system

Abstract

The system according to this embodiment aims to provide support for accurately conveying the user's intentions and emotions. [Solution] The system according to the embodiment comprises an analysis unit, a suggestion unit, a feedback unit, a sharing unit, and a history analysis unit. The analysis unit analyzes the user's text in real time. The suggestion unit suggests the most appropriate words and expressions based on the results analyzed by the analysis unit. The feedback unit provides feedback to the user on the words and expressions suggested by the suggestion unit. The sharing unit shares the results analyzed by the analysis unit. The history analysis unit analyzes past communication history and suggests areas for improvement.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is not enough support for accurately conveying the user's intention and emotion, and there is room for improvement in clarifying communication.

[0005] The system according to the embodiment aims to provide support for accurately conveying the user's intention and emotion.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a suggestion unit, a feedback unit, a sharing unit, and a history analysis unit. The analysis unit analyzes the user's text in real time. The suggestion unit suggests the most appropriate words and expressions based on the results analyzed by the analysis unit. The feedback unit provides the user with feedback on the words and expressions suggested by the suggestion unit. The sharing unit shares the results analyzed by the analysis unit. The history analysis unit analyzes past communication history and suggests areas for improvement. [Effects of the Invention]

[0007] The system according to this embodiment can provide support for accurately conveying the user's intentions and emotions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​tool according to an embodiment of the present invention is a system that assists in accurately conveying the user's intentions and emotions in conversations and text communications. This system aims to clarify communication and resolve the problem of misunderstandings by applying natural language processing technology to understand the context and select the most appropriate words. For example, when a user engages in text communication, the AI ​​tool analyzes the text in real time. This includes texts such as emails, social media posts, and chat messages. In this process, the AI ​​tool understands the context and analyzes the user's intentions and emotions. Next, based on the analysis results, the AI ​​tool suggests the most appropriate words and expressions. For example, if a user types "When will the document be ready?", the AI ​​tool analyzes the context and determines whether the other person is angry or simply checking. It then suggests an appropriate reply, such as "It seems they are checking out of concern." Furthermore, the AI ​​tool also points out unclear parts of the text sent by the user and suggests improvements. For example, if a user types "What is the status of this project?", the AI ​​tool determines that the sentence is ambiguous and suggests "Let's clarify which specific part you want to know about." In this way, the AI ​​tool supports users' text communication in real time, helping to accurately convey intentions and emotions. It also has the function to analyze past communication history and suggest areas for improvement. This allows users to improve their communication skills. This AI tool is particularly effective in business settings and supports professional communication. For example, it can enable effective communication in companies of all sizes, from large corporations to small and medium-sized enterprises, where text communication with customers is important. It can also be used by individual users to improve communication on social media and chat messages. Thus, the AI ​​tool of this invention aims to improve the quality of text communication by helping to accurately convey users' intentions and emotions.This allows AI tools to analyze users' text communications in real time, suggest optimal words and expressions, and provide feedback, thereby improving the quality of communication.

[0029] The AI ​​tool according to this embodiment comprises an analysis unit, a suggestion unit, a feedback unit, a sharing unit, and a history analysis unit. The analysis unit analyzes the user's text in real time. The analysis unit analyzes text such as emails, social media posts, and chat messages in real time. The analysis unit uses natural language processing technology to understand the context of the text and analyze the user's intent and emotions. For example, the analysis unit analyzes the content of the text and understands what the user wants to convey. The analysis unit also analyzes the user's emotions and determines what kind of emotions are associated with the text. The suggestion unit proposes the most appropriate words and expressions based on the results analyzed by the analysis unit. For example, if the user inputs "When will the document be ready?", the suggestion unit, based on the results of the analysis unit, determines whether the other party is angry or simply checking, and proposes an appropriate reply. For example, the suggestion unit might suggest, "It seems they are checking out of concern." The suggestion unit also points out parts of the text sent by the user that are difficult to understand and proposes improvements. For example, if a user enters "What is the status of this project?", the suggestion unit will determine that the sentence is ambiguous and make a suggestion such as "Let's clarify which specific part you want to know about." The feedback unit provides feedback to the user on the words and expressions suggested by the suggestion unit. The feedback unit, for example, displays the suggested words and expressions to the user so that the user can confirm them. The feedback unit can also check whether the user accepts the suggestion and make a revised suggestion if necessary. The sharing unit shares the results analyzed by the analysis unit with other elements. For example, the sharing unit shares the analysis results with the suggestion unit and the feedback unit to strengthen the overall system coordination. The history analysis unit analyzes past communication history and suggests areas for improvement. For example, the history analysis unit analyzes the user's past communication history and suggests what aspects should be improved. As a result, the AI ​​tool according to the embodiment can improve the quality of communication by analyzing the user's text communication in real time, suggesting the most appropriate words and expressions, and providing feedback.

[0030] The analysis unit analyzes user text in real time. For example, it analyzes text from emails, social media posts, and chat messages in real time. Using natural language processing technology, the analysis unit understands the context of the text and analyzes the user's intent and emotions. Specifically, the analysis unit performs morphological and grammatical analysis to analyze the content of the text and understand what the user wants to convey. Morphological analysis breaks down the text into individual words and identifies the part of speech and meaning of each word. Grammatical analysis analyzes the structure of the sentence and clarifies the relationships between subjects, predicates, and objects. Furthermore, the analysis unit uses sentiment analysis technology to determine what emotions are associated with the text. Sentiment analysis classifies emotions into categories such as positive, negative, and neutral to understand the user's emotional state. For example, if a user enters "I'm so happy today!", the analysis unit analyzes this text as a positive emotion. Conversely, if a user enters "I'm so tired," the analysis unit analyzes it as a negative emotion. This allows the analysis unit to accurately grasp the user's intentions and emotions, and provide the necessary information to the next steps, the proposal unit and feedback unit.

[0031] The suggestion department proposes the most appropriate words and expressions based on the results analyzed by the analysis department. For example, if a user inputs "When will the document be ready?", the suggestion department will determine, based on the analysis department's results, whether the other party is angry or simply checking, and propose an appropriate reply. Specifically, the suggestion department utilizes generative AI to generate appropriate reply candidates based on the user's input text and the analysis results. Generative AI has the ability to learn from large amounts of text data and generate natural sentences appropriate to the context. For example, if a user inputs "When will the document be ready?", the suggestion department uses the generative AI to suggest something like, "It seems they are checking out of concern." The suggestion department also points out unclear parts of the messages sent by users and suggests improvements. For example, if a user inputs "What is the status of this project?", the suggestion department will determine that the sentence is ambiguous and suggest, "Let's clarify which specific part you want to know about." In this way, the suggestion department can facilitate user communication and prevent misunderstandings and problems. Furthermore, the suggestion department can also provide more personalized suggestions by referring to the user's past communication history. This allows the proposal department to provide the most appropriate words and expressions to meet user needs, thereby improving the quality of communication.

