System

Abstract

To provide a system capable of identifying speaking habits and pronunciation weaknesses and giving effective feedback.SOLUTION: A system in an embodiment includes a conversion unit, an analysis unit, a generation unit, and an audio generation unit. The conversion unit converts speech to text. The analysis unit analyzes the text converted by the conversion unit and identifies speaking habits and pronunciation weaknesses. The generation unit generates a report based on the analysis results obtained by the analysis unit. The audio generation unit generates sample audio generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional techniques fall short in efficiently identifying speaking habits and pronunciation weaknesses and providing feedback for improvement.

[0005] The system according to the embodiment aims to identify speaking habits and weak points in pronunciation and provide effective feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversion unit, an analysis unit, a generation unit, and a voice generation unit. The conversion unit converts voice into text. The analysis unit analyzes the text converted by the conversion unit and identifies speaking habits and weak points in pronunciation. The generation unit generates a report based on the analysis results obtained by the analysis unit. The voice generation unit generates the model voice generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can identify speaking habits and pronunciation weaknesses and provide effective feedback. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The English speaking improvement system according to the embodiment of the present invention is a system in which a user records a conversation or a single talk, analyzes the voice of the user using a generation AI, identifies the user's speaking habits and weak points in pronunciation, and proposes alternative phrases and improvements. This system converts the voice recorded by the user into text, and the generation AI analyzes it to identify the user's speaking habits and weak points in pronunciation and proposes alternative expressions. For example, if a user says, "The weather is nice today," the voice is converted into text, and the generation AI proposes an alternative expression such as "It's sunny today and it feels good." In addition, the generation AI generates a report based on the analysis result and provides it to the user. This report includes the user's speaking habits and weak points in pronunciation, alternative phrases, and improvements. For example, specific advice such as "You tend to use 'desu' a lot. If you use 'desu' instead, it will be a more natural expression" is described. In addition, the generation AI generates a model voice and transmits it to the user through a voice output device. This allows the user to obtain specific references for improving their pronunciation and speaking style. For example, by listening to a model voice saying "It's sunny today and it feels nice," the user can correct his / her own pronunciation. This system enables the user to understand the weaknesses of his / her own speaking style and pronunciation and learn specific ways to improve. This improves practical English speaking ability and enables more natural English conversation. In this way, the English speaking improvement system can identify the weaknesses of the user's speaking style and pronunciation and provide specific ways to improve them.

[0029] The English speaking improvement system according to the embodiment includes a conversion unit, an analysis unit, a generation unit, and a voice generation unit. The conversion unit converts a voice recorded by a user into text. For example, the conversion unit converts the voice into text using a voice recognition technology. The voice recognition technology includes deep learning-based voice recognition and HMM (Hidden Markov Model)-based voice recognition. The analysis unit uses a generation AI to analyze the text converted by the conversion unit and identify speaking habits and weak points in pronunciation. For example, the analysis unit uses a natural language processing technology to analyze the text and identify speaking habits such as speech speed, intonation, and rhythm, and weak points in pronunciation such as pronunciation of specific sounds and accent placement. The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for each situation, such as a business scene or a casual conversation. For example, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today." The generation unit generates a report including the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement based on the analysis results. The voice generation unit generates a model voice generated by the generation unit. For example, the voice generation unit cooperates with a voice output device such as a smartphone or a smart speaker, and transmits the generated model voice to the user. This allows the user to obtain specific references for improving their pronunciation and speaking style. As a result, the English speaking improvement system according to the embodiment can identify the weak points of the user's speaking style and pronunciation, and provide a specific method for improvement.

[0030] The conversion unit converts the voice recorded by the user into text. For example, the conversion unit converts the voice into text using a voice recognition technology. Voice recognition technologies include deep learning-based voice recognition and HMM (Hidden Markov Model)-based voice recognition. Specifically, deep learning-based voice recognition uses a neural network to analyze voice data and recognize phonemes and words with high accuracy. This allows the content spoken by the user to be accurately converted into text. In addition, HMM-based voice recognition converts voice into text by modeling the temporal fluctuation of the voice and probabilistically analyzing the voice signal. By combining these technologies, the conversion unit can convert voices of various environments and speakers into text with high accuracy. Furthermore, the conversion unit can use noise reduction technology to remove background noise and improve the accuracy of voice recognition. For example, even if the user records a voice in a noisy environment, clear voice data can be obtained by using noise reduction technology, and accurate text conversion can be achieved. This allows the conversion unit to accurately and quickly convert the user's voice into text and provide the data required for subsequent analysis and generation processes.

