system

Abstract

The system according to the embodiment aims to enable users to effectively practice business English conversation and receive feedback. According to an embodiment, the system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects conversation data of a user. The analysis unit analyzes the data collected by the collection unit. The provision unit provides feedback based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Previous technology had the problem that the environment for practicing business English was limited, making it difficult to obtain effective feedback.

[0005] The system according to the embodiment aims to enable users to effectively practice business English conversation and receive feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects conversation data of a user. The analysis unit analyzes the data collected by the collection unit. The provision unit provides feedback based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to effectively practice business English conversation and receive feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business English conversation practice system according to an embodiment of the present invention is a system that provides easy business English conversation using a messaging app (for example, LINE (registered trademark)). This system allows users to practice business English conversation through a messaging app or the like. User conversation data is accumulated and analyzed by AI. Based on the analysis results, appropriate feedback and advice is provided to the user. This system allows users to practice business English conversation in their own words without fear of failure. The accumulated data can also be used as killer content for companies. For example, if a user inputs, "I would like to talk about the progress of the meeting," the AI ​​will provide an appropriate conversation scenario. The user can proceed with the conversation according to that scenario. Next, the user's conversation data is accumulated and analyzed by AI. The AI ​​analyzes the content, pronunciation, grammar, etc. of the user's speech and identifies areas for improvement. For example, it can point out unclear pronunciation or grammatical errors. Based on the analysis results, the system provides users with appropriate feedback and advice. For example, it suggests ways to improve pronunciation or appropriate expressions. This allows users to identify their weaknesses and practice effectively. This system allows users to practice business English in their own words without fear of failure. For example, even if a user uses an incorrect expression, the AI ​​provides appropriate feedback, allowing them to continue practicing with peace of mind. In addition, the accumulated data can be used as killer content for companies. For example, new learning content and services can be developed based on user conversation data. This allows companies to provide competitive services. This allows the business English conversation practice system to efficiently collect and analyze user conversation data and provide appropriate feedback.

[0029] The business English conversation practice system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user conversation data. The user conversation data includes, but is not limited to, voice data, text data, and video data. The collection unit collects conversation data in real time, for example. The collection unit can also collect conversation data periodically. The collection unit can also collect conversation data based on a specific trigger. For example, the collection unit collects conversation data when a user utters a specific keyword. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the user's speech using, for example, speech recognition technology. The analysis unit can also analyze the user's speech using natural language processing technology. The analysis unit can also analyze the user's speech using a machine learning algorithm. For example, the analysis unit analyzes the user's pronunciation using speech waveform analysis. The analysis unit can also analyze the user's pronunciation using phoneme recognition. The analysis unit can also analyze the user's pronunciation using a pronunciation evaluation algorithm. The analysis unit analyzes the user's grammar using a grammar check tool. The analysis unit can also analyze the user's grammar using syntax analysis. The analysis unit can also analyze the user's grammar using grammatical error detection. The provision unit provides feedback based on the analysis results obtained by the analysis unit. The provision unit can provide feedback using, for example, a text message. The provision unit can also provide feedback using an audio message. The provision unit can also provide feedback using a video message. For example, the provision unit can provide pronunciation improvement methods using a text message. The provision unit can also provide pronunciation improvement methods using an audio message. The provision unit can also provide pronunciation improvement methods using a video message. The provision unit can provide appropriate expression methods using a text message. The provision unit can also provide appropriate expression methods using an audio message. The provision unit can also provide appropriate expression methods using a video message. This allows the business English conversation practice system to efficiently collect and analyze the user's conversation data and provide appropriate feedback.

[0030] The collection unit can collect data on business English conversations conducted by a user through a messaging app. For example, the collection unit collects data on business English conversations conducted by a user through a messaging app. For example, when a user types "I want to talk about the progress of the meeting" in a messaging app, the collection unit collects the conversation data. The collection unit can also collect data on business English conversations conducted by a user through WhatsApp (registered trademark). For example, when a user types "I want to talk about preparing for the presentation" in WhatsApp, the collection unit collects the conversation data. The collection unit can also collect data on business English conversations conducted by a user through Facebook (registered trademark) Messenger. For example, when a user types "I want to talk about the meeting with the client" in Facebook Messenger, the collection unit collects the conversation data. This allows users to easily practice business English by collecting data on business English conversations through messaging apps. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input text data entered by a user through a messaging app into a generation AI, which then collects the text data.

[0031] The analysis unit can analyze the collected data and identify areas for improvement in the user's speech content, pronunciation, and grammar. The analysis unit, for example, analyzes the collected data using speech recognition technology. For example, the analysis unit can analyze the user's speech content using keyword extraction technology. The analysis unit can also analyze the collected data using natural language processing technology. For example, the analysis unit can analyze the user's speech content using grammar analysis technology. The analysis unit can also analyze the collected data using a machine learning algorithm. For example, the analysis unit can analyze the user's speech content using semantic analysis technology. The analysis unit can analyze the user's pronunciation using speech waveform analysis. For example, the analysis unit can analyze the user's pronunciation using phoneme recognition technology. The analysis unit can also analyze the user's pronunciation using a pronunciation evaluation algorithm. The analysis unit can analyze the user's grammar using a grammar check tool. For example, the analysis unit can analyze the user's grammar using syntactic analysis technology. The analysis unit can also analyze the user's grammar using grammatical error detection technology. This allows for effective feedback by identifying areas for improvement in the user's speech content, pronunciation, grammar, etc. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collected voice data into a generation AI, which then analyzes the speech content, pronunciation, and grammar.