[0032] The feedback unit provides users with feedback on the words and expressions proposed by the proposal unit. For example, the feedback unit displays the proposed words and expressions to the user, allowing the user to review them. Specifically, the feedback unit visually displays the proposed content through the user interface, allowing the user to choose whether to accept the proposal. For example, if a user enters "What is the status of this project?", the feedback unit will display a pop-up from the proposal unit suggesting "Let's clarify which specific parts you want to know about," and ask the user whether to accept the proposal. The feedback unit also has a function to automatically reflect the content in the text if the user accepts the proposal. Furthermore, if the user does not accept the proposal, the feedback unit can make a revised proposal. For example, if the user rejects the proposal, the feedback unit will display another proposal and repeat the proposal until the user is satisfied. In this way, the feedback unit can provide flexible and effective support to the user and improve the quality of communication. In addition, the feedback unit can continuously improve the accuracy and effectiveness of the entire system by collecting user feedback and feeding it back to the proposal unit and analysis unit.

[0033] The sharing unit shares the results analyzed by the analysis unit with other elements. For example, the sharing unit shares analysis results with the proposal unit and the feedback unit, strengthening the overall system coordination. Specifically, the sharing unit stores the data received from the analysis unit in a central database, making it accessible to other elements. This allows the proposal unit and the feedback unit to make more accurate and effective proposals and feedback based on the latest analysis results. Furthermore, the sharing unit manages data versioning and access control to maintain data consistency and integrity. For example, the sharing unit manages the version of data accessed by each element, always providing the latest data. Access control also prevents unauthorized access and tampering with data, ensuring system security. In addition, the sharing unit is responsible for coordination with other systems and external services. For example, the sharing unit can obtain necessary information from external data sources and provide it to the analysis unit and the proposal unit. This allows the sharing unit to streamline the data flow throughout the system and provide a foundation for each element to function effectively.

[0034] The History Analysis Department analyzes past communication history and suggests areas for improvement. For example, it analyzes a user's past communication history and suggests what aspects should be improved. Specifically, the History Analysis Department collects past text data and analyzes it using natural language processing technology. Based on the analysis results, it understands the user's communication patterns and tendencies and identifies areas for improvement. For example, if a user has frequently used ambiguous expressions in the past, the History Analysis Department will point this out and suggest using more specific expressions. The History Analysis Department can also identify a user's strengths and weaknesses based on past communication history and suggest individual improvement measures. For example, if a user lacks emotional expression, the History Analysis Department will suggest ways to express emotions more richly. Furthermore, the History Analysis Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. In this way, the History Analysis Department can support users in improving their communication skills and achieving more effective communication.

[0035] The analysis unit can analyze text such as emails, social media posts, and chat messages in real time. For example, the analysis unit can analyze email text in real time to understand the user's intent and emotions. It can also analyze social media posts in real time to understand the user's intent and emotions. Furthermore, it can analyze chat messages in real time to understand the user's intent and emotions. This allows for accurate understanding of user intent and emotions by analyzing diverse text communications in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input email text into a generating AI, which can then perform the analysis.

[0036] The suggestion unit can analyze the user's intent and emotions and propose the most appropriate words and expressions. For example, if a user inputs "When will the document be ready?", the suggestion unit will determine, based on the analysis unit's results, whether the other party is angry or simply checking, and propose an appropriate response. For example, the suggestion unit might suggest, "It seems they are checking out of concern." The suggestion unit can also point out unclear parts of the text sent by the user and suggest improvements. For example, if a user inputs "What is the status of this project?", the suggestion unit will determine that the sentence is ambiguous and suggest, "Let's clarify which specific part you want to know about." In this way, by suggesting the most appropriate words and expressions based on the user's intent and emotions, it helps to clarify communication. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the results of the analysis unit into a generation AI, which can then propose the most appropriate words and expressions.

[0037] The feedback unit can provide the user with feedback on the suggested words and expressions. For example, the feedback unit can display the suggested words and expressions to the user so that the user can confirm them. The feedback unit can also check whether the user accepts the suggestion and make a revised suggestion if necessary. This promotes improved communication by providing the user with appropriate feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the suggested words and expressions into a generating AI, and the generating AI can provide feedback.

[0038] The sharing unit can share analysis results with other elements. For example, the sharing unit can share analysis results with the proposal unit and the feedback unit to strengthen the overall system coordination. The sharing unit shares analysis results in real time, enabling each element to operate based on the latest information. This strengthens the overall system coordination by sharing analysis results with other elements. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input analysis results into a generating AI, which can then share them with other elements.

[0039] The history analysis unit can analyze past communication history and suggest areas for improvement. For example, the history analysis unit can analyze a user's past communication history and suggest areas that should be improved. The history analysis unit can analyze a user's communication patterns and propose effective areas for improvement. In this way, by analyzing past communication history, it helps improve the user's communication skills. Some or all of the above processing in the history analysis unit may be performed using AI, for example, or without AI. For example, the history analysis unit can input past communication history into a generating AI, which can then suggest areas for improvement.

[0040] The analysis unit can optimize its analysis algorithm by referring to the user's past communication patterns during analysis. For example, the analysis unit optimizes the analysis algorithm based on words and expressions the user has used in the past. The analysis unit can also extract specific trends from the user's past communication patterns and adjust the analysis algorithm accordingly. Furthermore, the analysis unit can analyze the user's past communication history and select the optimal analysis algorithm. This optimizes the analysis algorithm and improves accuracy by referring to the user's past communication patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past communication patterns into a generating AI, which can then optimize the analysis algorithm.

[0041] The analysis unit can apply different analysis methods depending on the length and complexity of the text during analysis. For example, the analysis unit can apply a simple analysis method to short texts. It can also apply a more detailed analysis method to longer texts. Furthermore, for complex texts, the analysis unit can combine and apply multiple analysis methods. This allows the analysis unit to provide appropriate results by adjusting the analysis method according to the length and complexity of the text. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the length and complexity of the text into a generating AI, which can then apply an appropriate analysis method.

[0042] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit will prioritize analyzing information related to that region. Furthermore, if the user is on the move, the analysis unit can adjust the analysis results based on their current location. Additionally, if the user is in a specific location, the analysis unit can analyze information related to that location. This allows for the provision of more relevant analysis results by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then perform the analysis.