[0031] The analysis unit uses the generation AI to analyze the text converted by the conversion unit and identify speaking habits and weak points in pronunciation. For example, the analysis unit uses natural language processing technology to analyze the text and identify speaking habits such as speaking speed, intonation, and rhythm, and pronunciation weak points such as the pronunciation of specific sounds and the location of the accent. Specifically, the generation AI receives the user's text data as input and analyzes the speaking speed and intonation patterns. For example, it detects cases where the user tends to speak specific words quickly or when the intonation is flat. In addition, it identifies cases where specific phonemes or accent locations are not accurate for pronunciation weak points. For example, it analyzes cases where the pronunciation of "r" or "l" is unclear or where the accent is not placed in the correct position. Based on this information, the analysis unit can grasp the user's speaking style and pronunciation patterns in detail and identify areas for improvement. Furthermore, the analysis unit can relatively evaluate the user's speaking style and pronunciation characteristics by comparing them with past data and data of other users. This enables the analysis unit to accurately identify weak points in the user's speaking style and pronunciation and provide basic data for proposing specific methods for improvement.

[0032] The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for each situation, such as business scenes and casual conversations. For example, the generation unit proposes an alternative expression such as "It's sunny today and it feels good" for the phrase "The weather is nice today." The generation unit generates a report based on the analysis results, including the user's speaking habits and pronunciation weaknesses, alternative phrases, and improvements. Specifically, the generation AI considers the characteristics of the user's speaking style and pronunciation and proposes appropriate alternative expressions and improvement methods. For example, in business scenes, it proposes more formal expressions, and in casual conversations, it proposes more natural expressions. In addition, the generation unit specifically indicates the improvements in the user's speaking style and pronunciation and suggests how to practice. For example, it specifically indicates how to practice the pronunciation of specific phonemes and how to improve intonation. In addition, the generation unit tracks the user's progress and regularly updates the report to support continuous improvement. In this way, the generation unit can effectively support the improvement of the user's speaking style and pronunciation, and improve English speaking ability.

[0033] The voice generation unit generates the model voice generated by the generation unit. For example, the voice generation unit cooperates with a voice output device such as a smartphone or a smart speaker, and conveys the generated model voice to the user. Specifically, the voice generation unit generates the model voice based on the alternative expressions and improvements proposed by the generation unit. For example, for the phrase "The weather is nice today," the voice generation unit generates a model voice of "It's sunny today and it feels good" with correct intonation and pronunciation. The voice generation unit uses synthetic voice technology to generate natural and easy-to-listen voice. This allows the user to obtain specific references for improving their pronunciation and speaking style. Furthermore, the voice generation unit can receive feedback from the user and continuously improve the quality of the generated voice. For example, if the user feels that a particular voice is difficult to hear, the voice generation algorithm is adjusted based on the feedback to generate a voice that is easier to hear. In addition, the voice generation unit can provide a model voice with adjusted difficulty according to the user's progress. This allows the voice generation unit to effectively support the improvement of the user's English speaking ability and promote continuous learning.

[0034] The analysis unit can perform analysis by combining speech recognition technology and natural language processing technology. The analysis unit performs analysis by combining, for example, speech recognition technology and natural language processing technology. The speech recognition technology includes deep learning-based speech recognition and HMM (Hidden Markov Model)-based speech recognition. The natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, and the like. For example, the analysis unit converts speech into text using speech recognition technology and analyzes the text using natural language processing technology. This improves the accuracy of analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can convert speech into text and analyze the text using an AI model that combines speech recognition technology and natural language processing technology.

[0035] The generation unit can propose different alternative expressions for each situation, such as a business scene or a casual conversation. The generation unit proposes different alternative expressions for each situation, such as a business scene or a casual conversation. Business scenes include meetings, presentations, negotiations, etc. Casual conversations include daily conversations, conversations with friends, and chats. For example, in a business scene, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today." Also, in a casual conversation, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today." This improves the user's ability to express themselves. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can propose alternative expressions using an AI model that proposes different alternative expressions for each situation, such as a business scene or a casual conversation.

