System

Abstract

An object of a system according to an embodiment is to detect an error in a text input by a user in real time and present a correction proposal.SOLUTION: A system includes an analysis unit, a detection unit, a proposal unit, a presentation unit, and a learning unit. The analysis unit analyzes a text input by a user in real time. The detection unit detects an error based on the text analyzed by the analysis unit. The proposal unit proposes a correction proposal on the basis of the error detected by the detection unit. The presentation unit presents the correction proposal proposed by the proposal unit to the user. The learning unit learns an input pattern of the user on the basis of the text analyzed 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies do not adequately detect errors in text entered by a user in real time and provide suggested corrections, and there is room for improvement.

[0005] The system according to the embodiment aims to detect errors in text entered by a user in real time and provide suggested corrections. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a detection unit, a suggestion unit, a presentation unit, and a learning unit. The analysis unit analyzes text entered by a user in real time. The detection unit detects errors based on the text analyzed by the analysis unit. The suggestion unit proposes corrections based on the errors detected by the detection unit. The presentation unit presents the corrections proposed by the suggestion unit to the user. The learning unit learns the user's input patterns based on the text analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect errors in the text entered by the user in real time and provide suggested corrections. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A keyboard application system according to an embodiment of the present invention analyzes text entered by a user in real time, detects errors, suggests corrections, and presents them to the user. The keyboard application system analyzes text entered by a user in real time, detects typographical errors and grammatical errors, and suggests appropriate corrections. For example, if a user enters "hello," the keyboard application system suggests "hello." The keyboard application system can also learn the user's input patterns and perform predictive conversion the next time the user enters text. This allows the user to efficiently enter accurate text. For example, the keyboard application system analyzes text entered by a user in real time and detects typographical errors and grammatical errors. The keyboard application system then suggests appropriate corrections based on the detected errors. The keyboard application system can also learn the user's input patterns and perform predictive conversion the next time the user enters text. This allows the user to reduce typographical errors and grammatical errors and enter accurate text. The predictive conversion function also improves input efficiency and enhances user convenience. For example, if a user attempts to enter "good morning," the keyboard application system can suggest predictive conversion such as "good morning." This allows users to input text accurately in a short amount of time. This allows the keyboard application system to analyze the user's input in real time, detect errors, suggest corrections, and present them to the user. For example, by quickly and accurately analyzing the text entered by the user and detecting errors, the user can input accurate text efficiently. In addition, the predictive conversion function improves input efficiency and increases user convenience.

[0029] A keyboard application system according to an embodiment includes an analysis unit, a detection unit, a suggestion unit, a presentation unit, and a learning unit. The analysis unit analyzes text entered by a user in real time. For example, the analysis unit analyzes the text entered by the user using natural language processing technology to detect typographical errors and grammatical errors. The analysis unit can also analyze the content of the input text using a generation AI (e.g., a text generation AI or a multimodal generation AI). The detection unit detects errors based on the text analyzed by the analysis unit. For example, the detection unit detects grammatical errors, spelling mistakes, contextual errors, etc. The detection unit can also identify the type and location of the error using the generation AI. The suggestion unit proposes corrections based on the errors detected by the detection unit. For example, the suggestion unit proposes corrections for typographical errors and grammatical corrections. The suggestion unit can also generate appropriate corrections using the generation AI. The presentation unit presents the corrections proposed by the suggestion unit to the user. For example, the presentation unit displays the corrections as a pop-up window or a tooltip. The presentation unit can also adjust the display method of suggested corrections using the generation AI. The learning unit learns the user's input patterns based on the text analyzed by the analysis unit. For example, the learning unit learns words and phrases frequently used by the user and performs predictive conversion the next time the user inputs text. The learning unit can also analyze and learn the user's input patterns using the generation AI. As a result, the keyboard application system according to the embodiment can analyze the user's input in real time, detect errors, suggest corrections, and present them to the user. For example, by quickly and accurately analyzing the text entered by the user and detecting errors, the user can efficiently input accurate text. The predictive conversion function also improves input efficiency and enhances user convenience.

[0030] During analysis, the analysis unit can adjust the analysis algorithm by referring to the user's past input history. For example, the analysis unit prioritizes analysis of words and phrases that the user has frequently used in the past. The analysis unit can also learn specific error patterns from the user's past input history and adjust the analysis algorithm. The analysis unit can also perform analysis by taking into account the context of text previously entered by the user. In this way, by referring to the user's past input history, the analysis algorithm can be optimized and analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past input history data into the generation AI and have the generation AI adjust the analysis algorithm.

[0031] During analysis, the analysis unit can apply different analysis methods depending on the category of the input text. For example, in the case of a business document, the analysis unit may prioritize analysis of formal grammar and technical terminology. In addition, in the case of a casual message, the analysis unit may prioritize analysis of colloquial expressions and slang. In addition, in the case of a technical document, the analysis unit may prioritize analysis of technical terminology and technical terminology. In this way, analysis accuracy is improved by applying an analysis method depending on the text category. 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 may input category data of the input text to the generation AI and have the generation AI apply the analysis method.

[0032] During analysis, the analysis unit can adjust the timing of analysis based on the user's input speed. For example, if the user is typing quickly, the analysis unit performs analysis in real time and provides immediate feedback. Furthermore, if the user is typing slowly, the analysis unit can perform detailed analysis and provide highly accurate feedback. Furthermore, if the user is typing intermittently, the analysis unit can perform analysis each time the input is completed. By adjusting the timing of analysis according to the user's input speed, analysis results can be provided at appropriate times. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input user input speed data to the generation AI and have the generation AI adjust the timing of analysis.

[0033] During analysis, the analysis unit can analyze region-specific expressions taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit analyzes dialects and expressions specific to that region. Furthermore, if the user is traveling, the analysis unit can analyze expressions specific to the region the user is visiting. Furthermore, if the user is in a different region, the analysis unit can analyze expressions based on the culture and customs of that region. This allows region-specific expressions to be appropriately analyzed by taking the user's geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to analyze region-specific expressions.