[0043] The analysis unit can analyze a user's social media activity during analysis and prioritize the analysis of relevant text. For example, the analysis unit can prioritize the analysis of social media content that the user frequently posts. The analysis unit can also analyze relevant text based on the user's social media activity history. Furthermore, the analysis unit can prioritize the analysis of content that the user has posted on a specific topic. In this way, by analyzing the user's social media activity, highly relevant text is prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity into a generating AI, which can then prioritize the analysis of relevant text.

[0044] The proposal function can adjust the level of detail of a proposal based on the importance of the text. For example, the proposal function will provide a detailed proposal for important text. It can also provide a concise proposal for general text. Furthermore, for urgent text, the proposal function can provide a proposal that allows for a quick response. By adjusting the level of detail of a proposal based on the importance of the text, it provides an appropriate proposal. Some or all of the above processing in the proposal function may be performed using AI, for example, or not using AI. For example, the proposal function can input the importance of the text into a generating AI, which can then adjust the level of detail of the proposal.

[0045] The suggestion function can apply different suggestion algorithms depending on the text category when making suggestions. For example, the suggestion function will make formal suggestions for business-related text. It can also make casual suggestions for social media-related text. Furthermore, it can make friendly suggestions for private text. In this way, appropriate suggestions are made by adjusting the suggestion algorithm according to the text category. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the text category into a generating AI, and the generating AI can apply different suggestion algorithms.

[0046] The proposal department can determine the priority of proposals based on the submission date of the text. For example, the proposal department can prioritize urgent texts. It can also propose regular texts with normal priority. Furthermore, it can propose older texts with lower priority. This ensures that proposals are submitted at the appropriate time by prioritizing them based on the submission date of the text. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the text submission date into a generating AI, which can then determine the priority of the proposals.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of the texts during the suggestion process. For example, it may suggest importantly relevant texts first. It may also suggest generally relevant texts in the middle. Furthermore, it may suggest less relevant texts last. This prioritizes important suggestions by adjusting the order of suggestions based on the relevance of the texts. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the relevance of the texts into a generating AI, which can then adjust the order of suggestions.

[0048] The feedback unit can select the optimal feedback method by referring to the user's past feedback history when providing feedback. For example, the feedback unit may prioritize feedback methods that the user has preferred in the past. The feedback unit can also select the optimal feedback method based on the user's past feedback history. Furthermore, the feedback unit can analyze the effects of feedback the user has received in the past and select the optimal feedback method. In this way, by referring to the user's past feedback history, the optimal feedback method is selected and effective feedback is provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history into a generating AI, which can then select the optimal feedback method.

[0049] The feedback unit can select the optimal feedback method by considering the user's device information when providing feedback. For example, if the user is using a smartphone, the feedback unit can provide a feedback method that matches the screen size. Furthermore, if the user is using a tablet, the feedback unit can provide a feedback method optimized for a larger screen. Additionally, if the user is using a smartwatch, the feedback unit can provide a concise and highly visible feedback method. In this way, the optimal feedback method is provided by considering the user's device information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's device information into a generating AI, which can then select the optimal feedback method.

[0050] The sharing unit can select the optimal sharing method by referring to the user's past sharing history when sharing information. For example, the sharing unit may prioritize sharing methods that the user has preferred in the past. It can also select the optimal sharing method based on the user's past sharing history. Furthermore, the sharing unit can analyze the effects of past sharing experiences and select the optimal sharing method. This allows for effective information sharing by selecting the optimal sharing method based on the user's past sharing history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the user's past sharing history into a generating AI, which can then select the optimal sharing method.

[0051] The sharing unit can select the optimal sharing method when sharing information, taking into account the user's geographical location. For example, if the user is in a specific region, the sharing unit will prioritize sharing information related to that region. Furthermore, if the user is on the move, the sharing unit can adjust the sharing method based on their current location. Additionally, if the user is in a specific location, the sharing unit can share information related to that location. This ensures that the optimal sharing method is provided by considering the user's geographical location. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For instance, the sharing unit can input the user's geographical location information into a generating AI, which can then select the optimal sharing method.

[0052] The history analysis unit can select the optimal analysis method by referring to the user's past communication history during history analysis. For example, the history analysis unit can select the optimal analysis method based on the words and expressions the user has used in the past. The history analysis unit can also extract specific trends from the user's past communication history and adjust the analysis method accordingly. Furthermore, the history analysis unit can analyze the user's past communication history and select the most effective analysis method. In this way, by referring to the user's past communication history, the optimal analysis method is selected and effective history analysis is performed. Some or all of the above processing in the history analysis unit may be performed using AI, for example, or without AI. For example, the history analysis unit can input the user's past communication history into a generating AI, which can then select the optimal analysis method.

[0053] The history analysis unit can select the optimal analysis method by considering the user's geographical location information during history analysis. For example, if the user is in a specific region, the history analysis unit will prioritize analyzing history related to that region. Furthermore, if the user is on the move, the history analysis unit can adjust the history analysis based on the user's current location. Additionally, if the user is in a specific location, the history analysis unit can analyze history related to that location. This allows the system to provide the optimal analysis method by considering the user's geographical location information. Some or all of the above processing in the history analysis unit may be performed using AI, for example, or without AI. For example, the history analysis unit can input the user's geographical location information into a generating AI, which can then select the optimal analysis method.

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

[0055] The analysis unit can improve the accuracy of its analysis of user intent and emotions by referencing the user's past behavioral patterns. For example, it can more accurately understand the intent of the current text based on the words and expressions the user has used in the past. It can also more accurately estimate the user's current emotions by referring to the emotions the user has expressed in the past. Furthermore, it can improve the accuracy of the analysis by analyzing the user's past communication style and applying it to the current text. In this way, by referring to the user's past behavioral patterns, the accuracy of the analysis can be improved, and more appropriate analysis results can be provided.

[0056] The suggestion function can analyze a user's intentions and emotions by referring to their past communication history to provide optimal suggestions. For example, it can suggest the most appropriate words and expressions for the current text based on the words and expressions the user has used in the past. It can also suggest the most appropriate emotional expressions for the current text by referring to the emotions the user has expressed in the past. Furthermore, it can analyze the user's past communication style and apply it to the current text to provide optimal suggestions. In this way, by referring to the user's past communication history, optimal suggestions can be made, improving the quality of communication.

[0057] The feedback system can analyze a user's intentions and emotions, referencing their past feedback history to provide optimal feedback. For example, it can provide the best feedback for the current text based on what kind of feedback the user has accepted in the past. It can also provide appropriate feedback for the current text by referring to what kind of feedback the user has rejected in the past. Furthermore, it can provide optimal feedback by analyzing what kind of feedback the user has preferred in the past and applying that to the current text. In this way, by referring to the user's past feedback history, the system can provide optimal feedback and improve the quality of communication.