[0032] The providing unit can provide feedback on how to improve pronunciation and appropriate expressions based on the analysis results. The providing unit, for example, provides the pronunciation improvement method in the form of a text message based on the analysis results. For example, the providing unit sends a text message that specifically indicates how to improve the user's pronunciation. The providing unit can also provide the pronunciation improvement method in the form of an audio message based on the analysis results. For example, the providing unit sends a audio message that specifically indicates how to improve the user's pronunciation. The providing unit can also provide the pronunciation improvement method in the form of a video message based on the analysis results. For example, the providing unit sends a video message that specifically indicates how to improve the user's pronunciation. The providing unit provides appropriate expressions based on the analysis results in the form of a text message. For example, the providing unit sends a text message that specifically indicates appropriate expressions for the user. The providing unit can also provide appropriate expressions based on the analysis results in the form of an audio message. For example, the providing unit sends a audio message that specifically indicates appropriate expressions for the user. The providing unit can also provide appropriate expressions based on the analysis results in the form of a video message. For example, the providing unit sends a video message that specifically indicates appropriate expressions for the user. In this way, by providing feedback based on the analysis results, the user's business English conversation ability is improved. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the analysis results to a generating AI, which may generate feedback on how to improve pronunciation or appropriate expression methods.

[0033] The providing unit can provide appropriate feedback when the user uses an incorrect expression. For example, when the user uses an incorrect expression, the providing unit transmits a text message pointing out the error. For example, the providing unit transmits a text message that specifically indicates the incorrect expression used by the user and suggests a correct expression. The providing unit can also transmit an audio message pointing out the error when the user uses an incorrect expression. For example, the providing unit transmits an audio message that specifically indicates the incorrect expression used by the user and suggests a correct expression. Furthermore, the providing unit can also transmit a video message pointing out the error when the user uses an incorrect expression. For example, the providing unit transmits a video message that specifically indicates the incorrect expression used by the user and suggests a correct expression. This allows the user to continue practicing with peace of mind by providing appropriate feedback even if they use an incorrect expression. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the incorrect expression used by the user to a generation AI, which then generates a correct expression.

[0034] The collection unit can collect data for developing new learning content or services based on the accumulated data. For example, the collection unit accumulates user conversation data and collects data for developing new learning content based on the data. For example, the collection unit analyzes the user conversation data to identify frequently used phrases and expressions. The collection unit can also collect data for developing new exercises or lessons based on the user conversation data. For example, the collection unit analyzes the user conversation data to collect data on specific topics. The collection unit can also collect data for developing new interactive learning materials based on the user conversation data. For example, the collection unit analyzes the user conversation data to develop learning materials tailored to the user's learning needs. This allows new learning content or services to be developed using the accumulated data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the accumulated data into a generation AI and collect data for developing new learning content or services using the generation AI.

[0035] The collection unit can analyze the user's past conversation history and select an appropriate collection method. The collection unit, for example, analyzes the user's past conversation history and selects the optimal collection method. For example, the collection unit prioritizes collection of conversation scenarios that the user has frequently used in the past. The collection unit can also concentrate collection on a specific time period from the user's past conversation history. Furthermore, the collection unit can also focus collection of data on a specific topic based on the user's past conversation history. In this way, the optimal collection method can be selected by analyzing the past conversation history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past conversation history data into a generation AI, and the generation AI can select the optimal collection method.

[0036] The collection unit can filter the conversation data based on the user's current business situation or areas of interest when collecting the conversation data. For example, the collection unit can filter the conversation data based on the user's current business situation or areas of interest when collecting the conversation data. For example, the collection unit prioritizes collecting conversation data related to a project the user is currently working on. The collection unit can also filter and collect related conversation data based on the user's areas of interest. Furthermore, the collection unit can select and collect appropriate conversation data according to the user's business situation. This makes it possible to collect highly relevant data by filtering data based on the user's business situation or areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data related to the user's business situation or areas of interest into a generation AI, and the generation AI can perform filtering.

[0037] When collecting conversation data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when collecting conversation data, the collection unit prioritizes collecting highly relevant data by taking the user's geographical location information into consideration. For example, when collecting conversation data, the collection unit prioritizes collecting business English conversation data related to that area when the user is in a specific region. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting conversation data related to the business trip destination. Furthermore, when the user is overseas, the collection unit can prioritize collecting conversation data related to the business culture of that country. In this way, highly relevant data can be collected by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the generation AI, and the generation AI can prioritize collecting highly relevant data.