[0036] The voice generation unit can cooperate with a voice output device such as a smartphone or a smart speaker. The voice generation unit cooperates with a voice output device such as a smartphone or a smart speaker. The smartphone includes an iOS device or an Android device. The smart speaker includes an Amazon (registered trademark) Echo or a Google (registered trademark) Home. For example, the voice generation unit cooperates with a smartphone and transmits the generated voice of the model to the user through the smartphone's speaker. In addition, the voice generation unit cooperates with a smart speaker and transmits the generated voice of the model to the user through the smart speaker's speaker. This allows the user to easily listen to the generated voice of the model. A part or all of the above-mentioned processing in the voice generation unit may be performed, for example, using AI or may be performed without using AI. For example, the voice generation unit can transmit the generated voice of the model to the user using an AI model that cooperates with a voice output device such as a smartphone or a smart speaker.

[0037] The conversion unit can automatically remove background noise from the voice to perform more accurate text conversion. The conversion unit can, for example, automatically remove background noise from the voice to perform more accurate text conversion. Background noise includes environmental sounds, noise, echoes, and the like. For example, the conversion unit filters ambient noise during recording to obtain clear voice data. The conversion unit can also remove noise in a specific frequency band during voice recognition to improve the accuracy of text conversion. Furthermore, the conversion unit can also apply noise reduction technology after recording to convert voice data with minimal noise into text. This improves the accuracy of text conversion by removing background noise. Some or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can perform text conversion using an AI model that automatically removes background noise from the voice to perform more accurate text conversion.

[0038] The conversion unit can dynamically adjust the conversion algorithm according to the user's speaking speed and volume when recording the voice. For example, the conversion unit dynamically adjusts the conversion algorithm according to the user's speaking speed and volume when recording the voice. The speaking speed includes the number of words and the number of syllables per unit time. The volume includes measurement in decibels (dB) and adjustment of the volume level. For example, when the user speaks quickly, the conversion unit increases the sampling rate of the voice recognition to improve the accuracy of the text conversion. In addition, when the user speaks softly, the conversion unit can also increase the sensitivity of the voice recognition to perform accurate text conversion. Furthermore, the conversion unit can dynamically adjust the parameters of the conversion algorithm according to the user's speaking speed to achieve optimal text conversion. As a result, optimal text conversion is possible by adjusting the conversion algorithm according to the user's speaking speed and volume. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can perform text conversion using an AI model that dynamically adjusts the conversion algorithm according to the user's speaking speed and volume.

[0039] The conversion unit can appropriately convert dialects and expressions specific to a region based on the geographical location information of the user when recording the voice. For example, the conversion unit appropriately converts dialects and expressions specific to a region in consideration of the geographical location information of the user when recording the voice. The geographical location information includes location estimation from GPS data and IP addresses. The dialects and expressions specific to a region include Kansai dialect and Tohoku dialect. For example, when the user is in the Kansai region, the conversion unit performs text conversion taking into account the dialect of the Kansai dialect. Furthermore, when the user is in New York, the conversion unit can also appropriately convert expressions specific to New York. Furthermore, when the user is in London, the conversion unit can also perform text conversion taking into account pronunciations and expressions specific to London. In this way, dialects and expressions specific to a region can be appropriately converted by taking into account the geographical location information. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can perform text conversion using an AI model that appropriately converts dialects and expressions specific to a region in consideration of the geographical location information of the user when recording the voice.

[0040] The conversion unit can analyze the social media activity of the user when recording the voice, and convert related terms and phrases preferentially. The conversion unit can, for example, analyze the social media activity of the user when recording the voice, and convert related terms and phrases preferentially. Social media activity includes the content of posts, comments, the number of likes, and the like. For example, the conversion unit preferentially converts social media terms frequently used by the user into text. The conversion unit can also preferentially convert phrases related to a specific topic from the social media activity of the user. Furthermore, the conversion unit can also analyze the content of posts on the social media of the user and appropriately convert related terms into text. In this way, by analyzing the social media activity, related terms and phrases can be preferentially converted. A part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can perform text conversion using an AI model that analyzes the social media activity of the user when recording the voice and preferentially converts related terms and phrases.