[0058] The sharing function can analyze a user's intentions and emotions, referencing their past sharing history to provide the optimal sharing method. For example, it can provide the best sharing method for the current text based on what information the user has shared in the past. It can also provide an appropriate sharing method for the current text by referring to what information the user has shared in the past. Furthermore, it can provide the optimal sharing method by analyzing what sharing methods the user has preferred in the past and applying that to the current text. In this way, by referring to the user's past sharing history, the optimal sharing method can be provided, improving the quality of communication.

[0059] The history analysis unit can provide the optimal analysis method when analyzing a user's intentions and emotions by referring to the user's past communication history. For example, it can provide the optimal analysis method for the current text based on the words and expressions the user has used in the past. It can also provide an appropriate analysis method for the current text by referring to the emotions the user has expressed in the past. Furthermore, it can provide the optimal analysis method by analyzing the user's past communication style and applying it to the current text. In this way, by referring to the user's past communication history, the optimal analysis method can be provided, and the quality of communication can be improved.

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

[0061] Step 1: The analysis unit analyzes the user's text in real time. The analysis unit analyzes text such as emails, social media posts, and chat messages in real time. The analysis unit uses natural language processing technology to understand the context of the text and analyze the user's intent and emotions. For example, the analysis unit analyzes the content of the text to understand what the user wants to convey. The analysis unit also analyzes the user's emotions to determine what kind of emotions are associated with the text. Step 2: The suggestion department proposes the most appropriate words and expressions based on the results analyzed by the analysis department. For example, if a user enters "When will the document be ready?", the suggestion department will determine, based on the analysis department's results, whether the other party is angry or simply checking, and propose an appropriate response. For example, the suggestion department might suggest, "It seems they are checking out of concern." The suggestion department also points out unclear parts of the text sent by the user and suggests improvements. For example, if a user enters "What is the status of this project?", the suggestion department will determine that the sentence is ambiguous and suggest, "Let's clarify which specific part you want to know about." Step 3: The feedback unit provides feedback to the user regarding the words and expressions proposed by the suggestion unit. For example, the feedback unit displays the proposed words and expressions to the user so that the user can confirm them. The feedback unit can also check whether the user accepts the suggestion and make revisions if necessary. Step 4: The sharing unit shares the results analyzed by the analysis unit with other elements. For example, the sharing unit shares the analysis results with the proposal unit and the feedback unit to strengthen the overall system coordination. Step 5: The History Analysis Department analyzes past communication history and suggests areas for improvement. For example, the History Analysis Department analyzes a user's past communication history and suggests what aspects should be improved.

[0062] (Example of form 2) The AI ​​tool according to an embodiment of the present invention is a system that assists in accurately conveying the user's intentions and emotions in conversations and text communications. This system aims to clarify communication and resolve the problem of misunderstandings by applying natural language processing technology to understand the context and select the most appropriate words. For example, when a user engages in text communication, the AI ​​tool analyzes the text in real time. This includes texts such as emails, social media posts, and chat messages. In this process, the AI ​​tool understands the context and analyzes the user's intentions and emotions. Next, based on the analysis results, the AI ​​tool suggests the most appropriate words and expressions. For example, if a user types "When will the document be ready?", the AI ​​tool analyzes the context and determines whether the other person is angry or simply checking. It then suggests an appropriate reply, such as "It seems they are checking out of concern." Furthermore, the AI ​​tool also points out unclear parts of the text sent by the user and suggests improvements. For example, if a user types "What is the status of this project?", the AI ​​tool determines that the sentence is ambiguous and suggests "Let's clarify which specific part you want to know about." In this way, the AI ​​tool supports users' text communication in real time, helping to accurately convey intentions and emotions. It also has the function to analyze past communication history and suggest areas for improvement. This allows users to improve their communication skills. This AI tool is particularly effective in business settings and supports professional communication. For example, it can enable effective communication in companies of all sizes, from large corporations to small and medium-sized enterprises, where text communication with customers is important. It can also be used by individual users to improve communication on social media and chat messages. Thus, the AI ​​tool of this invention aims to improve the quality of text communication by helping to accurately convey users' intentions and emotions.This allows AI tools to analyze users' text communications in real time, suggest optimal words and expressions, and provide feedback, thereby improving the quality of communication.

[0063] The AI ​​tool according to this embodiment comprises an analysis unit, a suggestion unit, a feedback unit, a sharing unit, and a history analysis unit. The analysis unit analyzes the user's text in real time. The analysis unit analyzes text such as emails, social media posts, and chat messages in real time. The analysis unit uses natural language processing technology to understand the context of the text and analyze the user's intent and emotions. For example, the analysis unit analyzes the content of the text and understands what the user wants to convey. The analysis unit also analyzes the user's emotions and determines what kind of emotions are associated with the text. The suggestion unit proposes the most appropriate words and expressions based on the results analyzed by the analysis unit. For example, if the user inputs "When will the document be ready?", the suggestion unit, based on the results of the analysis unit, determines whether the other party is angry or simply checking, and proposes an appropriate reply. For example, the suggestion unit might suggest, "It seems they are checking out of concern." The suggestion unit also points out parts of the text sent by the user that are difficult to understand and proposes improvements. For example, if a user enters "What is the status of this project?", the suggestion unit will determine that the sentence is ambiguous and make a suggestion such as "Let's clarify which specific part you want to know about." The feedback unit provides feedback to the user on the words and expressions suggested by the suggestion unit. The feedback unit, for example, displays the suggested words and expressions to the user so that the user can confirm them. The feedback unit can also check whether the user accepts the suggestion and make a revised suggestion if necessary. The sharing unit shares the results analyzed by the analysis unit with other elements. For example, the sharing unit shares the analysis results with the suggestion unit and the feedback unit to strengthen the overall system coordination. The history analysis unit analyzes past communication history and suggests areas for improvement. For example, the history analysis unit analyzes the user's past communication history and suggests what aspects should be improved. As a result, the AI ​​tool according to the embodiment can improve the quality of communication by analyzing the user's text communication in real time, suggesting the most appropriate words and expressions, and providing feedback.

