentially displays notes with high relevance. The generation AI can also display notes with low relevance later. Furthermore, in the case of notes with unknown relevance, the generation AI can also display based on importance. For example, it preferentially displays notes with high importance. As a result, by adjusting the display order based on the relevance of the notes, appropriate information is preferentially displayed. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the relevance of the notes to the generation AI and cause the generation AI to adjust the display order.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the user's input speed and dynamically change the layout of the input interface according to the input speed. For example, if the user is inputting quickly, the input field can be widened to make input easier. If the user is inputting slowly, the input field can be narrowed to improve visibility. Furthermore, the number of predictive conversion candidates can be increased or decreased according to the user's input speed. This improves input efficiency by providing an optimal interface according to the user's input speed.

[0052] The analysis unit can automatically obtain related external information based on the user's input and add it to the note. For example, if the user is inputting minutes of a meeting, related meeting materials and past minutes are automatically obtained and added to the note. Also, if the user is taking notes on a specific topic, related websites and papers are automatically obtained and added to the note. Furthermore, based on keywords entered by the user, related news articles and blog articles can be automatically obtained and added to the note. This allows the user to quickly obtain the information they need and enrich the content of their notes.

[0053] The analysis unit can automatically summarize the contents of notes based on the user's input. For example, if a long note is input, the analysis unit can extract important points and generate a summary. Also, if meeting minutes are input, summaries can be generated for each agenda item. Furthermore, if a user is taking notes on a specific topic, the analysis unit can extract important information related to that topic and generate a summary. This allows the user to concisely grasp the contents of the notes, making it convenient for later reference.

[0054] The analysis unit can automatically categorize the contents of notes based on the user's input. For example, if a user inputs minutes of a meeting, the notes can be categorized by topic. If the user is taking notes on multiple projects, the notes can also be categorized by project. Furthermore, if the user is taking notes on a specific topic, the notes can also be categorized by subtopics related to that topic. This makes it easier for the user to organize the contents of the notes, making them convenient for future reference.

[0055] The analysis unit can automatically translate the contents of the notes based on the user's input. For example, if the user is taking notes in English, it can translate them into Japanese. Also, if the user is taking notes in multiple languages, it can translate into each language. Furthermore, if the user is taking notes on a specific topic, it can translate information related to that topic into other languages. This allows users to take notes in different languages, facilitating international communication.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The user enters a note in the reception area. The user can enter a note using a keyboard or a touch screen. The reception area can be used in a variety of situations, such as taking minutes during a meeting or taking notes on ideas. Step 2: The analysis unit uses the generation AI to analyze the memo entered by the reception unit and corrects typos and spelling errors. The generation AI analyzes the entered text, detects and corrects typos and spelling errors. It can also perform predictive conversion; for example, if you enter "oha," it will be predicted and converted to "ohayou gozaimasu." It also corrects typos, for example correcting "ohayou gozaimasu" to "ohayou gozaimasu." Step 3: The display unit displays the memo corrected by the analysis unit. The display unit can display the corrected memo on the screen. This allows the user to always take accurate and easy-to-read notes.

[0058] (Example 2) The memo pad application according to the embodiment of the present invention is a system in which a user inputs a memo, a generation AI always synchronizes the entire text, corrects typos, predicts conversion, and corrects typos. In the memo pad application, a user inputs a memo, and a generation AI always synchronizes the entire text, corrects typos, predicts conversion, and corrects typos. Furthermore, taking into consideration the ease of correction like handwritten notes, the generation AI automatically corrects them, thereby saving the user the trouble of correcting them. For example, a user inputs a memo. At this time, the user inputs the memo using a keyboard or touch screen. For example, it can be used in various scenes, such as taking minutes during a meeting or taking notes on ideas. Next, the generation AI always synchronizes the input memo and corrects typos. The generation AI analyzes the input text, detects typos and omissions, and corrects them. For example, it corrects "Good morning" to "Good morning". In addition, it uses a predictive conversion function to predict the word the user is about to input and assists input. For example, if "oha" is input, it is predicted to be converted to "Good morning". In addition, the generation AI also corrects typos. A typo is an input mistake, for example, "Good morning" is corrected to "Good morning". In this way, the notes entered by the user can always be kept accurate. In addition, the generation AI automatically corrects points that are easy to correct, such as handwritten notes. For example, if a user draws a double line to indicate a part that they want to correct, the generation AI automatically corrects that part. This saves the user the trouble of manually correcting it. This mechanism allows the user to always take accurate and easy-to-read notes. For example, when taking minutes during a meeting, you can take notes smoothly without worrying about typos or omissions. In addition, when taking notes on ideas, the generation AI automatically corrects them, so you can take notes without interrupting your thoughts. In this way, a memo pad app that utilizes the generation AI supports the user's note-taking and allows efficient and accurate notes to be taken. As a result, the memo pad application can automatically correct typos and typos when the user takes notes, providing accurate and easy-to-read notes.