[0038] The collection unit can analyze the user's online activities and collect related data when collecting conversation data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting conversation data. For example, the collection unit can collect business terms frequently used by the user on social media. The collection unit can also collect conversation data related to the user's topics of interest on social media. Furthermore, the collection unit can determine the optimal collection timing based on the user's time spent active on social media. This allows related data to be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI, which can collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the conversation. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the conversation. For example, the analysis unit performs a detailed analysis of important conversation content. The analysis unit can also perform a concise analysis of general conversation content. Furthermore, the analysis unit can also focus on analyzing conversations that are important in a specific business situation. In this way, by adjusting the level of detail of the analysis based on the importance of the conversation, important conversation content can be analyzed in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation importance data to a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the type of conversation. The analysis unit applies different analysis algorithms depending on, for example, the category of the conversation. For example, the analysis unit applies a conference-specific analysis algorithm to a conversation about the progress of a meeting. The analysis unit can also apply a presentation-specific analysis algorithm to a conversation about a presentation. Furthermore, the analysis unit can apply a negotiation-specific analysis algorithm to a conversation about a negotiation. In this way, applying different analysis algorithms depending on the category of the conversation enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation category data to a generation AI and have the generation AI apply different analysis algorithms.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the submission date and time of the conversation. The analysis unit determines the priority of analysis based, for example, on the time of submission of the conversation. For example, the analysis unit prioritizes analysis of the most recent conversation data. The analysis unit can also prioritize analysis of conversation data from a specific period specified by the user. Furthermore, the analysis unit can also prioritize analysis of important conversation data. In this way, by determining the priority of analysis based on the time of submission of the conversation, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission date and time data of the conversation to a generation AI, and the generation AI can determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the conversation. The analysis unit adjusts the order of analysis based on, for example, the relevance of the conversation. For example, the analysis unit prioritizes analysis of highly relevant conversation data. The analysis unit can also prioritize analysis of conversation data related to the user's field of interest. Furthermore, the analysis unit can also prioritize analysis of conversation data that is important in business situations. In this way, by adjusting the order of analysis based on the relevance of the conversation, highly relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation relevance data to a generation AI, and the generation AI can adjust the order of analysis.

[0043] When providing feedback, the providing unit can select appropriate feedback by referring to the user's past feedback history. When providing feedback, the providing unit, for example, can provide optimal feedback by referring to the user's past feedback history. For example, the providing unit can provide optimal feedback based on feedback the user has received in the past. The providing unit can also provide feedback that focuses on areas for improvement based on the user's past feedback history. Furthermore, the providing unit can analyze the user's past feedback history and provide effective feedback. In this way, optimal feedback can be provided by referring to the past feedback history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback history data into a generation AI, which can select optimal feedback.

[0044] The providing unit can adjust the means of feedback based on the user's current learning situation when providing feedback. For example, the providing unit customizes the means of feedback based on the user's current learning situation when providing feedback. For example, the providing unit can provide basic feedback if the user is a beginner. The providing unit can also provide detailed feedback if the user is an intermediate learner. Furthermore, the providing unit can also provide specialized feedback if the user is an advanced learner. This allows for effective feedback to be provided by customizing the means of feedback according to the user's learning situation. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's learning situation data into a generating AI, and the generating AI can adjust the means of feedback.

[0045] The providing unit can select an appropriate feedback method based on the user's geographical location information when providing feedback. For example, the providing unit selects the optimal feedback method by taking the user's geographical location information into consideration when providing feedback. For example, if the user is in a specific area, the providing unit can provide feedback related to that area. Also, if the user is on a business trip, the providing unit can provide feedback related to the business trip destination. Furthermore, if the user is overseas, the providing unit can provide feedback related to the business culture of that country. In this way, by taking the user's geographical location information into consideration, highly relevant feedback can be provided. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's geographical location information into a generating AI, which can select the optimal feedback method.

[0046] The providing unit may analyze the user's online activities and suggest a means of providing feedback when providing feedback. For example, the providing unit may analyze the user's social media activities and suggest a means of providing feedback when providing feedback. For example, the providing unit may provide feedback based on business terms frequently used by the user on social media. The providing unit may also provide feedback related to topics of interest to the user on social media. Furthermore, the providing unit may suggest an optimal feedback timing based on the user's time spent active on social media. In this way, the optimal means of feedback can be suggested by analyzing the user's social media activities. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's social media activity data into a generating AI, which may suggest a means of feedback.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The business English conversation practice system may further include a progress management unit that tracks the user's learning progress. The progress management unit analyzes the user's past practice data and evaluates the degree of progress the user has made. For example, the progress management unit visualizes the user's learning progress based on the number and content of the user's past practice sessions. The progress management unit can also evaluate the user's achievement level toward goals set by the user and provide advice for achieving those goals. Furthermore, the progress management unit can suggest the next practice content to tackle based on the user's learning pace. This allows the user to understand their own learning progress and study effectively.

[0049] When analyzing the content of a user's speech, the analysis unit can perform a more accurate analysis by referring to the user's past speech history. For example, the analysis unit identifies phrases and expressions that the user has frequently used in the past and analyzes the current content of the speech based on them. The analysis unit can also identify specific grammatical errors or pronunciation problems from the user's past speech history and analyze the current content of the speech based on them. Furthermore, the analysis unit can evaluate the user's learning progress based on the user's past speech history and provide analysis results accordingly. This makes it possible to perform a more accurate analysis by utilizing the user's past speech history.