[0064] The analysis unit analyzes user text in real time. For example, it analyzes text from emails, social media posts, and chat messages in real time. Using natural language processing technology, the analysis unit understands the context of the text and analyzes the user's intent and emotions. Specifically, the analysis unit performs morphological and grammatical analysis to analyze the content of the text and understand what the user wants to convey. Morphological analysis breaks down the text into individual words and identifies the part of speech and meaning of each word. Grammatical analysis analyzes the structure of the sentence and clarifies the relationships between subjects, predicates, and objects. Furthermore, the analysis unit uses sentiment analysis technology to determine what emotions are associated with the text. Sentiment analysis classifies emotions into categories such as positive, negative, and neutral to understand the user's emotional state. For example, if a user enters "I'm so happy today!", the analysis unit analyzes this text as a positive emotion. Conversely, if a user enters "I'm so tired," the analysis unit analyzes it as a negative emotion. This allows the analysis unit to accurately grasp the user's intentions and emotions, and provide the necessary information to the next steps, the proposal unit and feedback unit.

[0065] The suggestion department proposes the most appropriate words and expressions based on the results analyzed by the analysis department. For example, if a user inputs "When will the document be ready?", the suggestion department will determine, based on the analysis department's results, whether the other party is angry or simply checking, and propose an appropriate reply. Specifically, the suggestion department utilizes generative AI to generate appropriate reply candidates based on the user's input text and the analysis results. Generative AI has the ability to learn from large amounts of text data and generate natural sentences appropriate to the context. For example, if a user inputs "When will the document be ready?", the suggestion department uses the generative AI to suggest something like, "It seems they are checking out of concern." The suggestion department also points out unclear parts of the messages sent by users and suggests improvements. For example, if a user inputs "What is the status of this project?", the suggestion department will determine that the sentence is ambiguous and suggest, "Let's clarify which specific part you want to know about." In this way, the suggestion department can facilitate user communication and prevent misunderstandings and problems. Furthermore, the suggestion department can also provide more personalized suggestions by referring to the user's past communication history. This allows the proposal department to provide the most appropriate words and expressions to meet user needs, thereby improving the quality of communication.

[0066] The feedback unit provides users with feedback on the words and expressions proposed by the proposal unit. For example, the feedback unit displays the proposed words and expressions to the user, allowing the user to review them. Specifically, the feedback unit visually displays the proposed content through the user interface, allowing the user to choose whether to accept the proposal. For example, if a user enters "What is the status of this project?", the feedback unit will display a pop-up from the proposal unit suggesting "Let's clarify which specific parts you want to know about," and ask the user whether to accept the proposal. The feedback unit also has a function to automatically reflect the content in the text if the user accepts the proposal. Furthermore, if the user does not accept the proposal, the feedback unit can make a revised proposal. For example, if the user rejects the proposal, the feedback unit will display another proposal and repeat the proposal until the user is satisfied. In this way, the feedback unit can provide flexible and effective support to the user and improve the quality of communication. In addition, the feedback unit can continuously improve the accuracy and effectiveness of the entire system by collecting user feedback and feeding it back to the proposal unit and analysis unit.

[0067] The sharing unit shares the results analyzed by the analysis unit with other elements. For example, the sharing unit shares analysis results with the proposal unit and the feedback unit, strengthening the overall system coordination. Specifically, the sharing unit stores the data received from the analysis unit in a central database, making it accessible to other elements. This allows the proposal unit and the feedback unit to make more accurate and effective proposals and feedback based on the latest analysis results. Furthermore, the sharing unit manages data versioning and access control to maintain data consistency and integrity. For example, the sharing unit manages the version of data accessed by each element, always providing the latest data. Access control also prevents unauthorized access and tampering with data, ensuring system security. In addition, the sharing unit is responsible for coordination with other systems and external services. For example, the sharing unit can obtain necessary information from external data sources and provide it to the analysis unit and the proposal unit. This allows the sharing unit to streamline the data flow throughout the system and provide a foundation for each element to function effectively.

[0068] The History Analysis Department analyzes past communication history and suggests areas for improvement. For example, it analyzes a user's past communication history and suggests what aspects should be improved. Specifically, the History Analysis Department collects past text data and analyzes it using natural language processing technology. Based on the analysis results, it understands the user's communication patterns and tendencies and identifies areas for improvement. For example, if a user has frequently used ambiguous expressions in the past, the History Analysis Department will point this out and suggest using more specific expressions. The History Analysis Department can also identify a user's strengths and weaknesses based on past communication history and suggest individual improvement measures. For example, if a user lacks emotional expression, the History Analysis Department will suggest ways to express emotions more richly. Furthermore, the History Analysis Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. In this way, the History Analysis Department can support users in improving their communication skills and achieving more effective communication.

[0069] The analysis unit can analyze text such as emails, social media posts, and chat messages in real time. For example, the analysis unit can analyze email text in real time to understand the user's intent and emotions. It can also analyze social media posts in real time to understand the user's intent and emotions. Furthermore, it can analyze chat messages in real time to understand the user's intent and emotions. This allows for accurate understanding of user intent and emotions by analyzing diverse text communications in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input email text into a generating AI, which can then perform the analysis.

[0070] The suggestion unit can analyze the user's intent and emotions and propose the most appropriate words and expressions. For example, if a user inputs "When will the document be ready?", the suggestion unit will determine, based on the analysis unit's results, whether the other party is angry or simply checking, and propose an appropriate response. For example, the suggestion unit might suggest, "It seems they are checking out of concern." The suggestion unit can also point out unclear parts of the text sent by the user and suggest improvements. For example, if a user inputs "What is the status of this project?", the suggestion unit will determine that the sentence is ambiguous and suggest, "Let's clarify which specific part you want to know about." In this way, by suggesting the most appropriate words and expressions based on the user's intent and emotions, it helps to clarify communication. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the results of the analysis unit into a generation AI, which can then propose the most appropriate words and expressions.

[0071] The feedback unit can provide the user with feedback on the suggested words and expressions. For example, the feedback unit can display the suggested words and expressions to the user so that the user can confirm them. The feedback unit can also check whether the user accepts the suggestion and make a revised suggestion if necessary. This promotes improved communication by providing the user with appropriate feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the suggested words and expressions into a generating AI, and the generating AI can provide feedback.

[0072] The sharing unit can share analysis results with other elements. For example, the sharing unit can share analysis results with the proposal unit and the feedback unit to strengthen the overall system coordination. The sharing unit shares analysis results in real time, enabling each element to operate based on the latest information. This strengthens the overall system coordination by sharing analysis results with other elements. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input analysis results into a generating AI, which can then share them with other elements.

[0073] The history analysis unit can analyze past communication history and suggest areas for improvement. For example, the history analysis unit can analyze a user's past communication history and suggest areas that should be improved. The history analysis unit can analyze a user's communication patterns and propose effective areas for improvement. In this way, by analyzing past communication history, it helps improve the user's communication skills. Some or all of the above processing in the history analysis unit may be performed using AI, for example, or without AI. For example, the history analysis unit can input past communication history into a generating AI, which can then suggest areas for improvement.