[0059] The memo pad application according to the embodiment includes a reception unit, an analysis unit, and a display unit. The reception unit allows a user to input a memo. When the user inputs a memo, for example, a keyboard or a touch screen can be used. The reception unit can be used in various situations, such as taking minutes during a meeting or taking notes on an idea. The analysis unit uses a generation AI to analyze the memo input by the reception unit and corrects typos and spelling errors. The generation AI analyzes the input text and detects and corrects typos and spelling errors. For example, it corrects "Good morning" to "Good morning". The analysis unit can also perform predictive conversion using the generation AI. For example, when "Oha" is input, it is predicted to be converted to "Good morning". The analysis unit can also correct typos using the generation AI. A typo is an input error, and for example, it corrects "Good morning" to "Good morning". The display unit displays the memo corrected by the analysis unit. The display unit can display, for example, the corrected memo on the screen. This allows the user to always take accurate and easy-to-read notes. For example, when taking minutes during a meeting, the user can take notes smoothly without worrying about typos or spelling mistakes. In addition, when taking notes on ideas, the generation AI automatically corrects them, so the user can take notes without interrupting their thoughts. This allows the memo pad application according to the embodiment to automatically correct typos and spelling mistakes when the user takes notes, providing accurate and easy-to-read notes.

[0060] The reception unit provides an interface for the user to input a memo. When the user inputs a memo, the user can use a keyboard or a touch screen. Specifically, when inputting text using a keyboard, either a physical keyboard or a software keyboard can be used, and the user can select according to his / her preference. When using a touch screen, the user can input characters by tapping a virtual keyboard displayed on the screen. In addition, by providing a voice input function, it is also possible to convert what the user speaks into text. This allows the user to respond to cases where their hands are full or when they want to take notes quickly. The reception unit can be used in various scenes, such as when taking minutes during a meeting or when taking notes on ideas. For example, input can be made in real time to quickly record what is said during a meeting. In addition, when taking notes on an idea, the user can instantly input the content that comes to mind and look back on it later. In addition, the reception unit also has a function of temporarily saving the memo entered by the user and allowing editing or addition later. This allows the user to respond flexibly when taking notes, and to modify or add to the content as necessary.

[0061] The analysis unit uses the generation AI to analyze the memo entered by the reception unit and corrects misspellings and typos. The generation AI uses natural language processing technology to analyze the entered text, detects misspellings and typos, and corrects them. For example, it corrects "Good morning" to "Good morning". The analysis unit can also perform predictive conversion using the generation AI. For example, if "Oha" is entered, it is predicted to be converted to "Good morning". The generation AI can learn the user's input patterns and past input history, and provide more accurate predictive conversion. The analysis unit can also correct typos using the generation AI. A typo is an input mistake, for example, it corrects "Good morning" to "Good morning". The generation AI understands the context and makes appropriate corrections, so the user can take notes without worrying about misspellings and typos. The analysis unit can make these corrections in real time and reflect the correction results before the user completes the input. This allows users to take notes smoothly and eliminates the need to correct them later. Furthermore, the analysis unit regularly updates the learning data of the generation AI to keep up with the latest language trends and user input habits, ensuring high accuracy at all times.