[0034] During analysis, the analysis unit can analyze the user's social media activity and prioritize analyzing related text. For example, the analysis unit can prioritize analyzing phrases and words that the user frequently uses on social media. The analysis unit can also analyze the content of the user's social media posts and prioritize analyzing related text. The analysis unit can also prioritize analyzing related text by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related text can be prioritized. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into the generation AI and cause the generation AI to analyze related text.

[0035] During analysis, the analysis unit can adjust the analysis algorithm by reflecting the user's past feedback. The analysis unit adjusts the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also learn specific error patterns from the user's past feedback and optimize the analysis algorithm. The analysis unit can also improve the analysis algorithm by reflecting problems previously pointed out by the user. In this way, the analysis algorithm is customized by reflecting the user's past feedback, and analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past feedback data into the generation AI and have the generation AI adjust the analysis algorithm.

[0036] During detection, the detection unit can improve error detection accuracy based on the context of the text. For example, the detection unit improves error detection accuracy by taking into account the context before and after the text. The detection unit can also identify the type of error based on the context of the text and improve detection accuracy. The detection unit can also analyze the context of the text and optimize the error detection algorithm. This improves error detection accuracy by taking the context of the text into consideration. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input text context data to the generation AI and cause the generation AI to improve error detection accuracy.

[0037] During detection, the detection unit can analyze the user's input pattern and adjust the error detection algorithm. For example, the detection unit analyzes the user's input pattern, learns specific error patterns, and optimizes the detection algorithm. The detection unit can also build an algorithm that prioritizes detecting errors frequently made by users. The detection unit can also improve the error detection accuracy based on the user's input pattern. In this way, by analyzing the user's input pattern, the error detection algorithm is optimized and the detection accuracy is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's input pattern data to the generation AI and cause the generation AI to adjust the error detection algorithm.

[0038] During detection, the detection unit can apply different error detection methods depending on the text category. For example, in the case of a business document, the detection unit may prioritize detecting errors in formal grammar and technical terminology. In addition, in the case of a casual message, the detection unit may prioritize detecting errors in colloquial expressions and slang. In addition, in the case of a technical document, the detection unit may prioritize detecting errors in technical terminology and technical terminology. In this way, detection accuracy is improved by applying an error detection method depending on the text category. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input text category data to the generation AI and cause the generation AI to apply the error detection method.

[0039] During detection, the detection unit can detect region-specific errors by taking into account the user's geographical location information. For example, if the user is in a specific region, the detection unit prioritizes detecting region-specific errors. Furthermore, if the user is traveling, the detection unit can also detect region-specific errors for the region the user is visiting. Furthermore, if the user is in a different region, the detection unit can detect errors based on the culture and customs of that region. This allows region-specific errors to be appropriately detected by taking the user's geographical location information into account. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's geographical location information data into the generation AI and cause the generation AI to detect region-specific errors.

[0040] During detection, the detection unit can analyze the user's social media activity and prioritize detecting related errors. For example, the detection unit can prioritize detecting errors in phrases and words that the user frequently uses on social media. The detection unit can also analyze the content of the user's social media posts and prioritize detecting related errors. The detection unit can also prioritize detecting related errors by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related errors can be prioritized. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's social media activity data into the generation AI and cause the generation AI to detect related errors.

[0041] The detection unit can adjust the error detection algorithm by reflecting the user's past feedback during detection. The detection unit adjusts the error detection algorithm based on, for example, feedback provided by the user in the past. The detection unit can also learn specific error patterns from the user's past feedback and optimize the error detection algorithm. The detection unit can also improve the error detection algorithm by reflecting problems previously pointed out by the user. In this way, the error detection algorithm is customized by reflecting the user's past feedback, and detection accuracy is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the error detection algorithm.

[0042] When making a suggestion, the suggestion unit can adjust the level of detail of the proposed correction based on the type of error. For example, in the case of a typographical error, the suggestion unit can provide a simple proposed correction. In the case of a grammatical error, the suggestion unit can also provide a proposed correction including a detailed explanation. In the case of a contextual error, the suggestion unit can also provide a proposed correction of the entire sentence. In this way, by adjusting the level of detail of the proposed correction based on the type of error, an appropriate proposed correction can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input error type data to a generation AI and cause the generation AI to adjust the level of detail of the proposed correction.

[0043] When making a suggestion, the suggestion unit can present different suggested revisions depending on the category of the text. For example, in the case of a business document, the suggestion unit can provide suggested revisions including formal expressions. In addition, in the case of a casual message, the suggestion unit can also provide suggested revisions including colloquial expressions. In addition, in the case of a technical document, the suggestion unit can also provide suggested revisions including technical terms. In this way, by presenting suggested revisions according to the category of the text, appropriate suggested revisions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input text category data to a generation AI and cause the generation AI to present suggested revisions.

[0044] When making a suggestion, the suggestion unit can improve the accuracy of the suggested revision by referring to the user's past revision history. For example, the suggestion unit provides an optimal suggested revision for a similar error based on revisions previously selected by the user. The suggestion unit can also learn specific revision patterns from the user's past revision history and improve the accuracy of the suggestions. The suggestion unit can also avoid revisions previously rejected by the user and provide more appropriate revisions. In this way, the accuracy of the suggested revisions is improved by referring to the user's past revision history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's past revision history data into the generation AI and cause the generation AI to improve the accuracy of the suggested revisions.

[0045] When making a proposal, the suggestion unit can determine the priority of correction proposals based on the time when the error occurred. For example, the suggestion unit can prioritize providing correction proposals for errors that have occurred recently. The suggestion unit can also prioritize providing correction proposals for errors that have been left uncorrected for a long period of time. The suggestion unit can also prioritize providing correction proposals for errors that the user frequently makes during a specific period of time. In this way, by prioritizing correction proposals based on the time when the error occurred, appropriate correction proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the time when the error occurred to the generation AI and cause the generation AI to determine the priority of correction proposals.