[0074] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the accuracy of the analysis and provide more specific suggestions. If the user is relaxed, the analysis unit can also adjust the accuracy of the analysis to provide more flexible suggestions. Furthermore, if the user is in a hurry, the analysis unit can adjust the accuracy of the analysis to provide quicker suggestions. This allows for more appropriate analysis results by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the accuracy of the analysis.

[0075] The analysis unit can optimize its analysis algorithm by referring to the user's past communication patterns during analysis. For example, the analysis unit optimizes the analysis algorithm based on words and expressions the user has used in the past. The analysis unit can also extract specific trends from the user's past communication patterns and adjust the analysis algorithm accordingly. Furthermore, the analysis unit can analyze the user's past communication history and select the optimal analysis algorithm. This optimizes the analysis algorithm and improves accuracy by referring to the user's past communication patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past communication patterns into a generating AI, which can then optimize the analysis algorithm.

[0076] The analysis unit can apply different analysis methods depending on the length and complexity of the text during analysis. For example, the analysis unit can apply a simple analysis method to short texts. It can also apply a more detailed analysis method to longer texts. Furthermore, for complex texts, the analysis unit can combine and apply multiple analysis methods. This allows the analysis unit to provide appropriate results by adjusting the analysis method according to the length and complexity of the text. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the length and complexity of the text into a generating AI, which can then apply an appropriate analysis method.

[0077] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is angry, the analysis unit will prioritize displaying important analysis results. If the user is relaxed, the analysis unit can also display the overall analysis results in a balanced manner. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that require immediate attention. This ensures that important information is provided preferentially by prioritizing the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI, which can then determine the priority of the analysis results.

[0078] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit will prioritize analyzing information related to that region. Furthermore, if the user is on the move, the analysis unit can adjust the analysis results based on their current location. Additionally, if the user is in a specific location, the analysis unit can analyze information related to that location. This allows for the provision of more relevant analysis results by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then perform the analysis.

[0079] The analysis unit can analyze a user's social media activity during analysis and prioritize the analysis of relevant text. For example, the analysis unit can prioritize the analysis of social media content that the user frequently posts. The analysis unit can also analyze relevant text based on the user's social media activity history. Furthermore, the analysis unit can prioritize the analysis of content that the user has posted on a specific topic. In this way, by analyzing the user's social media activity, highly relevant text is prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity into a generating AI, which can then prioritize the analysis of relevant text.

[0080] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on those emotions. For example, if the user is angry, the suggestion unit can suggest calm and polite expressions. If the user is relaxed, it can also suggest casual expressions. Furthermore, if the user is in a hurry, it can suggest concise and quick expressions. By adjusting the way suggestions are expressed according to the user's emotions, the suggestion unit can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the way suggestions are expressed.

[0081] The proposal function can adjust the level of detail of a proposal based on the importance of the text. For example, the proposal function will provide a detailed proposal for important text. It can also provide a concise proposal for general text. Furthermore, for urgent text, the proposal function can provide a proposal that allows for a quick response. By adjusting the level of detail of a proposal based on the importance of the text, it provides an appropriate proposal. Some or all of the above processing in the proposal function may be performed using AI, for example, or not using AI. For example, the proposal function can input the importance of the text into a generating AI, which can then adjust the level of detail of the proposal.

[0082] The suggestion function can apply different suggestion algorithms depending on the text category when making suggestions. For example, the suggestion function will make formal suggestions for business-related text. It can also make casual suggestions for social media-related text. Furthermore, it can make friendly suggestions for private text. In this way, appropriate suggestions are made by adjusting the suggestion algorithm according to the text category. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the text category into a generating AI, and the generating AI can apply different suggestion algorithms.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is angry, the suggestion unit will make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can also make a detailed suggestion. Furthermore, if the user is in a hurry, the suggestion unit can make a short, quick suggestion. By adjusting the length of the suggestion according to the user's emotions, the suggestion unit can make more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI, which can then adjust the length of the suggestion.

[0084] The proposal department can determine the priority of proposals based on the submission date of the text. For example, the proposal department can prioritize urgent texts. It can also propose regular texts with normal priority. Furthermore, it can propose older texts with lower priority. This ensures that proposals are submitted at the appropriate time by prioritizing them based on the submission date of the text. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the text submission date into a generating AI, which can then determine the priority of the proposals.

[0085] The suggestion unit can adjust the order of suggestions based on the relevance of the texts during the suggestion process. For example, it may suggest importantly relevant texts first. It may also suggest generally relevant texts in the middle. Furthermore, it may suggest less relevant texts last. This prioritizes important suggestions by adjusting the order of suggestions based on the relevance of the texts. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the relevance of the texts into a generating AI, which can then adjust the order of suggestions.

[0086] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is angry, the feedback unit can provide calm and polite feedback. It can also provide casual feedback if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can provide quick and concise feedback. This allows for more appropriate feedback by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user emotion data into the generative AI, which can then adjust the feedback method.

[0087] The feedback unit can select the optimal feedback method by referring to the user's past feedback history when providing feedback. For example, the feedback unit may prioritize feedback methods that the user has preferred in the past. The feedback unit can also select the optimal feedback method based on the user's past feedback history. Furthermore, the feedback unit can analyze the effects of feedback the user has received in the past and select the optimal feedback method. In this way, by referring to the user's past feedback history, the optimal feedback method is selected and effective feedback is provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history into a generating AI, which can then select the optimal feedback method.

[0088] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is angry, the feedback unit will prioritize important feedback. If the user is relaxed, the feedback unit can provide balanced overall feedback. Furthermore, if the user is in a hurry, the feedback unit can prioritize feedback that requires immediate attention. This ensures that important feedback is provided preferentially by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI, which can then determine the priority of feedback.

[0089] The feedback unit can select the optimal feedback method by considering the user's device information when providing feedback. For example, if the user is using a smartphone, the feedback unit can provide a feedback method that matches the screen size. Furthermore, if the user is using a tablet, the feedback unit can provide a feedback method optimized for a larger screen. Additionally, if the user is using a smartwatch, the feedback unit can provide a concise and highly visible feedback method. In this way, the optimal feedback method is provided by considering the user's device information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's device information into a generating AI, which can then select the optimal feedback method.

[0090] The sharing unit can estimate the user's emotions and select information to share based on the estimated emotions. For example, if the user is angry, the sharing unit will share calm and polite information. It can also share casual information if the user is relaxed. Furthermore, if the user is in a hurry, it can share quick and concise information. This ensures that appropriate information is shared by selecting information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI, or not. For example, the sharing unit can input user emotion data into a generative AI, which can then select information to share.