[0062] The display unit displays the memo corrected by the analysis unit. The display unit can display the corrected memo on the screen, for example. Specifically, the memo entered by the user is corrected in real time, and the result is immediately reflected on the screen. This allows the user to always take accurate and easy-to-read notes. For example, when taking minutes during a meeting, you can take notes smoothly without worrying about typos or omissions. In addition, when taking notes on ideas, the generation AI automatically corrects them, so you can take notes without interrupting your thoughts. The display unit also has a function to adjust the font size and color so that the user can easily see the memo. This improves visibility and reduces fatigue even with long-term use. The display unit also provides a tagging function to organize the contents of the memo and a function to highlight important parts. This allows the user to quickly find the information he or she needs. In addition, the display unit also has a function to share the contents of the memo with other devices and applications. For example, you can use a cloud service to synchronize your memo and make it accessible from different devices. You can also share your memo with other users through email or a messaging app. This allows the display unit to automatically correct typos and spelling errors when the user takes notes, not only providing accurate, easy-to-read notes, but also making it easy to manage and share notes.

[0063] The analysis unit can detect and correct typos and omissions using the generation AI. The analysis unit detects and corrects typos and omissions in the input memo using the generation AI. The generation AI analyzes the input text and detects typos and omissions, for example, using natural language processing technology. For example, "Good morning" is corrected to "Good morning". In addition, the generation AI learns a large amount of text data to improve the accuracy of correcting typos and omissions. For example, the generation AI improves the correction accuracy by learning past memo data and understanding the pattern of typos and omissions. As a result, the use of the generation AI improves the correction accuracy of typos and omissions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the input memo to the generation AI and have the generation AI execute the correction of typos and omissions.

[0064] The analysis unit can detect and correct typos using the generation AI. The analysis unit detects and corrects typos in the input memo using the generation AI. The generation AI, for example, uses natural language processing technology to analyze the input text and detect typos. A typo is an input mistake, and for example, "Good morning" is corrected to "Good morning". In addition, the generation AI learns a large amount of text data to improve the accuracy of typo correction. For example, the generation AI improves the correction accuracy by learning past memo data and understanding the typo pattern. As a result, the accuracy of typo correction is improved by using the generation AI. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the input memo to the generation AI and have the generation AI execute the typo correction.

[0065] The analysis unit can perform predictive conversion using the generation AI. The analysis unit performs predictive conversion of the input memo using the generation AI. The generation AI analyzes the input text using, for example, natural language processing technology and performs predictive conversion. For example, when "oha" is input, it is predicted to be converted to "ohayou gozaimasu". In addition, the generation AI learns a large amount of text data to improve the accuracy of predictive conversion. For example, the generation AI improves the conversion accuracy by learning past memo data and grasping the pattern of predictive conversion. As a result, the accuracy of predictive conversion is improved by using the generation AI. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the input memo to the generation AI and have the generation AI execute predictive conversion.

[0066] The analysis unit can automatically correct the double-lined portion. The analysis unit uses the generation AI to automatically correct the double-lined portion as in a handwritten note. The generation AI, for example, uses natural language processing technology to analyze the input text and detect the double-lined portion. For example, when a user indicates a portion to be corrected by drawing a double line, the generation AI automatically corrects that portion. This can save the user the trouble of manually correcting the portion. In addition, the generation AI learns a large amount of text data to improve the accuracy of correcting the double line. For example, the generation AI learns past memo data and grasps the pattern of the double line to improve the correction accuracy. This can save the user the trouble of automatically correcting the portion by the generation AI, taking into account points that are easy to correct as in a handwritten note. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the input memo to the generation AI and cause the generation AI to execute correction of the double-lined portion.

[0067] The reception unit may include an interface including a keyboard, a touch screen, or a voice input. The reception unit includes an interface for a user to input a memo. The interface includes, for example, a keyboard, a touch screen, a voice input, and the like. For example, a user can input a memo using a keyboard. Also, a user can input a memo using a touch screen. Furthermore, a user can input a memo using a voice input. For example, when a user inputs a memo using a voice input, the voice can be converted into text using a voice recognition technology. This improves user convenience by supporting various input methods. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input a voice input to a generation AI and cause the generation AI to convert the voice data into text data.