[0046] When making suggestions, the suggestion unit can adjust the order of suggested corrections based on the relevance of the errors. For example, the suggestion unit can prioritize providing suggested corrections for contextually important errors. The suggestion unit can also postpone providing suggested corrections for minor errors such as typos and omissions. The suggestion unit can also prioritize providing suggested corrections for grammatically significant errors. In this way, by adjusting the order of suggested corrections based on the relevance of the errors, appropriate suggested corrections can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input error relevance data to a generation AI and cause the generation AI to adjust the order of suggested corrections.

[0047] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposed revision depending on the user's level of expertise. For example, if the user is an expert, the suggestion unit can provide a proposed revision including technical terms. Furthermore, if the user is a beginner, the suggestion unit can also provide a proposed revision explained in simple terms. The suggestion unit can also estimate the user's level of expertise from their past input history and provide a proposed revision accordingly. This allows for the provision of an appropriate proposed revision by adjusting the use of technical terms in the proposed revision depending on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the proposed revision.

[0048] The presentation unit can select the optimal display method by referring to the user's past selection history when presenting the display. The presentation unit provides the optimal display method, for example, based on display methods previously selected by the user. The presentation unit can also learn specific display patterns from the user's past selection history and provide the optimal display method. The presentation unit can also avoid display methods previously rejected by the user and provide a more appropriate display method. In this way, the optimal display method can be provided by referring to the user's past selection history. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI or without AI. For example, the presentation unit can input the user's past selection history data into the generation AI and cause the generation AI to select the optimal display method.

[0049] The presentation unit can customize the display content according to the user's current task when presenting it. For example, if the user is creating a business document, the presentation unit can provide revision suggestions including formal expressions. Furthermore, if the user is creating a casual message, the presentation unit can provide revision suggestions including colloquial expressions. Furthermore, if the user is creating a technical document, the presentation unit can provide revision suggestions including technical terms. In this way, by customizing the display content according to the user's current task, appropriate display content can be provided. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's current task data into a generation AI and cause the generation AI to customize the display content.

[0050] The presentation unit can select the optimal display method by taking into consideration the user's device information when presenting the display. For example, if the user is using a smartphone, the presentation unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the presentation unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a display method that is simple and highly visible. This allows the optimal display method to be provided by taking into consideration the user's device information. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0051] The presentation unit can make the display content multilingual when presenting it according to the user's language setting. The presentation unit automatically sets the display content based on, for example, the language setting of the user's device. The presentation unit can also provide a language switching function when the user uses multiple languages. The presentation unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide appropriate display content by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's language setting data to the generation AI and cause the generation AI to set the display content to be multilingual.

[0052] The presentation unit can customize the display design based on the user's visual preferences when presenting the display design. The presentation unit customizes the display design based on, for example, the user's preferred color or font. The presentation unit can also learn specific design patterns from the user's past selection history and provide an optimal display design. The presentation unit can also avoid designs that the user has previously rejected and provide a more appropriate display design. In this way, the display design can be customized based on the user's visual preferences, thereby providing an appropriate display design. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's visual preference data into a generation AI and cause the generation AI to customize the display design.

[0053] The presentation unit can improve the display method by reflecting user feedback when presenting the display. The presentation unit adjusts the display method based on, for example, feedback provided by the user. The presentation unit can also learn specific display patterns from past user feedback and provide an optimal display method. The presentation unit can also improve the display method by reflecting problems previously pointed out by the user. In this way, the display method can be improved and an appropriate display method can be provided by reflecting user feedback. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI or without AI. For example, the presentation unit can input user feedback data into a generation AI and cause the generation AI to improve the display method.

[0054] During learning, the learning unit can adjust the learning algorithm by referring to past learning data. For example, the learning unit learns specific error patterns based on past learning data and optimizes the algorithm. The learning unit can also learn user input patterns from past learning data and optimize the algorithm. The learning unit can also analyze past learning data and select the most effective learning method. In this way, by referring to past learning data, the learning algorithm is optimized and learning accuracy is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and have the generation AI adjust the learning algorithm.

[0055] During learning, the learning unit can analyze variations in the user's input pattern and adjust the update frequency of the learning data. For example, if the user's input pattern changes frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's input pattern is stable, the learning unit can also decrease the update frequency of the learning data. Furthermore, the learning unit can analyze variations in the user's input pattern and set an optimal update frequency. In this way, by analyzing variations in the user's input pattern, the update frequency of the learning data can be appropriately adjusted. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user's input pattern data to a generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0056] During learning, the learning unit can integrate information from different data sources to expand the learning data. For example, the learning unit integrates social media posts into the learning data. The learning unit can also integrate the user's past input history into the learning data. The learning unit can also integrate information obtained from public databases into the learning data. In this way, by integrating information from different data sources, the learning data is expanded and the learning accuracy is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to integrate the learning data.

[0057] During learning, the learning unit can weight the training data based on the category of the input text. For example, in the case of a business document, the learning unit weights the training data by emphasizing formal expressions. In addition, in the case of a casual message, the learning unit can weight the training data by emphasizing colloquial expressions. In addition, in the case of a technical document, the learning unit can weight the training data by emphasizing technical terms. In this way, by weighting the training data based on the category of the input text, appropriate training data can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input category data of the input text to the generation AI and cause the generation AI to weight the training data.

[0058] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit adjusts the learning algorithm based on, for example, feedback provided by the user. The learning unit can also learn specific learning patterns from past user feedback and optimize the algorithm. The learning unit can also improve the learning algorithm by reflecting problems previously pointed out by the user. In this way, the learning algorithm is adjusted by reflecting user feedback, and learning accuracy is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0059] During learning, the learning unit can learn region-specific data by taking into account the user's geographical location information. For example, if the user is in a specific region, the learning unit learns expressions and errors specific to that region. Furthermore, if the user is traveling, the learning unit can learn data specific to the region the user visited. Furthermore, if the user is in a different region, the learning unit can learn data based on the culture and customs of that region. This allows region-specific data to be appropriately learned by taking the user's geographical location information into account. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the user's geographical location information data into the generation AI and cause the generation AI to learn region-specific data.