[0091] The sharing unit can select the optimal sharing method by referring to the user's past sharing history when sharing information. For example, the sharing unit may prioritize sharing methods that the user has preferred in the past. It can also select the optimal sharing method based on the user's past sharing history. Furthermore, the sharing unit can analyze the effects of past sharing experiences and select the optimal sharing method. This allows for effective information sharing by selecting the optimal sharing method based on the user's past sharing history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the user's past sharing history into a generating AI, which can then select the optimal sharing method.

[0092] The sharing unit can estimate the user's emotions and determine sharing priorities based on those emotions. For example, if the user is angry, the sharing unit will prioritize sharing important information. If the user is relaxed, the sharing unit can also share information in a balanced way. Furthermore, if the user is in a hurry, the sharing unit can prioritize sharing information that requires a quick response. In this way, by determining sharing priorities based on the user's emotions, important information is shared preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI, or not using AI. For example, the sharing unit can input user emotion data into a generative AI, which can then determine sharing priorities.

[0093] The sharing unit can select the optimal sharing method when sharing information, taking into account the user's geographical location. For example, if the user is in a specific region, the sharing unit will prioritize sharing information related to that region. Furthermore, if the user is on the move, the sharing unit can adjust the sharing method based on their current location. Additionally, if the user is in a specific location, the sharing unit can share information related to that location. This ensures that the optimal sharing method is provided by considering the user's geographical location. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For instance, the sharing unit can input the user's geographical location information into a generating AI, which can then select the optimal sharing method.

[0094] The history analysis unit can estimate the user's emotions and adjust the history analysis method based on the estimated user emotions. For example, if the user is angry, the history analysis unit can perform a detailed history analysis. It can also perform a general history analysis if the user is relaxed. Furthermore, if the user is in a hurry, the history analysis unit can perform a rapid history analysis. This provides appropriate analysis results by adjusting the history analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history analysis unit may be performed using AI or not using AI. For example, the history analysis unit can input user emotion data into a generative AI, which can then adjust the history analysis method.

[0095] The history analysis unit can select the optimal analysis method by referring to the user's past communication history during history analysis. For example, the history analysis unit can select the optimal analysis method based on the words and expressions the user has used in the past. The history analysis unit can also extract specific trends from the user's past communication history and adjust the analysis method accordingly. Furthermore, the history analysis unit can analyze the user's past communication history and select the most effective analysis method. In this way, by referring to the user's past communication history, the optimal analysis method is selected and effective history analysis is performed. Some or all of the above processing in the history analysis unit may be performed using AI, for example, or without AI. For example, the history analysis unit can input the user's past communication history into a generating AI, which can then select the optimal analysis method.

[0096] The history analysis unit can estimate the user's emotions and determine the priority of the history analysis based on the estimated emotions. For example, if the user is angry, the history analysis unit will prioritize analyzing important history. If the user is relaxed, the history analysis unit can also analyze the overall history in a balanced manner. Furthermore, if the user is in a hurry, the history analysis unit can prioritize analyzing history that requires immediate attention. In this way, by determining the priority of the history analysis based on the user's emotions, important history is prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history analysis unit may be performed using AI or not using AI. For example, the history analysis unit can input user emotion data into a generative AI, and the generative AI can determine the priority of the history analysis.

[0097] The history analysis unit can select the optimal analysis method by considering the user's geographical location information during history analysis. For example, if the user is in a specific region, the history analysis unit will prioritize analyzing history related to that region. Furthermore, if the user is on the move, the history analysis unit can adjust the history analysis based on the user's current location. Additionally, if the user is in a specific location, the history analysis unit can analyze history related to that location. This allows the system to provide the optimal analysis method by considering the user's geographical location information. Some or all of the above processing in the history analysis unit may be performed using AI, for example, or without AI. For example, the history analysis unit can input the user's geographical location information into a generating AI, which can then select the optimal analysis method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can improve the accuracy of its analysis of user intent and emotions by referencing the user's past behavioral patterns. For example, it can more accurately understand the intent of the current text based on the words and expressions the user has used in the past. It can also more accurately estimate the user's current emotions by referring to the emotions the user has expressed in the past. Furthermore, it can improve the accuracy of the analysis by analyzing the user's past communication style and applying it to the current text. In this way, by referring to the user's past behavioral patterns, the accuracy of the analysis can be improved, and more appropriate analysis results can be provided.

[0100] The suggestion function can analyze a user's intentions and emotions by referring to their past communication history to provide optimal suggestions. For example, it can suggest the most appropriate words and expressions for the current text based on the words and expressions the user has used in the past. It can also suggest the most appropriate emotional expressions for the current text by referring to the emotions the user has expressed in the past. Furthermore, it can analyze the user's past communication style and apply it to the current text to provide optimal suggestions. In this way, by referring to the user's past communication history, optimal suggestions can be made, improving the quality of communication.

[0101] The feedback system can analyze a user's intentions and emotions, referencing their past feedback history to provide optimal feedback. For example, it can provide the best feedback for the current text based on what kind of feedback the user has accepted in the past. It can also provide appropriate feedback for the current text by referring to what kind of feedback the user has rejected in the past. Furthermore, it can provide optimal feedback by analyzing what kind of feedback the user has preferred in the past and applying that to the current text. In this way, by referring to the user's past feedback history, the system can provide optimal feedback and improve the quality of communication.

[0102] The sharing function can analyze a user's intentions and emotions, referencing their past sharing history to provide the optimal sharing method. For example, it can provide the best sharing method for the current text based on what information the user has shared in the past. It can also provide an appropriate sharing method for the current text by referring to what information the user has shared in the past. Furthermore, it can provide the optimal sharing method by analyzing what sharing methods the user has preferred in the past and applying that to the current text. In this way, by referring to the user's past sharing history, the optimal sharing method can be provided, improving the quality of communication.

[0103] The history analysis unit can provide the optimal analysis method when analyzing a user's intentions and emotions by referring to the user's past communication history. For example, it can provide the optimal analysis method for the current text based on the words and expressions the user has used in the past. It can also provide an appropriate analysis method for the current text by referring to the emotions the user has expressed in the past. Furthermore, it can provide the optimal analysis method by analyzing the user's past communication style and applying it to the current text. In this way, by referring to the user's past communication history, the optimal analysis method can be provided, and the quality of communication can be improved.