[0068] The reception unit can estimate the user's emotion and dynamically change the layout of the input interface based on the estimated user's emotion. The reception unit uses the generation AI to estimate the user's emotion and dynamically change the layout of the input interface based on the estimated user's emotion. The generation AI, for example, uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. For example, when the user is stressed, a simple and intuitive interface is provided to reduce the effort of input. The generation AI can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. For example, when the user is relaxed, a customizable interface including detailed options is provided. The generation AI can also analyze the content of the user's input using text analysis technology to estimate the emotion. For example, when the user is in a hurry, voice input is prioritized so that notes can be entered quickly. This can reduce the effort of input by optimizing the interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0069] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit uses the generation AI to analyze the user's past input history and suggest the optimal input method. The generation AI, for example, analyzes past input data and identifies input methods frequently used by the user. For example, it preferentially suggests input methods (keyboard, touch screen, voice input, etc.) that the user has frequently used in the past. The generation AI can also analyze past input contents and suggest related input methods. For example, it analyzes the contents of notes that the user has input in the past and suggests related input methods. Furthermore, the generation AI can predict and suggest an input method to be used in a specific time period from the past input history. For example, if a user frequently uses voice input in a specific time period, the generation AI suggests voice input in that time period. This improves input efficiency by suggesting the optimal input method based on the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input data to the generation AI and cause the generation AI to execute a suggestion of the optimal input method.

[0070] The reception unit can automatically switch the input mode based on the user's current activity status at the time of input. The reception unit uses the generation AI to analyze the user's current activity status at the time of input and automatically switch the input mode. The generation AI, for example, analyzes the user's activity log and identifies the current activity status. For example, when the user is in a meeting, the voice input mode is prioritized so that the user can take notes quickly. The generation AI can also analyze the user's movement status and switch the input mode. For example, when the user is moving, the touch screen input mode is prioritized so that the user can operate with one hand. Furthermore, the generation AI can also analyze the user's relaxation status and switch the input mode. For example, when the user is relaxed, the keyboard input mode is prioritized so that the user can take detailed notes. This improves the convenience of input by switching the input mode according to the user's activity status. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's activity log to the generation AI and cause the generation AI to switch the input mode.

[0071] The reception unit can estimate the user's emotion and determine the priority of input based on the estimated user's emotion. The reception unit uses the generation AI to estimate the user's emotion and determine the priority of input based on the estimated user's emotion. The generation AI, for example, uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. For example, if the user is nervous, important notes can be input preferentially. The generation AI can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. For example, if the user is relaxed, detailed notes can be input. The generation AI can also analyze the user's input content using text analysis technology to estimate the emotion. For example, if the user is in a hurry, brief notes can be input preferentially. This allows important notes to be input preferentially by determining the priority of input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0072] The reception unit can provide a highly relevant input method preferentially based on the geographical location information of the user at the time of input. The reception unit uses the generation AI to analyze the geographical location information of the user at the time of input, and provides a highly relevant input method preferentially. The generation AI, for example, analyzes GPS data and identifies the user's current geographical location. For example, when the user is in an office, keyboard input is provided preferentially. The generation AI can also switch the input method based on the geographical location information of the user. For example, when the user is out, voice input is provided preferentially. Furthermore, the generation AI can also suggest an input method based on the geographical location information of the user. For example, when the user is at home, touch screen input is provided preferentially. This improves the convenience of input by providing the optimal input method based on the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input GPS data to the generation AI and cause the generation AI to execute a suggestion of the optimal input method.

[0073] The reception unit can analyze the user's social media activity at the time of input and suggest a related input method. The reception unit uses the generation AI to analyze the user's social media activity at the time of input and suggest a related input method. The generation AI, for example, analyzes the content of the user's social media posts and identifies frequently used words and phrases. For example, the generation AI suggests an input method based on the words and phrases the user frequently uses on social media. The generation AI can also suggest an input method related to a specific topic based on the user's social media activity. For example, if a user frequently posts on a specific topic, an input method related to the topic is suggested. Furthermore, the generation AI can also analyze the user's social media activity and suggest an optimal input method. For example, the generation AI can analyze the user's social media activity and suggest an optimal input method. As a result, input efficiency is improved by suggesting an optimal input method based on social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input social media posting data to the generation AI and cause the generation AI to execute a suggestion of an optimal input method.