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

[0061] The analysis unit can retrieve information from related external databases based on keywords included in the user's input text and reflect this in the analysis results. For example, if the user inputs "latest technology trends," the analysis unit can retrieve related technical articles and papers from external databases and reflect this in the analysis results. Also, if the user inputs "recommended travel spots," the analysis unit can retrieve information from travel guides and review sites and reflect this in the analysis results. Furthermore, if the user inputs "healthy meals," the analysis unit can retrieve information from a nutritional database and reflect this in the analysis results. In this way, the accuracy of the analysis results can be improved by retrieving information from external databases related to the user's input text.

[0062] The detection unit can improve the accuracy of error detection based on the context included in the user's input text. For example, if a user inputs "what was discussed in the meeting yesterday," the detection unit detects errors by taking into account the context before and after. Also, if a user inputs "plans for the next project," the detection unit can detect appropriate errors based on the context. Furthermore, if a user inputs "reviews of new products," the detection unit can also detect errors by analyzing the context. In this way, the accuracy of error detection can be improved by taking into account the context of the user's input text.

[0063] The suggestion unit can provide relevant revision suggestions based on keywords included in the user's input text. For example, if the user inputs "presentation materials," the suggestion unit can provide revision suggestions related to presentations. Also, if the user inputs "replying to emails," the suggestion unit can provide revision suggestions related to replying to emails. Furthermore, if the user inputs "writing reports," the suggestion unit can provide revision suggestions related to reports. In this way, by providing relevant revision suggestions based on keywords included in the user's input text, appropriate revision suggestions can be provided.

[0064] The presentation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. In this way, the optimal display method can be provided by taking into account the user's device information.

[0065] The learning unit can integrate information from different data sources to enrich the training data. For example, social media posts can be integrated into the training data. It can also integrate a user's past input history into the training data. Furthermore, it can integrate information obtained from public databases into the training data. By integrating information from different data sources, the training data can be enriched and the learning accuracy can be improved.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The analyzer analyzes the text entered by the user in real time. For example, the analyzer uses natural language processing technology to detect typos and grammatical errors. It can also use generative AI to analyze the content of the input text. Step 2: The detection unit detects errors based on the text analyzed by the analysis unit, such as grammatical errors, spelling mistakes, and contextual errors, and can also use generation AI to identify the type and location of the errors. Step 3: The suggestion unit proposes corrections based on the errors detected by the detection unit. For example, it can propose corrections for typos or grammar, and can generate appropriate corrections using a generative AI. Step 4: The suggestion unit presents the proposed revisions to the user. For example, the suggestion unit may display the revisions as a pop-up window or a tooltip, and the generation AI may adjust how the revisions are displayed. Step 5: The learning unit learns the user's input patterns based on the text analyzed by the analysis unit. For example, it learns the user's frequently used words and phrases and performs predictive conversion the next time the user enters text. It is also possible to use a generation AI to analyze and learn the user's input patterns.

[0068] (Example 2) A keyboard application system according to an embodiment of the present invention analyzes text entered by a user in real time, detects errors, suggests corrections, and presents them to the user. The keyboard application system analyzes text entered by a user in real time, detects typographical errors and grammatical errors, and suggests appropriate corrections. For example, if a user enters "hello," the keyboard application system suggests "hello." The keyboard application system can also learn the user's input patterns and perform predictive conversion the next time the user enters text. This allows the user to efficiently enter accurate text. For example, the keyboard application system analyzes text entered by a user in real time and detects typographical errors and grammatical errors. The keyboard application system then suggests appropriate corrections based on the detected errors. The keyboard application system can also learn the user's input patterns and perform predictive conversion the next time the user enters text. This allows the user to reduce typographical errors and grammatical errors and enter accurate text. The predictive conversion function also improves input efficiency and enhances user convenience. For example, if a user attempts to enter "good morning," the keyboard application system can suggest predictive conversion such as "good morning." This allows users to input text accurately in a short amount of time. This allows the keyboard application system to analyze the user's input in real time, detect errors, suggest corrections, and present them to the user. For example, by quickly and accurately analyzing the text entered by the user and detecting errors, the user can input accurate text efficiently. In addition, the predictive conversion function improves input efficiency and increases user convenience.

[0069] A keyboard application system according to an embodiment includes an analysis unit, a detection unit, a suggestion unit, a presentation unit, and a learning unit. The analysis unit analyzes text entered by a user in real time. For example, the analysis unit analyzes the text entered by the user using natural language processing technology to detect typographical errors and grammatical errors. The analysis unit can also analyze the content of the input text using a generation AI (e.g., a text generation AI or a multimodal generation AI). The detection unit detects errors based on the text analyzed by the analysis unit. For example, the detection unit detects grammatical errors, spelling mistakes, contextual errors, etc. The detection unit can also identify the type and location of the error using the generation AI. The suggestion unit proposes corrections based on the errors detected by the detection unit. For example, the suggestion unit proposes corrections for typographical errors and grammatical corrections. The suggestion unit can also generate appropriate corrections using the generation AI. The presentation unit presents the corrections proposed by the suggestion unit to the user. For example, the presentation unit displays the corrections as a pop-up window or a tooltip. The presentation unit can also adjust the display method of suggested corrections using the generation AI. The learning unit learns the user's input patterns based on the text analyzed by the analysis unit. For example, the learning unit learns words and phrases frequently used by the user and performs predictive conversion the next time the user inputs text. The learning unit can also analyze and learn the user's input patterns using the generation AI. As a result, the keyboard application system according to the embodiment can analyze the user's input in real time, detect errors, suggest corrections, and present them to the user. For example, by quickly and accurately analyzing the text entered by the user and detecting errors, the user can efficiently input accurate text. The predictive conversion function also improves input efficiency and enhances user convenience.