[0050] When providing feedback based on the analysis results, the providing unit can customize the feedback according to the user's learning style. For example, if the user is a visual learner, the providing unit can provide feedback using visual aids. If the user is an auditory learner, the providing unit can also provide feedback using audio messages. Furthermore, if the user is a hands-on learner, the providing unit can also provide interactive practice questions. In this way, effective learning can be supported by providing feedback according to the user's learning style.

[0051] When a user uses an incorrect expression, the providing unit can not only point out the error but also explain the cause of the error. For example, when a user makes a grammatical error, the providing unit can send a text message explaining the grammar rule. Also, when a user makes a pronunciation error, the providing unit can send an audio message showing the correct pronunciation. Furthermore, when a user does not use an appropriate expression, the providing unit can send a video message explaining the background and cultural nuances of the expression. This allows the user to understand the cause of the error and progress with their learning more effectively.

[0052] When collecting user conversation data, the collection unit can determine the priority of data to be collected based on the user's current business situation and areas of interest. For example, the collection unit prioritizes collection of conversation data related to a project the user is currently working on. The collection unit can also filter and collect related conversation data based on the user's areas of interest. Furthermore, the collection unit can select and collect appropriate conversation data according to the user's business situation. This makes it possible to collect highly relevant data by filtering data based on the user's business situation and areas of interest.

[0053] When providing feedback based on the analysis results, the providing unit can provide more effective feedback by referring to the user's past feedback history. For example, the providing unit customizes current feedback based on feedback the user has received in the past. The providing unit can also provide feedback that focuses on specific areas for improvement based on the user's past feedback history. Furthermore, the providing unit can analyze the user's past feedback history and provide feedback according to the user's learning progress. This makes it possible to provide more effective feedback by utilizing the user's past feedback history.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects user conversation data. The user conversation data includes voice data, text data, video data, etc. The collection unit can collect conversation data in real time or periodically. Furthermore, it is also possible to collect conversation data based on a specific trigger. For example, conversation data can be collected when a user utters a specific keyword. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the user's speech using speech recognition technology, natural language processing technology, machine learning algorithms, etc. For example, it uses techniques such as speech waveform analysis, phoneme recognition, pronunciation evaluation algorithms, grammar check tools, syntax analysis, and grammatical error detection. Step 3: The providing unit provides feedback based on the analysis results obtained by the analyzing unit. The providing unit provides feedback using text messages, audio messages, video messages, etc. For example, the providing unit provides methods for improving pronunciation or appropriate ways of expressing oneself using text messages, audio messages, or video messages.

[0056] (Example 2) A business English conversation practice system according to an embodiment of the present invention provides easy-to-use business English conversation through messaging apps (e.g., LINE). This system allows users to practice business English through messaging apps. The system accumulates user conversation data and analyzes it using AI. Based on the analysis results, appropriate feedback and advice is provided to the user. This system allows users to practice business English in their own words without fear of failure. The accumulated data can also be used as a company's killer content. For example, if a user inputs, "I would like to talk about the progress of the meeting," the AI ​​provides an appropriate conversation scenario. The user can then advance the conversation according to the scenario. Next, the user's conversation data is accumulated and analyzed by AI. The AI ​​analyzes the user's speech content, pronunciation, grammar, and other factors to identify areas for improvement. For example, it can point out unclear pronunciation and grammatical errors. Based on the analysis results, the system provides the user with appropriate feedback and advice. For example, it suggests ways to improve pronunciation and appropriate expressions. This allows users to identify their weaknesses and effectively practice. This system allows users to practice business English in their own words without fear of failure. For example, even if a user uses an incorrect expression, the AI ​​provides appropriate feedback, allowing them to continue practicing with peace of mind. In addition, the accumulated data can be used as killer content for companies. For example, new learning content and services can be developed based on user conversation data, allowing companies to provide competitive services. This allows the business English conversation practice system to efficiently collect and analyze user conversation data and provide appropriate feedback.

[0057] The business English conversation practice system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user conversation data. The user conversation data includes, but is not limited to, voice data, text data, and video data. The collection unit collects conversation data in real time, for example. The collection unit can also collect conversation data periodically. The collection unit can also collect conversation data based on a specific trigger. For example, the collection unit collects conversation data when a user utters a specific keyword. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the user's speech using, for example, speech recognition technology. The analysis unit can also analyze the user's speech using natural language processing technology. The analysis unit can also analyze the user's speech using a machine learning algorithm. For example, the analysis unit analyzes the user's pronunciation using speech waveform analysis. The analysis unit can also analyze the user's pronunciation using phoneme recognition. The analysis unit can also analyze the user's pronunciation using a pronunciation evaluation algorithm. The analysis unit analyzes the user's grammar using a grammar check tool. The analysis unit can also analyze the user's grammar using syntax analysis. The analysis unit can also analyze the user's grammar using grammatical error detection. The provision unit provides feedback based on the analysis results obtained by the analysis unit. The provision unit can provide feedback using, for example, a text message. The provision unit can also provide feedback using an audio message. The provision unit can also provide feedback using a video message. For example, the provision unit can provide pronunciation improvement methods using a text message. The provision unit can also provide pronunciation improvement methods using an audio message. The provision unit can also provide pronunciation improvement methods using a video message. The provision unit can provide appropriate expression methods using a text message. The provision unit can also provide appropriate expression methods using an audio message. The provision unit can also provide appropriate expression methods using a video message. This allows the business English conversation practice system to efficiently collect and analyze the user's conversation data and provide appropriate feedback.