[0074] The analysis unit can estimate the user's emotions and adjust the correction method for typos and omissions based on the estimated user's emotions. The analysis unit uses the generation AI to estimate the user's emotions and adjust the correction method for typos and omissions based on the estimated user's emotions. The generation AI, for example, uses facial expression recognition technology to analyze the user's facial expressions and estimate emotions. For example, if the user is relaxed, detailed correction suggestions are made. The generation AI can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate emotions. For example, if the user is in a hurry, concise correction suggestions are made. Furthermore, the generation AI can also use text analysis technology to analyze the user's input contents and estimate emotions. For example, if the user is stressed, minimal correction suggestions are made. This allows more appropriate corrections to be made by adjusting the correction method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0075] The analysis unit can adjust the level of detail of the correction based on the importance of the note during analysis. The analysis unit uses the generation AI to evaluate the importance of the note during analysis and adjust the level of detail of the correction. The generation AI, for example, analyzes the frequency of occurrence of keywords in the input note and evaluates the importance of the note. For example, in the case of an important note, detailed correction is performed. The generation AI can also adjust the level of detail of the correction based on the importance specified by the user. For example, in the case of a general note, a concise correction is performed. Furthermore, the generation AI can analyze the contents of the note and adjust the level of detail of the correction based on the importance. For example, in the case of a note with low importance, a minimum correction is performed. As a result, appropriate correction can be performed by adjusting the level of detail of the correction according to the importance of the note. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the note to the generation AI and cause the generation AI to adjust the level of detail of the correction.

[0076] The analysis unit can apply different correction algorithms depending on the category of the note during analysis. The analysis unit uses the generation AI to identify the category of the note during analysis and apply different correction algorithms. The generation AI, for example, analyzes the contents of the input note and identifies the category. For example, in the case of a business note, the writing style is corrected to a formal style. In addition, the generation AI can also correct the writing style to a casual style in the case of a private note. Furthermore, in the case of an academic note, the generation AI can also appropriately correct technical terms. As a result, appropriate corrections can be made by applying a correction algorithm according to the category of the note. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the note to the generation AI and cause the generation AI to apply the correction algorithm.

[0077] The analysis unit can estimate the user's emotion and determine the priority of corrections based on the estimated user's emotion. The analysis unit estimates the user's emotion using the generation AI and determines the priority of corrections based on the estimated user's emotion. The generation AI, for example, uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. For example, if the user is nervous, important corrections are prioritized. The generation AI can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. For example, if the user is relaxed, detailed corrections are prioritized. Furthermore, the generation AI can also analyze the user's input contents using text analysis technology to estimate the emotion. For example, if the user is in a hurry, brief corrections are prioritized. As a result, important corrections can be prioritized by determining the priority of corrections according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0078] The analysis unit can determine the priority of correction based on the submission time of the note during analysis. The analysis unit uses the generation AI to evaluate the submission time of the note during analysis and determine the priority of correction. The generation AI, for example, analyzes the submission deadline of the input note and identifies the submission time. For example, in the case of a note whose deadline is approaching, correction is performed preferentially. In addition, in the case of a note whose submission time is far away, the generation AI can also postpone the correction. Furthermore, in the case of a note whose submission time is unknown, the generation AI can also perform correction based on importance. For example, the generation AI prioritizes correction of notes with high importance. As a result, correction can be performed at an appropriate time by determining the priority of correction based on the submission time. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the note to the generation AI and cause the generation AI to execute the determination of the priority of correction.

[0079] The analysis unit can adjust the order of correction based on the relevance of the notes during analysis. The analysis unit uses the generation AI to evaluate the relevance of the notes during analysis and adjust the order of correction. The generation AI, for example, analyzes the contents of the input notes and evaluates the relevance. For example, it prioritizes correction of notes with high relevance. The generation AI can also postpone correction of notes with low relevance. Furthermore, in the case of notes whose relevance is unclear, the generation AI can also perform correction based on importance. For example, it prioritizes correction of notes with high importance. As a result, by adjusting the order of correction based on the relevance of the notes, appropriate correction can be performed. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the notes to the generation AI and cause the generation AI to adjust the order of correction.