[0070] The analysis unit estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. For example, when the user is stressed, the analysis unit increases the accuracy of the analysis to quickly detect errors. Furthermore, when the user is relaxed, the analysis unit can slightly relax the accuracy of the analysis to prioritize natural input. Furthermore, when the user is in a hurry, the analysis unit can prioritize analysis speed and quickly detect errors. This allows for more appropriate analysis results to be provided by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0071] During analysis, the analysis unit can adjust the analysis algorithm by referring to the user's past input history. For example, the analysis unit prioritizes analysis of words and phrases that the user has frequently used in the past. The analysis unit can also learn specific error patterns from the user's past input history and adjust the analysis algorithm. The analysis unit can also perform analysis by taking into account the context of text previously entered by the user. In this way, by referring to the user's past input history, the analysis algorithm can be optimized and analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past input history data into the generation AI and have the generation AI adjust the analysis algorithm.

[0072] During analysis, the analysis unit can apply different analysis methods depending on the category of the input text. For example, in the case of a business document, the analysis unit may prioritize analysis of formal grammar and technical terminology. In addition, in the case of a casual message, the analysis unit may prioritize analysis of colloquial expressions and slang. In addition, in the case of a technical document, the analysis unit may prioritize analysis of technical terminology and technical terminology. In this way, analysis accuracy is improved by applying an analysis method depending on the text category. 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 may input category data of the input text to the generation AI and have the generation AI apply the analysis method.

[0073] During analysis, the analysis unit can adjust the timing of analysis based on the user's input speed. For example, if the user is typing quickly, the analysis unit performs analysis in real time and provides immediate feedback. Furthermore, if the user is typing slowly, the analysis unit can perform detailed analysis and provide highly accurate feedback. Furthermore, if the user is typing intermittently, the analysis unit can perform analysis each time the input is completed. By adjusting the timing of analysis according to the user's input speed, analysis results can be provided at appropriate times. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input user input speed data to the generation AI and have the generation AI adjust the timing of analysis.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. By adjusting the display method of the analysis results according to the user's emotions, the optimal display method for the user can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0075] During analysis, the analysis unit can analyze region-specific expressions taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit analyzes dialects and expressions specific to that region. Furthermore, if the user is traveling, the analysis unit can analyze expressions specific to the region the user is visiting. Furthermore, if the user is in a different region, the analysis unit can analyze expressions based on the culture and customs of that region. This allows region-specific expressions to be appropriately analyzed by taking the user's geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to analyze region-specific expressions.

[0076] During analysis, the analysis unit can analyze the user's social media activity and prioritize analyzing related text. For example, the analysis unit can prioritize analyzing phrases and words that the user frequently uses on social media. The analysis unit can also analyze the content of the user's social media posts and prioritize analyzing related text. The analysis unit can also prioritize analyzing related text by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related text can be prioritized. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into the generation AI and cause the generation AI to analyze related text.

[0077] During analysis, the analysis unit can adjust the analysis algorithm by reflecting the user's past feedback. The analysis unit adjusts the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also learn specific error patterns from the user's past feedback and optimize the analysis algorithm. The analysis unit can also improve the analysis algorithm by reflecting problems previously pointed out by the user. In this way, the analysis algorithm is customized by reflecting the user's past feedback, and analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past feedback data into the generation AI and have the generation AI adjust the analysis algorithm.

[0078] The detection unit can estimate the user's emotions and adjust error detection criteria based on the estimated user emotions. For example, when the user is stressed, the detection unit can tighten the error detection criteria to quickly detect errors. Furthermore, when the user is relaxed, the detection unit can loosen the error detection criteria to prioritize natural input. Furthermore, when the user is in a hurry, the detection unit can prioritize error detection speed and quickly detect errors. This enables more appropriate error detection by adjusting the error detection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, an AI, or without an AI. For example, the detection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the error detection criteria.

[0079] During detection, the detection unit can improve error detection accuracy based on the context of the text. For example, the detection unit improves error detection accuracy by taking into account the context before and after the text. The detection unit can also identify the type of error based on the context of the text and improve detection accuracy. The detection unit can also analyze the context of the text and optimize the error detection algorithm. This improves error detection accuracy by taking the context of the text into consideration. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input text context data to the generation AI and cause the generation AI to improve error detection accuracy.

[0080] During detection, the detection unit can analyze the user's input pattern and adjust the error detection algorithm. For example, the detection unit analyzes the user's input pattern, learns specific error patterns, and optimizes the detection algorithm. The detection unit can also build an algorithm that prioritizes detecting errors frequently made by users. The detection unit can also improve the error detection accuracy based on the user's input pattern. In this way, by analyzing the user's input pattern, the error detection algorithm is optimized and the detection accuracy is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's input pattern data to the generation AI and cause the generation AI to adjust the error detection algorithm.

[0081] During detection, the detection unit can apply different error detection methods depending on the text category. For example, in the case of a business document, the detection unit may prioritize detecting errors in formal grammar and technical terminology. In addition, in the case of a casual message, the detection unit may prioritize detecting errors in colloquial expressions and slang. In addition, in the case of a technical document, the detection unit may prioritize detecting errors in technical terminology and technical terminology. In this way, detection accuracy is improved by applying an error detection method depending on the text category. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input text category data to the generation AI and cause the generation AI to apply the error detection method.

[0082] The detection unit can estimate the user's emotions and adjust the importance of errors based on the estimated user emotions. For example, when the user is stressed, the detection unit prioritizes detecting errors with high importance. Furthermore, when the user is relaxed, the detection unit can also detect errors with low importance. Furthermore, when the user is in a hurry, the detection unit can quickly detect errors with high importance. By adjusting the importance of errors according to the user's emotions, it is possible to prioritize the detection of important errors. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the importance of errors.