[0058] The collection unit can collect data on business English conversations conducted by a user through a messaging app. For example, the collection unit collects data on business English conversations conducted by a user through a messaging app. For example, when a user types "I want to talk about the progress of the meeting" in a messaging app, the collection unit collects the conversation data. The collection unit can also collect data on business English conversations conducted by a user through WhatsApp. For example, when a user types "I want to talk about preparing for the presentation" in WhatsApp, the collection unit collects the conversation data. The collection unit can also collect data on business English conversations conducted by a user through Facebook Messenger. For example, when a user types "I want to talk about the meeting with the client" in Facebook Messenger, the collection unit collects the conversation data. This allows users to easily practice business English by collecting data on business English conversations through messaging apps. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input text data entered by a user through a messaging app into a generation AI, which then collects the text data.

[0059] The analysis unit can analyze the collected data and identify areas for improvement in the user's speech content, pronunciation, and grammar. The analysis unit, for example, analyzes the collected data using speech recognition technology. For example, the analysis unit can analyze the user's speech content using keyword extraction technology. The analysis unit can also analyze the collected data using natural language processing technology. For example, the analysis unit can analyze the user's speech content using grammar analysis technology. The analysis unit can also analyze the collected data using a machine learning algorithm. For example, the analysis unit can analyze the user's speech content using semantic analysis technology. The analysis unit can analyze the user's pronunciation using speech waveform analysis. For example, the analysis unit can analyze the user's pronunciation using phoneme recognition technology. The analysis unit can also analyze the user's pronunciation using a pronunciation evaluation algorithm. The analysis unit can analyze the user's grammar using a grammar check tool. For example, the analysis unit can analyze the user's grammar using syntactic analysis technology. The analysis unit can also analyze the user's grammar using grammatical error detection technology. This allows for effective feedback by identifying areas for improvement in the user's speech content, pronunciation, grammar, etc. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collected voice data into a generation AI, which then analyzes the speech content, pronunciation, and grammar.

[0060] The providing unit can provide feedback on how to improve pronunciation and appropriate expressions based on the analysis results. The providing unit, for example, provides the pronunciation improvement method in the form of a text message based on the analysis results. For example, the providing unit sends a text message that specifically indicates how to improve the user's pronunciation. The providing unit can also provide the pronunciation improvement method in the form of an audio message based on the analysis results. For example, the providing unit sends a audio message that specifically indicates how to improve the user's pronunciation. The providing unit can also provide the pronunciation improvement method in the form of a video message based on the analysis results. For example, the providing unit sends a video message that specifically indicates how to improve the user's pronunciation. The providing unit provides appropriate expressions based on the analysis results in the form of a text message. For example, the providing unit sends a text message that specifically indicates appropriate expressions for the user. The providing unit can also provide appropriate expressions based on the analysis results in the form of an audio message. For example, the providing unit sends a audio message that specifically indicates appropriate expressions for the user. The providing unit can also provide appropriate expressions based on the analysis results in the form of a video message. For example, the providing unit sends a video message that specifically indicates appropriate expressions for the user. In this way, by providing feedback based on the analysis results, the user's business English conversation ability is improved. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the analysis results to a generating AI, which may generate feedback on how to improve pronunciation or appropriate expression methods.

[0061] The providing unit can provide appropriate feedback when the user uses an incorrect expression. For example, when the user uses an incorrect expression, the providing unit transmits a text message pointing out the error. For example, the providing unit transmits a text message that specifically indicates the incorrect expression used by the user and suggests a correct expression. The providing unit can also transmit an audio message pointing out the error when the user uses an incorrect expression. For example, the providing unit transmits an audio message that specifically indicates the incorrect expression used by the user and suggests a correct expression. Furthermore, the providing unit can also transmit a video message pointing out the error when the user uses an incorrect expression. For example, the providing unit transmits a video message that specifically indicates the incorrect expression used by the user and suggests a correct expression. This allows the user to continue practicing with peace of mind by providing appropriate feedback even if they use an incorrect expression. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the incorrect expression used by the user to a generation AI, which then generates a correct expression.

[0062] The collection unit can collect data for developing new learning content or services based on the accumulated data. For example, the collection unit accumulates user conversation data and collects data for developing new learning content based on the data. For example, the collection unit analyzes the user conversation data to identify frequently used phrases and expressions. The collection unit can also collect data for developing new exercises or lessons based on the user conversation data. For example, the collection unit analyzes the user conversation data to collect data on specific topics. The collection unit can also collect data for developing new interactive learning materials based on the user conversation data. For example, the collection unit analyzes the user conversation data to develop learning materials tailored to the user's learning needs. This allows new learning content or services to be developed using the accumulated data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the accumulated data into a generation AI and collect data for developing new learning content or services using the generation AI.