[0080] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. The display unit uses the generation AI to estimate the user's emotion and adjust the display method based on the estimated user's emotion. The generation AI, for example, uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. For example, when the user is nervous, a simple and highly visible display method is provided. The generation AI can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. For example, when the user is relaxed, a display method including detailed information is provided. Furthermore, the generation AI can also use text analysis technology to analyze the contents of the user's input and estimate the emotion. For example, when the user is in a hurry, a display method that focuses on the main points is provided. This improves visibility by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0081] The display unit can select the optimal display method by referring to the user's past display history at the time of display. The display unit uses the generation AI to analyze the user's past display history at the time of display and select the optimal display method. The generation AI, for example, analyzes past display data and identifies a display method that the user prefers to use. For example, the display method that the user prefers to use in the past is preferentially provided. The generation AI can also suggest a highly relevant display method based on the past display history. For example, a highly relevant display method is proposed from the user's past display history. Furthermore, the generation AI can also analyze the past display history and select the optimal display method. For example, the user's past display history is analyzed and the optimal display method is selected. As a result, the visibility is improved by providing the optimal display method based on the past display history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input past display data to the generation AI and cause the generation AI to select the optimal display method.

[0082] The display unit can adjust the level of detail of the display based on the importance of the note at the time of display. The display unit uses the generation AI to evaluate the importance of the note at the time of display and adjust the level of detail of the display. The generation AI, for example, analyzes the frequency of occurrence of keywords of the input note and evaluates the importance of the note. For example, in the case of an important note, detailed information is displayed. The generation AI can also adjust the level of detail of the display based on the importance specified by the user. For example, in the case of a general note, concise information is displayed. Furthermore, the generation AI can analyze the contents of the note and adjust the level of detail of the display based on the importance. For example, in the case of a note with low importance, the minimum information is displayed. As a result, the level of detail of the display is adjusted according to the importance of the note, so that appropriate information is displayed. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the importance of the note to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0083] The display unit can estimate the user's emotion and determine the display priority based on the estimated user's emotion. The display unit estimates the user's emotion using the generation AI and determines the display priority based on the estimated user's emotion. The generation AI, for example, uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. For example, when the user is nervous, important information is preferentially displayed. The generation AI can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. For example, when the user is relaxed, detailed information is preferentially displayed. Furthermore, the generation AI can also analyze the contents of the user's input using text analysis technology to estimate the emotion. For example, when the user is in a hurry, information that covers the main points is preferentially displayed. As a result, important information is preferentially displayed by determining the display priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0084] The display unit can select the optimal display method in consideration of the device information of the user at the time of display. The display unit uses the generation AI to analyze the device information of the user at the time of display and select the optimal display method. The generation AI, for example, analyzes the type and screen size of the user's device and identifies the optimal display method. For example, if the user is using a smartphone, a display method that matches the screen size is provided. In addition, if the user is using a tablet, the generation AI can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the generation AI can also provide a simple and highly visible display method. As a result, visibility is improved by providing the optimal display method based on the device information. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input device information to the generation AI and cause the generation AI to select the optimal display method.

[0085] The display unit can adjust the display order based on the relevance of the notes at the time of display. The display unit uses the generation AI to evaluate the relevance of the notes at the time of display and adjust the display order. The generation AI, for example, analyzes the contents of the inputted notes and evaluates the relevance. For example, it preferentially displays notes with high relevance. The generation AI can also display notes with low relevance later. Furthermore, in the case of notes with unknown relevance, the generation AI can also display based on importance. For example, it preferentially displays notes with high importance. As a result, by adjusting the display order based on the relevance of the notes, appropriate information is preferentially displayed. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the relevance of the notes to the generation AI and cause the generation AI to adjust the display order.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The reception unit can analyze the user's input speed and dynamically change the layout of the input interface according to the input speed. For example, if the user is inputting quickly, the input field can be widened to make input easier. If the user is inputting slowly, the input field can be narrowed to improve visibility. Furthermore, the number of predictive conversion candidates can be increased or decreased according to the user's input speed. This improves input efficiency by providing an optimal interface according to the user's input speed.