[0083] During detection, the detection unit can detect region-specific errors by taking into account the user's geographical location information. For example, if the user is in a specific region, the detection unit prioritizes detecting region-specific errors. Furthermore, if the user is traveling, the detection unit can also detect region-specific errors for the region the user is visiting. Furthermore, if the user is in a different region, the detection unit can detect errors based on the culture and customs of that region. This allows region-specific errors to be appropriately detected by taking the user's geographical location information into account. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's geographical location information data into the generation AI and cause the generation AI to detect region-specific errors.

[0084] During detection, the detection unit can analyze the user's social media activity and prioritize detecting related errors. For example, the detection unit can prioritize detecting errors in phrases and words that the user frequently uses on social media. The detection unit can also analyze the content of the user's social media posts and prioritize detecting related errors. The detection unit can also prioritize detecting related errors by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related errors can be prioritized. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's social media activity data into the generation AI and cause the generation AI to detect related errors.

[0085] The detection unit can adjust the error detection algorithm by reflecting the user's past feedback during detection. The detection unit adjusts the error detection algorithm based on, for example, feedback provided by the user in the past. The detection unit can also learn specific error patterns from the user's past feedback and optimize the error detection algorithm. The detection unit can also improve the error detection algorithm by reflecting problems previously pointed out by the user. In this way, the error detection algorithm is customized by reflecting the user's past feedback, and detection accuracy is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the error detection algorithm.

[0086] The suggestion unit can estimate the user's emotions and adjust the expression of the proposed revision based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide a simple and easy-to-understand revision. Furthermore, if the user is relaxed, the suggestion unit can provide a revision with detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can provide a revision that can be quickly selected. This allows the optimal revision to be provided by adjusting the expression of the proposed revision according to the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression of the proposed revision.

[0087] When making a suggestion, the suggestion unit can adjust the level of detail of the proposed correction based on the type of error. For example, in the case of a typographical error, the suggestion unit can provide a simple proposed correction. In the case of a grammatical error, the suggestion unit can also provide a proposed correction including a detailed explanation. In the case of a contextual error, the suggestion unit can also provide a proposed correction of the entire sentence. In this way, by adjusting the level of detail of the proposed correction based on the type of error, an appropriate proposed correction can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input error type data to a generation AI and cause the generation AI to adjust the level of detail of the proposed correction.

[0088] When making a suggestion, the suggestion unit can present different suggested revisions depending on the category of the text. For example, in the case of a business document, the suggestion unit can provide suggested revisions including formal expressions. In addition, in the case of a casual message, the suggestion unit can also provide suggested revisions including colloquial expressions. In addition, in the case of a technical document, the suggestion unit can also provide suggested revisions including technical terms. In this way, by presenting suggested revisions according to the category of the text, appropriate suggested revisions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input text category data to a generation AI and cause the generation AI to present suggested revisions.

[0089] When making a suggestion, the suggestion unit can improve the accuracy of the suggested revision by referring to the user's past revision history. For example, the suggestion unit provides an optimal suggested revision for a similar error based on revisions previously selected by the user. The suggestion unit can also learn specific revision patterns from the user's past revision history and improve the accuracy of the suggestions. The suggestion unit can also avoid revisions previously rejected by the user and provide more appropriate revisions. In this way, the accuracy of the suggested revisions is improved by referring to the user's past revision history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's past revision history data into the generation AI and cause the generation AI to improve the accuracy of the suggested revisions.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of the suggested revisions based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can provide short and concise revisions. If the user is relaxed, the suggestion unit can also provide longer revisions with detailed explanations. If the user is in a hurry, the suggestion unit can also provide short revisions that can be quickly selected. This allows the length of the revisions to be adjusted according to the user's emotions, thereby providing the optimal revisions for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the revisions.

[0091] When making a proposal, the suggestion unit can determine the priority of correction proposals based on the time when the error occurred. For example, the suggestion unit can prioritize providing correction proposals for errors that have occurred recently. The suggestion unit can also prioritize providing correction proposals for errors that have been left uncorrected for a long period of time. The suggestion unit can also prioritize providing correction proposals for errors that the user frequently makes during a specific period of time. In this way, by prioritizing correction proposals based on the time when the error occurred, appropriate correction proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the time when the error occurred to the generation AI and cause the generation AI to determine the priority of correction proposals.

[0092] When making suggestions, the suggestion unit can adjust the order of suggested corrections based on the relevance of the errors. For example, the suggestion unit can prioritize providing suggested corrections for contextually important errors. The suggestion unit can also postpone providing suggested corrections for minor errors such as typos and omissions. The suggestion unit can also prioritize providing suggested corrections for grammatically significant errors. In this way, by adjusting the order of suggested corrections based on the relevance of the errors, appropriate suggested corrections can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input error relevance data to a generation AI and cause the generation AI to adjust the order of suggested corrections.

[0093] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposed revision depending on the user's level of expertise. For example, if the user is an expert, the suggestion unit can provide a proposed revision including technical terms. Furthermore, if the user is a beginner, the suggestion unit can also provide a proposed revision explained in simple terms. The suggestion unit can also estimate the user's level of expertise from their past input history and provide a proposed revision accordingly. This allows for the provision of an appropriate proposed revision by adjusting the use of technical terms in the proposed revision depending on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the proposed revision.

[0094] The presentation unit can estimate the user's emotions and adjust the display method of the revision suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the presentation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the presentation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a display method that focuses on the main points. By adjusting the display method of the revision suggestions according to the user's emotions, the optimal display method for the user can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the revision suggestions.

[0095] The presentation unit can select the optimal display method by referring to the user's past selection history when presenting the display. The presentation unit provides the optimal display method, for example, based on display methods previously selected by the user. The presentation unit can also learn specific display patterns from the user's past selection history and provide the optimal display method. The presentation unit can also avoid display methods previously rejected by the user and provide a more appropriate display method. In this way, the optimal display method can be provided by referring to the user's past selection history. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI or without AI. For example, the presentation unit can input the user's past selection history data into the generation AI and cause the generation AI to select the optimal display method.