[0063] The collection unit can analyze the user's emotions and adjust the timing of conversation data collection based on the analyzed user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of conversation data collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit frequently collects conversation data to maintain a natural conversation flow. Furthermore, when the user is stressed, the collection unit can also moderate the collection of conversation data to reduce the user's burden. Furthermore, when the user is concentrating, the collection unit can timely collect conversation data to obtain detailed data. This allows for more natural conversation data to be collected by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI, and the generation AI can adjust the collection timing.

[0064] The collection unit can analyze the user's past conversation history and select an appropriate collection method. The collection unit, for example, analyzes the user's past conversation history and selects the optimal collection method. For example, the collection unit prioritizes collection of conversation scenarios that the user has frequently used in the past. The collection unit can also concentrate collection on a specific time period from the user's past conversation history. Furthermore, the collection unit can also focus collection of data on a specific topic based on the user's past conversation history. In this way, the optimal collection method can be selected by analyzing the past conversation history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past conversation history data into a generation AI, and the generation AI can select the optimal collection method.

[0065] The collection unit can filter the conversation data based on the user's current business situation or areas of interest when collecting the conversation data. For example, the collection unit can filter the conversation data based on the user's current business situation or areas of interest when collecting the conversation data. For example, the collection unit prioritizes collecting conversation data related to a project the user is currently working on. The collection unit can also filter and collect related conversation data based on the user's areas of interest. Furthermore, the collection unit can select and collect appropriate conversation data according to the user's business situation. This makes it possible to collect highly relevant data by filtering data based on the user's business situation or areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data related to the user's business situation or areas of interest into a generation AI, and the generation AI can perform filtering.

[0066] The collection unit can analyze the user's emotions and determine the priority of the conversation data to be collected based on the analyzed user's emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the conversation data to be collected based on the estimated user's emotions. For example, the collection unit can prioritize collecting detailed conversation data when the user is relaxed. The collection unit can also prioritize collecting concise conversation data when the user is stressed. Furthermore, the collection unit can prioritize collecting important conversation data when the user is concentrating. In this way, by determining the priority of data based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and determine the priority of the conversation data to be collected by the generation AI.

[0067] When collecting conversation data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when collecting conversation data, the collection unit prioritizes collecting highly relevant data by taking the user's geographical location information into consideration. For example, when collecting conversation data, the collection unit prioritizes collecting business English conversation data related to that area when the user is in a specific region. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting conversation data related to the business trip destination. Furthermore, when the user is overseas, the collection unit can prioritize collecting conversation data related to the business culture of that country. In this way, highly relevant data can be collected by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the generation AI, and the generation AI can prioritize collecting highly relevant data.

[0068] The collection unit can analyze the user's online activities and collect related data when collecting conversation data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting conversation data. For example, the collection unit can collect business terms frequently used by the user on social media. The collection unit can also collect conversation data related to the user's topics of interest on social media. Furthermore, the collection unit can determine the optimal collection timing based on the user's time spent active on social media. This allows related data to be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI, which can collect related data.

[0069] The analysis unit can analyze the user's emotions and adjust the presentation of the analysis based on the analyzed user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the presentation of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise and concise analysis results when the user is stressed. Furthermore, the analysis unit can provide in-depth analysis results when the user is concentrating. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into a generation AI, which can then adjust the presentation of the analysis.

[0070] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the conversation. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the conversation. For example, the analysis unit performs a detailed analysis of important conversation content. The analysis unit can also perform a concise analysis of general conversation content. Furthermore, the analysis unit can also focus on analyzing conversations that are important in a specific business situation. In this way, by adjusting the level of detail of the analysis based on the importance of the conversation, important conversation content can be analyzed in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation importance data to a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0071] During analysis, the analysis unit can apply different analysis algorithms depending on the type of conversation. The analysis unit applies different analysis algorithms depending on, for example, the category of the conversation. For example, the analysis unit applies a conference-specific analysis algorithm to a conversation about the progress of a meeting. The analysis unit can also apply a presentation-specific analysis algorithm to a conversation about a presentation. Furthermore, the analysis unit can apply a negotiation-specific analysis algorithm to a conversation about a negotiation. In this way, applying different analysis algorithms depending on the category of the conversation enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation category data to a generation AI and have the generation AI apply different analysis algorithms.

[0072] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user's emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a brief analysis when the user is stressed. Furthermore, the analysis unit can perform an analysis of an appropriate length when the user is concentrating. This allows for adjusting the length of the analysis based on the user's emotions to provide an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0073] During analysis, the analysis unit can determine the priority of analysis based on the submission date and time of the conversation. The analysis unit determines the priority of analysis based, for example, on the time of submission of the conversation. For example, the analysis unit prioritizes analysis of the most recent conversation data. The analysis unit can also prioritize analysis of conversation data from a specific period specified by the user. Furthermore, the analysis unit can also prioritize analysis of important conversation data. In this way, by determining the priority of analysis based on the time of submission of the conversation, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission date and time data of the conversation to a generation AI, and the generation AI can determine the priority of analysis.