[0088] The analysis unit can automatically obtain related external information based on the user's input and add it to the note. For example, if the user is inputting minutes of a meeting, related meeting materials and past minutes are automatically obtained and added to the note. Also, if the user is taking notes on a specific topic, related websites and papers are automatically obtained and added to the note. Furthermore, based on keywords entered by the user, related news articles and blog articles can be automatically obtained and added to the note. This allows the user to quickly obtain the information they need and enrich the content of their notes.

[0089] The analysis unit can automatically summarize the contents of notes based on the user's input. For example, if a long note is input, the analysis unit can extract important points and generate a summary. Also, if meeting minutes are input, summaries can be generated for each agenda item. Furthermore, if a user is taking notes on a specific topic, the analysis unit can extract important information related to that topic and generate a summary. This allows the user to concisely grasp the contents of the notes, making it convenient for later reference.

[0090] The analysis unit can automatically categorize the contents of notes based on the user's input. For example, if a user inputs minutes of a meeting, the notes can be categorized by topic. If the user is taking notes on multiple projects, the notes can also be categorized by project. Furthermore, if the user is taking notes on a specific topic, the notes can also be categorized by subtopics related to that topic. This makes it easier for the user to organize the contents of the notes, making them convenient for future reference.

[0091] The analysis unit can automatically translate the contents of the notes based on the user's input. For example, if the user is taking notes in English, it can translate them into Japanese. Also, if the user is taking notes in multiple languages, it can translate into each language. Furthermore, if the user is taking notes on a specific topic, it can translate information related to that topic into other languages. This allows users to take notes in different languages, facilitating international communication.

[0092] The reception unit can estimate the user's emotion and provide input feedback based on the estimated user's emotion. For example, if the user is feeling stressed, an encouraging message can be displayed. Also, if the user is relaxed, positive feedback can be provided. Furthermore, if the user is in a hurry, a hint can be provided to support rapid input. In this way, by providing feedback according to the user's emotion, it is possible to improve the motivation for input.

[0093] The analysis unit can estimate the user's emotions and adjust the contents of the memo based on the estimated user's emotions. For example, if the user is nervous, a concise memo that focuses on the main points can be generated. If the user is relaxed, a memo that includes detailed information can also be generated. Furthermore, if the user is stressed, important information can be highlighted and displayed. This makes it easier for the user to understand the information by providing memo contents that correspond to the user's emotions.

[0094] The display unit can estimate the user's emotion and adjust the display color tone based on the estimated user's emotion. For example, if the user is stressed, a calm color tone can be used. If the user is relaxed, a bright color tone can be used. Furthermore, if the user is in a hurry, a color tone with high visibility can be used. In this way, by providing a display color tone according to the user's emotion, visibility is improved and information can be easily understood.

[0095] The display unit can estimate the user's emotion and adjust the font size of the display based on the estimated user's emotion. For example, if the user is nervous, a large font size can be used. Also, if the user is relaxed, a standard font size can be used. Furthermore, if the user is in a hurry, a font size with high visibility can be used. In this way, by providing a font size of the display according to the user's emotion, visibility is improved and information can be easily understood.