[0096] The presentation unit can customize the display content according to the user's current task when presenting it. For example, if the user is creating a business document, the presentation unit can provide revision suggestions including formal expressions. Furthermore, if the user is creating a casual message, the presentation unit can provide revision suggestions including colloquial expressions. Furthermore, if the user is creating a technical document, the presentation unit can provide revision suggestions including technical terms. In this way, by customizing the display content according to the user's current task, appropriate display content can be provided. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's current task data into a generation AI and cause the generation AI to customize the display content.

[0097] The presentation unit can select the optimal display method by taking into consideration the user's device information when presenting the display. For example, if the user is using a smartphone, the presentation unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the presentation unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a display method that is simple and highly visible. This allows the optimal display method to be provided by taking into consideration the user's device information. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0098] The presentation unit can estimate the user's emotions and adjust the suggested operation procedures based on the estimated user emotions. For example, if the user is feeling stressed, the presentation unit can provide simple operation procedures. If the user is relaxed, the presentation unit can also provide detailed operation procedures. If the user is in a hurry, the presentation unit can also provide quick operation procedures. This allows the suggested operation procedures to be adjusted according to the user's emotions, thereby providing the optimal operation procedures for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the presentation unit may be performed using an AI, for example, or without an AI. For example, the presentation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the operation procedures.

[0099] The presentation unit can make the display content multilingual when presenting it according to the user's language setting. The presentation unit automatically sets the display content based on, for example, the language setting of the user's device. The presentation unit can also provide a language switching function when the user uses multiple languages. The presentation unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide appropriate display content by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's language setting data to the generation AI and cause the generation AI to set the display content to be multilingual.

[0100] The presentation unit can customize the display design based on the user's visual preferences when presenting the display design. The presentation unit customizes the display design based on, for example, the user's preferred color or font. The presentation unit can also learn specific design patterns from the user's past selection history and provide an optimal display design. The presentation unit can also avoid designs that the user has previously rejected and provide a more appropriate display design. In this way, the display design can be customized based on the user's visual preferences, thereby providing an appropriate display design. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's visual preference data into a generation AI and cause the generation AI to customize the display design.

[0101] The presentation unit can improve the display method by reflecting user feedback when presenting the display. The presentation unit adjusts the display method based on, for example, feedback provided by the user. The presentation unit can also learn specific display patterns from past user feedback and provide an optimal display method. The presentation unit can also improve the display method by reflecting problems previously pointed out by the user. In this way, the display method can be improved and an appropriate display method can be provided by reflecting user feedback. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI or without AI. For example, the presentation unit can input user feedback data into a generation AI and cause the generation AI to improve the display method.

[0102] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can prioritize simple training data. The learning unit can also select detailed training data if the user is relaxed. The learning unit can also select data that can be quickly learned if the user is in a hurry. This allows appropriate training data to be provided by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI select the training data.

[0103] During learning, the learning unit can adjust the learning algorithm by referring to past learning data. For example, the learning unit learns specific error patterns based on past learning data and optimizes the algorithm. The learning unit can also learn user input patterns from past learning data and optimize the algorithm. The learning unit can also analyze past learning data and select the most effective learning method. In this way, by referring to past learning data, the learning algorithm is optimized and learning accuracy is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and have the generation AI adjust the learning algorithm.

[0104] During learning, the learning unit can analyze variations in the user's input pattern and adjust the update frequency of the learning data. For example, if the user's input pattern changes frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's input pattern is stable, the learning unit can also decrease the update frequency of the learning data. Furthermore, the learning unit can analyze variations in the user's input pattern and set an optimal update frequency. In this way, by analyzing variations in the user's input pattern, the update frequency of the learning data can be appropriately adjusted. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user's input pattern data to a generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0105] During learning, the learning unit can integrate information from different data sources to expand the learning data. For example, the learning unit integrates social media posts into the learning data. The learning unit can also integrate the user's past input history into the learning data. The learning unit can also integrate information obtained from public databases into the learning data. In this way, by integrating information from different data sources, the learning data is expanded and the learning accuracy is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to integrate the learning data.

[0106] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. For example, the learning unit can decrease the learning frequency when the user is stressed. The learning unit can also increase the learning frequency when the user is relaxed. The learning unit can also adjust the learning frequency to speed up learning when the user is in a hurry. This allows for an appropriate learning frequency to be provided by adjusting the learning frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI adjust the learning frequency.

[0107] During learning, the learning unit can weight the training data based on the category of the input text. For example, in the case of a business document, the learning unit weights the training data by emphasizing formal expressions. In addition, in the case of a casual message, the learning unit can weight the training data by emphasizing colloquial expressions. In addition, in the case of a technical document, the learning unit can weight the training data by emphasizing technical terms. In this way, by weighting the training data based on the category of the input text, appropriate training data can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input category data of the input text to the generation AI and cause the generation AI to weight the training data.

[0108] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit adjusts the learning algorithm based on, for example, feedback provided by the user. The learning unit can also learn specific learning patterns from past user feedback and optimize the algorithm. The learning unit can also improve the learning algorithm by reflecting problems previously pointed out by the user. In this way, the learning algorithm is adjusted by reflecting user feedback, and learning accuracy is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0109] During learning, the learning unit can learn region-specific data by taking into account the user's geographical location information. For example, if the user is in a specific region, the learning unit learns expressions and errors specific to that region. Furthermore, if the user is traveling, the learning unit can learn data specific to the region the user visited. Furthermore, if the user is in a different region, the learning unit can learn data based on the culture and customs of that region. This allows region-specific data to be appropriately learned by taking the user's geographical location information into account. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the user's geographical location information data into the generation AI and cause the generation AI to learn region-specific data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, detection unit, suggestion unit, presentation unit, and learning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The presentation unit is realized, for example, by the output device 40 of the smart device 14. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, detection unit, suggestion unit, presentation unit, and learning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The presentation unit is realized, for example, by the speaker 240 of the smart glasses 214. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, detection unit, proposal unit, presentation unit, and learning unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The presentation unit is realized, for example, by the display 343 of the headset type terminal 314. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, detection unit, proposal unit, presentation unit, and learning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The presentation unit is realized, for example, by the speaker 240 of the robot 414. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The analysis unit can estimate the emotion contained in the user's input text and adjust the analysis priority based on the estimated emotion. For example, if the user expresses anger, the analysis unit can quickly detect errors and immediately suggest corrections. Alternatively, if the user expresses sadness, the analysis unit can prioritize error detection and provide concise corrections to reduce the user's burden. Furthermore, if the user expresses joy, the analysis unit can perform a more detailed analysis and provide more accurate corrections. This allows for more appropriate analysis results to be provided by adjusting the analysis priority according to the user's emotion.