[0104] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is stressed, the accuracy of the analysis can be increased to provide more specific suggestions. If the user is relaxed, the accuracy of the analysis can be adjusted to provide more flexible suggestions. Furthermore, if the user is in a hurry, the accuracy of the analysis can be adjusted to provide quicker suggestions. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0105] The suggestion function can estimate the user's emotions and adjust the way suggestions are expressed based on those emotions. For example, if the user is angry, it can suggest calm and polite language. If the user is relaxed, it can suggest casual language. Furthermore, if the user is in a hurry, it can suggest concise and quick language. By adjusting the way suggestions are expressed according to the user's emotions, it can provide more appropriate suggestions.

[0106] The feedback unit can estimate the user's emotions and adjust the feedback method based on those emotions. For example, if the user is angry, it can provide calm and polite feedback. If the user is relaxed, it can provide casual feedback. Furthermore, if the user is in a hurry, it can provide quick and concise feedback. By adjusting the feedback method according to the user's emotions, it is possible to provide more appropriate feedback.

[0107] The sharing function can estimate the user's emotions and select information to share based on those emotions. For example, if the user is angry, it can share calm and polite information. If the user is relaxed, it can share casual information. Furthermore, if the user is in a hurry, it can share quick and concise information. In this way, by selecting information to share based on the user's emotions, it can share appropriate information.

[0108] The history analysis unit can estimate the user's emotions and adjust the history analysis method based on the estimated emotions. For example, if the user is angry, a detailed history analysis can be performed. If the user is relaxed, a general history analysis can be performed. Furthermore, if the user is in a hurry, a rapid history analysis can be performed. In this way, by adjusting the history analysis method based on the user's emotions, appropriate analysis results can be provided.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The analysis unit analyzes the user's text in real time. The analysis unit analyzes text such as emails, social media posts, and chat messages in real time. The analysis unit uses natural language processing technology to understand the context of the text and analyze the user's intent and emotions. For example, the analysis unit analyzes the content of the text to understand what the user wants to convey. The analysis unit also analyzes the user's emotions to determine what kind of emotions are associated with the text. Step 2: The suggestion department proposes the most appropriate words and expressions based on the results analyzed by the analysis department. For example, if a user enters "When will the document be ready?", the suggestion department will determine, based on the analysis department's results, whether the other party is angry or simply checking, and propose an appropriate response. For example, the suggestion department might suggest, "It seems they are checking out of concern." The suggestion department also points out unclear parts of the text sent by the user and suggests improvements. For example, if a user enters "What is the status of this project?", the suggestion department will determine that the sentence is ambiguous and suggest, "Let's clarify which specific part you want to know about." Step 3: The feedback unit provides feedback to the user regarding the words and expressions proposed by the suggestion unit. For example, the feedback unit displays the proposed words and expressions to the user so that the user can confirm them. The feedback unit can also check whether the user accepts the suggestion and make revisions if necessary. Step 4: The sharing unit shares the results analyzed by the analysis unit with other elements. For example, the sharing unit shares the analysis results with the proposal unit and the feedback unit to strengthen the overall system coordination. Step 5: The History Analysis Department analyzes past communication history and suggests areas for improvement. For example, the History Analysis Department analyzes a user's past communication history and suggests what aspects should be improved.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the analysis unit, proposal unit, feedback unit, sharing unit, and history analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the user's text in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable words and expressions based on the analysis results. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides feedback to the user on the proposed words and expressions. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares the analysis results with other elements. The history analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past communication history and suggests areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the analysis unit, proposal unit, feedback unit, sharing unit, and history analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes the user's text in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable words and expressions based on the analysis results. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides feedback to the user on the proposed words and expressions. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares the analysis results with other elements. The history analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past communication history and suggests areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the analysis unit, proposal unit, feedback unit, sharing unit, and history analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes the user's text in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most appropriate words and expressions based on the analysis results. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides feedback to the user on the proposed words and expressions. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares the analysis results with other elements. The history analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past communication history and suggests areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the analysis unit, proposal unit, feedback unit, sharing unit, and history analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes the user's text in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable words or expressions based on the analysis results. The feedback unit is implemented by the control unit 46A of the robot 414 and provides feedback to the user on the proposed words or expressions. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares the analysis results with other elements. The history analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past communication history and suggests areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) An analysis unit that analyzes the user's text in real time, Based on the results of the analysis performed by the aforementioned analysis unit, the proposal unit proposes the most suitable words and expressions, A feedback unit that provides feedback to the user on the words and expressions proposed by the aforementioned proposal unit, A sharing unit that shares the results of the analysis performed by the aforementioned analysis unit, It includes a history analysis unit that analyzes past communication history and suggests areas for improvement. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyzes text in real time, including emails, social media posts, and chat messages. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, It analyzes the user's intentions and emotions and suggests the most appropriate words and expressions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Provide feedback to users on suggested words and expressions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, Share the analysis results with other elements. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned history analysis unit, Analyze past communication history and suggest areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past communication patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, different analysis methods are applied depending on the length and complexity of the text. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the system analyzes the user's social media activity and prioritizes analyzing relevant text. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the text category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the text was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, the system selects the most suitable feedback method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is When providing feedback, the optimal feedback method is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned shared portion is, It estimates the user's emotions and selects information to share based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned shared portion is, When sharing, the system will refer to the user's past sharing history to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned shared portion is, It estimates the user's emotions and determines sharing priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shared portion is, When sharing, the system selects the optimal sharing method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned history analysis unit, We estimate the user's emotions and adjust the historical analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned history analysis unit, During historical analysis, the system selects the optimal analysis method by referring to the user's past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned history analysis unit, It estimates the user's emotions and determines the priority of historical analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned history analysis unit, When performing historical analysis, the optimal analysis method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes the user's text in real time, Based on the results of the analysis performed by the aforementioned analysis unit, the proposal unit proposes the most suitable words and expressions, A feedback unit that provides feedback to the user on the words and expressions proposed by the aforementioned proposal unit, A sharing unit that shares the results of the analysis performed by the aforementioned analysis unit, It includes a history analysis unit that analyzes past communication history and suggests areas for improvement. A system characterized by the following features.

2. The aforementioned analysis unit, Analyzes text in real time, including emails, social media posts, and chat messages. The system according to feature 1.

3. The aforementioned proposal section is, It analyzes the user's intentions and emotions and suggests the most appropriate words and expressions. The system according to feature 1.

4. The aforementioned feedback unit is Provide feedback to users on suggested words and expressions. The system according to feature 1.

5. The aforementioned shared portion is, Share the analysis results with other elements. The system according to feature 1.

6. The aforementioned history analysis unit, Analyze past communication history and suggest areas for improvement. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past communication patterns. The system according to feature 1.

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