[0096] The display unit can estimate the user's emotion and adjust the display layout based on the estimated user's emotion. For example, if the user is stressed, a simple layout can be used. If the user is relaxed, a layout including detailed information can be used. If the user is in a hurry, a layout focusing on the main points can be used. In this way, by providing a display layout according to the user's emotion, visibility is improved and information can be easily understood.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The user enters a note in the reception area. The user can enter a note using a keyboard or a touch screen. The reception area can be used in a variety of situations, such as taking minutes during a meeting or taking notes on ideas. Step 2: The analysis unit uses the generation AI to analyze the memo entered by the reception unit and corrects typos and spelling errors. The generation AI analyzes the entered text, detects and corrects typos and spelling errors. It can also perform predictive conversion; for example, if you enter "oha," it will be predicted and converted to "ohayou gozaimasu." It also corrects typos, for example correcting "ohayou gozaimasu" to "ohayou gozaimasu." Step 3: The display unit displays the memo corrected by the analysis unit. The display unit can display the corrected memo on the screen. This allows the user to always take accurate and easy-to-read notes.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0101] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a memo input by the user using a touch screen or keyboard of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and corrects typos and omissions and performs predictive conversion using a generation AI. The display unit displays the corrected memo on the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0110] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a memo input by a user using a touch screen or keyboard of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and corrects typos and omissions and performs predictive conversion using a generation AI. The display unit displays the corrected memo on the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives memo input by the user using a touch screen or keyboard of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and corrects typos and omissions and performs predictive conversion using a generation AI. The display unit displays the corrected memo on the display 343 of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives memo input by the user using a touch screen or keyboard of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and corrects typos and omissions and performs predictive conversion using a generation AI. The display unit displays the memo corrected by the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0153] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0156] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0157] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0164] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0168] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0170] (Appendix 1) a reception unit where a user inputs a memo; an analysis unit that analyzes the memo input by the reception unit and corrects typos or spelling errors; A display unit that displays the memo corrected by the analysis unit. A system characterized by: (Appendix 2) The analysis unit is Generative AI detects and corrects typos 2. The system of claim 1, (Appendix 3) The analysis unit is Detect and fix typos with generative AI 2. The system of claim 1, (Appendix 4) The analysis unit is Predictive conversion using generative AI 2. The system of claim 1, (Appendix 5) The analysis unit is Automatically correct areas with double lines 2. The system of claim 1, (Appendix 6) The reception unit is Equipped with an interface that includes a keyboard, touch screen, or voice input 2. The system of claim 1, (Appendix 7) The reception unit is Estimating user emotions and dynamically changing the layout of the input interface based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past input history and suggest appropriate input methods 2. The system of claim 1, (Appendix 9) The reception unit is Automatically switch input modes while typing based on the user's current activity 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The reception unit is As you type, it prioritizes relevant input methods based on your geographic location 2. The system of claim 1, (Appendix 12) The reception unit is As you type, it analyzes your social media activity and suggests relevant input methods. 2. The system of claim 1, (Appendix 13) The analysis unit is Inferring user sentiment and adjusting typo correction methods based on the estimated user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the granularity of revisions based on the importance of the note 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different correction algorithms are applied depending on the note category. 2. The system of claim 1, (Appendix 16) The analysis unit is Inferring user sentiment and prioritizing modifications based on the estimated user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize fixes based on when notes were submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of corrections based on note relevance 2. The system of claim 1, (Appendix 19) The display unit is Estimate user's emotions and adjust the display method based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The display unit is When displaying, the optimal display method is selected by referring to the user's past display history. 2. The system of claim 1, (Appendix 21) The display unit is When viewing, adjust the level of detail shown based on the importance of the note 2. The system of claim 1, (Appendix 22) The display unit is Estimate the user's emotions and determine the display priority based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The display unit is When displaying, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 24) The display unit is When viewing, reorder notes based on their relevance 2. The system of claim 1, [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit where a user inputs a memo; an analysis unit that analyzes the memo input by the reception unit and corrects typos or spelling errors; A display unit that displays the memo corrected by the analysis unit. A system characterized by:

2. The analysis unit is Generative AI detects and corrects typos 2. The system of claim 1 .

3. The analysis unit is Detect and fix typos with generative AI 2. The system of claim 1 .

4. The analysis unit is Predictive conversion using generative AI 2. The system of claim 1 .

5. The analysis unit is Automatically correct areas with double lines 2. The system of claim 1 .

6. The reception unit is Equipped with an interface that includes a keyboard, touch screen, or voice input 2. The system of claim 1 .

7. The reception unit is Estimating user emotions and dynamically changing the layout of the input interface based on the estimated user emotions 2. The system of claim 1 .

8. The reception unit is Analyze the user's past input history and suggest appropriate input methods 2. The system of claim 1 .

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