[0112] The detection unit can estimate the emotion contained in the user's input text and adjust the error detection criteria based on the estimated emotion. For example, if the user is feeling stressed, the error detection criteria can be tightened to quickly detect errors. Alternatively, if the user is relaxed, the error detection criteria can be loosened to prioritize natural input. Furthermore, if the user is in a hurry, the speed of error detection can be prioritized to quickly detect errors. In this way, adjusting the error detection criteria according to the user's emotion enables more appropriate error detection.

[0113] The suggestion unit can estimate the user's emotions and adjust the way in which the suggested revisions are expressed based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-understand suggested revision can be provided. If the user is relaxed, a suggested revision with detailed explanations can be provided. Furthermore, if the user is in a hurry, a suggested revision that can be quickly selected can be provided. In this way, by adjusting the way in which the suggested revisions are expressed according to the user's emotions, the system can provide the most suitable suggested revisions for the user.

[0114] The presentation unit can estimate the user's emotions and adjust the display method of the revision suggestions based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the revision suggestions according to the user's emotions, it is possible to provide the optimal display method for the user.

[0115] The learning unit can estimate the user's emotions and select learning data based on the estimated emotions. For example, if the user is feeling stressed, simple learning data can be selected preferentially. If the user is relaxed, detailed learning data can be selected. Furthermore, if the user is in a hurry, data that can be learned quickly can be selected. In this way, appropriate learning data can be provided by selecting learning data according to the user's emotions.

[0116] The analysis unit can retrieve information from related external databases based on keywords included in the user's input text and reflect this in the analysis results. For example, if the user inputs "latest technology trends," the analysis unit can retrieve related technical articles and papers from external databases and reflect this in the analysis results. Also, if the user inputs "recommended travel spots," the analysis unit can retrieve information from travel guides and review sites and reflect this in the analysis results. Furthermore, if the user inputs "healthy meals," the analysis unit can retrieve information from a nutritional database and reflect this in the analysis results. In this way, the accuracy of the analysis results can be improved by retrieving information from external databases related to the user's input text.

[0117] The detection unit can improve the accuracy of error detection based on the context included in the user's input text. For example, if a user inputs "what was discussed in the meeting yesterday," the detection unit detects errors by taking into account the context before and after. Also, if a user inputs "plans for the next project," the detection unit can detect appropriate errors based on the context. Furthermore, if a user inputs "reviews of new products," the detection unit can also detect errors by analyzing the context. In this way, the accuracy of error detection can be improved by taking into account the context of the user's input text.

[0118] The suggestion unit can provide relevant revision suggestions based on keywords included in the user's input text. For example, if the user inputs "presentation materials," the suggestion unit can provide revision suggestions related to presentations. Also, if the user inputs "replying to emails," the suggestion unit can provide revision suggestions related to replying to emails. Furthermore, if the user inputs "writing reports," the suggestion unit can provide revision suggestions related to reports. In this way, by providing relevant revision suggestions based on keywords included in the user's input text, appropriate revision suggestions can be provided.

[0119] The presentation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. In this way, the optimal display method can be provided by taking into account the user's device information.

[0120] The learning unit can integrate information from different data sources to enrich the training data. For example, social media posts can be integrated into the training data. It can also integrate a user's past input history into the training data. Furthermore, it can integrate information obtained from public databases into the training data. By integrating information from different data sources, the training data can be enriched and the learning accuracy can be improved.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The analyzer analyzes the text entered by the user in real time. For example, the analyzer uses natural language processing technology to detect typos and grammatical errors. It can also use generative AI to analyze the content of the input text. Step 2: The detection unit detects errors based on the text analyzed by the analysis unit, such as grammatical errors, spelling mistakes, and contextual errors, and can also use generation AI to identify the type and location of the errors. Step 3: The suggestion unit proposes corrections based on the errors detected by the detection unit. For example, it can propose corrections for typos or grammar, and can generate appropriate corrections using a generative AI. Step 4: The suggestion unit presents the proposed revisions to the user. For example, the suggestion unit may display the revisions as a pop-up window or a tooltip, and the generation AI may adjust how the revisions are displayed. Step 5: The learning unit learns the user's input patterns based on the text analyzed by the analysis unit. For example, it learns the user's frequently used words and phrases and performs predictive conversion the next time the user enters text. It is also possible to use a generation AI to analyze and learn the user's input patterns.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] 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.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] 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.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] 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.

[0168] 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.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] 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.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] 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.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes text entered by a user in real time; a detection unit that detects errors based on the text analyzed by the analysis unit; a suggestion unit that proposes a correction plan based on the error detected by the detection unit; a presentation unit that presents the proposed revision to a user; a learning unit that learns a user's input pattern based on the text analyzed by the analysis unit. A system characterized by:

2. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The system of claim 1 .

3. The analysis unit During analysis, the analysis algorithm is adjusted by referring to the user's past input history. The system of claim 1 .

4. The analysis unit During analysis, different analysis methods are applied depending on the category of the input text. The system of claim 1 .

5. The analysis unit During analysis, adjust the timing of the analysis based on the user's input speed The system of claim 1 .

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

7. The analysis unit At parse time, parse region-specific expressions based on the user's geographic location. The system of claim 1 .

8. The analysis unit During analysis, the system analyzes the user's social media activity and prioritizes relevant text. The system of claim 1 .

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