[0074] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the conversation. The analysis unit adjusts the order of analysis based on, for example, the relevance of the conversation. For example, the analysis unit prioritizes analysis of highly relevant conversation data. The analysis unit can also prioritize analysis of conversation data related to the user's field of interest. Furthermore, the analysis unit can also prioritize analysis of conversation data that is important in business situations. In this way, by adjusting the order of analysis based on the relevance of the conversation, highly relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation relevance data to a generation AI, and the generation AI can adjust the order of analysis.

[0075] The providing unit can analyze the user's emotions and adjust the feedback expression method based on the analyzed user's emotions. The providing unit, for example, estimates the user's emotions and adjusts the feedback expression method based on the estimated user's emotions. For example, the providing unit can provide detailed feedback when the user is relaxed. The providing unit can also provide concise and to-the-point feedback when the user is feeling stressed. Furthermore, the providing unit can provide in-depth feedback when the user is concentrating. This allows for adjusting the feedback expression method based on the user's emotions, thereby providing more appropriate feedback. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, and the generation AI can adjust the feedback expression method.

[0076] When providing feedback, the providing unit can select appropriate feedback by referring to the user's past feedback history. When providing feedback, the providing unit, for example, can provide optimal feedback by referring to the user's past feedback history. For example, the providing unit can provide optimal feedback based on feedback the user has received in the past. The providing unit can also provide feedback that focuses on areas for improvement based on the user's past feedback history. Furthermore, the providing unit can analyze the user's past feedback history and provide effective feedback. In this way, optimal feedback can be provided by referring to the past feedback history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback history data into a generation AI, which can select optimal feedback.

[0077] The providing unit can adjust the means of feedback based on the user's current learning situation when providing feedback. For example, the providing unit customizes the means of feedback based on the user's current learning situation when providing feedback. For example, the providing unit can provide basic feedback if the user is a beginner. The providing unit can also provide detailed feedback if the user is an intermediate learner. Furthermore, the providing unit can also provide specialized feedback if the user is an advanced learner. This allows for effective feedback to be provided by customizing the means of feedback according to the user's learning situation. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's learning situation data into a generating AI, and the generating AI can adjust the means of feedback.

[0078] The providing unit can analyze the user's emotions and determine the priority of feedback based on the analyzed user's emotions. The providing unit, for example, estimates the user's emotions and determines the priority of feedback based on the estimated user's emotions. For example, the providing unit can prioritize detailed feedback when the user is relaxed. The providing unit can also prioritize brief feedback when the user is stressed. Furthermore, the providing unit can prioritize important feedback when the user is concentrating. In this way, by determining the priority of feedback based on the user's emotions, important feedback can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, and the generation AI can determine the priority of feedback.

[0079] The providing unit can select an appropriate feedback method based on the user's geographical location information when providing feedback. For example, the providing unit selects the optimal feedback method by taking the user's geographical location information into consideration when providing feedback. For example, if the user is in a specific area, the providing unit can provide feedback related to that area. Also, if the user is on a business trip, the providing unit can provide feedback related to the business trip destination. Furthermore, if the user is overseas, the providing unit can provide feedback related to the business culture of that country. In this way, by taking the user's geographical location information into consideration, highly relevant feedback can be provided. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's geographical location information into a generating AI, which can select the optimal feedback method.

[0080] The providing unit may analyze the user's online activities and suggest a means of providing feedback when providing feedback. For example, the providing unit may analyze the user's social media activities and suggest a means of providing feedback when providing feedback. For example, the providing unit may provide feedback based on business terms frequently used by the user on social media. The providing unit may also provide feedback related to topics of interest to the user on social media. Furthermore, the providing unit may suggest an optimal feedback timing based on the user's time spent active on social media. In this way, the optimal means of feedback can be suggested by analyzing the user's social media activities. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's social media activity data into a generating AI, which may suggest a means of feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user conversation data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides feedback based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user conversation data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides feedback based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user conversation data using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides feedback based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user conversation data using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides feedback based on the analysis results.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The business English conversation practice system may further include a progress management unit that tracks the user's learning progress. The progress management unit analyzes the user's past practice data and evaluates the degree of progress the user has made. For example, the progress management unit visualizes the user's learning progress based on the number and content of the user's past practice sessions. The progress management unit can also evaluate the user's achievement level toward goals set by the user and provide advice for achieving those goals. Furthermore, the progress management unit can suggest the next practice content to tackle based on the user's learning pace. This allows the user to understand their own learning progress and study effectively.

[0083] When collecting user conversation data, the collection unit can analyze the user's voice tone and speech rate to estimate the user's emotional state. For example, if the user's voice tone is high, the collection unit can estimate that the user is excited, and conversely, if the user's voice tone is low, the collection unit can estimate that the user is calm. Also, if the user's speech rate is fast, the collection unit can estimate that the user is nervous, and if the user's speech rate is slow, the collection unit can estimate that the user is relaxed. This allows the collection unit to collect conversation data at an appropriate time depending on the user's emotional state. For example, if the user is relaxed, detailed conversation data can be collected, and if the user is nervous, brief data can be collected. This makes it possible to collect data according to the user's emotional state.

[0084] When analyzing the content of a user's speech, the analysis unit can perform a more accurate analysis by referring to the user's past speech history. For example, the analysis unit identifies phrases and expressions that the user has frequently used in the past and analyzes the current content of the speech based on them. The analysis unit can also identify specific grammatical errors or pronunciation problems from the user's past speech history and analyze the current content of the speech based on them. Furthermore, the analysis unit can evaluate the user's learning progress based on the user's past speech history and provide analysis results accordingly. This makes it possible to perform a more accurate analysis by utilizing the user's past speech history.

[0085] When providing feedback based on the analysis results, the providing unit can customize the feedback according to the user's learning style. For example, if the user is a visual learner, the providing unit can provide feedback using visual aids. If the user is an auditory learner, the providing unit can also provide feedback using audio messages. Furthermore, if the user is a hands-on learner, the providing unit can also provide interactive practice questions. In this way, effective learning can be supported by providing feedback according to the user's learning style.

[0086] When a user uses an incorrect expression, the providing unit can not only point out the error but also explain the cause of the error. For example, when a user makes a grammatical error, the providing unit can send a text message explaining the grammar rule. Also, when a user makes a pronunciation error, the providing unit can send an audio message showing the correct pronunciation. Furthermore, when a user does not use an appropriate expression, the providing unit can send a video message explaining the background and cultural nuances of the expression. This allows the user to understand the cause of the error and progress with their learning more effectively.

[0087] When collecting user conversation data, the collection unit can estimate the user's emotional state and adjust the type of data to be collected based on the estimated emotional state. For example, if the user is relaxed, the collection unit can collect detailed audio data and video data. If the user is stressed, the collection unit can collect concise text data. Furthermore, if the user is focused, the collection unit can focus on collecting data on a specific topic. This allows for more effective data collection by adjusting the type of data to be collected according to the user's emotional state.

[0088] When collecting user conversation data, the collection unit can determine the priority of data to be collected based on the user's current business situation and areas of interest. For example, the collection unit prioritizes collection of conversation data related to a project the user is currently working on. The collection unit can also filter and collect related conversation data based on the user's areas of interest. Furthermore, the collection unit can select and collect appropriate conversation data according to the user's business situation. This makes it possible to collect highly relevant data by filtering data based on the user's business situation and areas of interest.

[0089] When analyzing the content of a user's speech, the analysis unit can estimate the user's emotional state and adjust the level of detail of the analysis based on the estimated emotional state. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is feeling stressed, the analysis unit can also provide a concise and to-the-point analysis result. Furthermore, if the user is concentrating, the analysis unit can also provide an in-depth analysis result. In this way, by adjusting the level of detail of the analysis based on the user's emotional state, more appropriate analysis results can be provided.

[0090] When providing feedback based on the analysis results, the providing unit can estimate the user's emotional state and adjust the way the feedback is expressed based on the estimated emotional state. For example, the providing unit can provide detailed feedback when the user is relaxed. Also, when the user is feeling stressed, the providing unit can provide brief feedback that gets to the point. Furthermore, when the user is concentrating, the providing unit can provide in-depth feedback. In this way, by adjusting the way the feedback is expressed based on the user's emotional state, more appropriate feedback can be provided.

[0091] When providing feedback based on the analysis results, the providing unit can provide more effective feedback by referring to the user's past feedback history. For example, the providing unit customizes current feedback based on feedback the user has received in the past. The providing unit can also provide feedback that focuses on specific areas for improvement based on the user's past feedback history. Furthermore, the providing unit can analyze the user's past feedback history and provide feedback according to the user's learning progress. This makes it possible to provide more effective feedback by utilizing the user's past feedback history.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection unit collects user conversation data. The user conversation data includes voice data, text data, video data, etc. The collection unit can collect conversation data in real time or periodically. Furthermore, it is also possible to collect conversation data based on a specific trigger. For example, conversation data can be collected when a user utters a specific keyword. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the user's speech using speech recognition technology, natural language processing technology, machine learning algorithms, etc. For example, it uses techniques such as speech waveform analysis, phoneme recognition, pronunciation evaluation algorithms, grammar check tools, syntax analysis, and grammatical error detection. Step 3: The providing unit provides feedback based on the analysis results obtained by the analyzing unit. The providing unit provides feedback using text messages, audio messages, video messages, etc. For example, the providing unit provides methods for improving pronunciation or appropriate ways of expressing oneself using text messages, audio messages, or video messages.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user conversation data; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides feedback based on the analysis result obtained by the analyzing unit. A system characterized by:

2. The collecting unit Collect data on business English conversations users have through messaging apps 2. The system of claim 1.

3. The analysis unit Analyzing the collected data to identify areas for improvement in user speech, pronunciation, and grammar 2. The system of claim 1.

4. The providing unit Based on the analysis results, we provide feedback on how to improve your pronunciation and how to express yourself appropriately.

2. The system of claim 1.

5. The providing unit Providing appropriate feedback when users use incorrect expressions 2. The system of claim 1.

6. The collecting unit Collect data to develop new learning content or services based on the accumulated data 2. The system of claim 1.

7. The collecting unit Analyze user emotions and adjust the timing of conversation data collection based on the analyzed user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past conversation history and select the appropriate collection method 2. The system of claim 1.

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