[0041] The analysis unit can improve the analysis accuracy by referring to the user's past conversation data during the analysis. The analysis unit can improve the analysis accuracy by referring to the user's past conversation data, for example, during the analysis. The past conversation data includes recording data and text logs. For example, the analysis unit can identify a speaking pattern based on the user's past conversation data and improve the analysis accuracy. The analysis unit can also more accurately identify weak points in pronunciation by referring to the user's past pronunciation data. Furthermore, the analysis unit can analyze the frequency of use of specific phrases and expressions from the user's past conversation data and reflect it in the analysis. As a result, the analysis accuracy is improved by referring to the past conversation data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that improves the analysis accuracy by referring to the user's past conversation data during the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the level of the language spoken by the user during analysis. For example, the analysis unit applies different analysis algorithms depending on the level of the language spoken by the user during analysis. The language level includes CEFR (Common European Framework of Reference for Languages) level and TOEFL score. For example, the analysis unit applies an algorithm that analyzes basic pronunciation and grammar for beginners. The analysis unit can also apply an algorithm that analyzes more advanced pronunciation and grammar for intermediate learners. Furthermore, the analysis unit can apply an algorithm that analyzes native-level pronunciation and grammar for advanced learners. This enables more appropriate analysis by applying an analysis algorithm according to the language level of the user. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that applies different analysis algorithms depending on the level of the language spoken by the user during analysis.

[0043] The analysis unit can analyze the region-specific pronunciation and expressions based on the geographical location information of the user during the analysis. For example, the analysis unit analyzes the region-specific pronunciation and expressions taking into account the geographical location information of the user during the analysis. The geographical location information includes GPS data and location estimation from an IP address. The region-specific pronunciation and expressions include Kansai dialect, Tohoku dialect, and the like. For example, when the user is in the Kansai region, the analysis unit analyzes the Kansai dialect pronunciation and expressions. Furthermore, when the user is in New York, the analysis unit can also analyze New York-specific pronunciation and expressions. Furthermore, when the user is in London, the analysis unit can also analyze London-specific pronunciation and expressions. This allows the region-specific pronunciation and expressions to be appropriately analyzed by taking into account the geographical location information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that analyzes the region-specific pronunciation and expressions taking into account the geographical location information of the user during the analysis.

[0044] The analysis unit can analyze the user's social media activity during the analysis and identify related speaking habits and pronunciation weaknesses. The analysis unit can, for example, analyze the user's social media activity during the analysis and identify related speaking habits and pronunciation weaknesses. The social media activity includes the content of posts, comments, the number of likes, and the like. For example, the analysis unit can analyze social media terms frequently used by the user and identify speaking habits. The analysis unit can also identify pronunciation weaknesses related to a specific topic from the user's social media activity. Furthermore, the analysis unit can analyze the content of the user's social media posts and identify related speaking habits and pronunciation weaknesses. In this way, the analysis of the social media activity can identify related speaking habits and pronunciation weaknesses. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that analyzes the user's social media activity during the analysis and identifies related speaking habits and pronunciation weaknesses.

[0045] The generation unit can suggest more specific improvements by referring to the user's past conversation data when generating a report. The generation unit, for example, suggests more specific improvements by referring to the user's past conversation data when generating a report. The past conversation data includes recording data and text logs. For example, the generation unit suggests specific improvements to pronunciation based on the user's past conversation data. The generation unit can also refer to the user's past pronunciation data to more accurately identify weak points in pronunciation. Furthermore, the generation unit can analyze the frequency of use of specific phrases and expressions from the user's past conversation data and suggest improvements. This makes it possible to suggest more specific improvements by referring to the past conversation data. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that suggests more specific improvements by referring to the user's past conversation data when generating a report.

[0046] The generation unit can apply different report formats according to the level of the language spoken by the user when generating a report. For example, the generation unit applies different report formats according to the level of the language spoken by the user when generating a report. The language level includes CEFR (Common European Framework of Reference for Languages) level and TOEFL score. For example, the generation unit generates a report including basic pronunciation and grammar improvement points for beginners by using the generation AI. The generation unit can also generate a report including more advanced pronunciation and grammar improvement points for intermediate learners by using the generation AI. Furthermore, the generation unit can also generate a report including native-level pronunciation and grammar improvement points for advanced learners by using the generation AI. As a result, a more appropriate report is generated by applying a report format according to the language level of the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that applies different report formats according to the level of the language spoken by the user when generating a report.

[0047] The generation unit can suggest region-specific expressions and improvements based on the geographical location information of the user when generating the report. For example, the generation unit suggests region-specific expressions and improvements in consideration of the geographical location information of the user when generating the report. The geographical location information includes GPS data and location estimation from an IP address. The region-specific expressions and improvements include Kansai dialect and Tohoku dialect. For example, when the user is in the Kansai region, the generation unit suggests improvements to the pronunciation and expressions of the Kansai dialect. Furthermore, when the user is in New York, the generation unit can also suggest improvements to the pronunciation and expressions specific to New York. Furthermore, when the user is in London, the generation unit can also suggest improvements to the pronunciation and expressions specific to London. In this way, region-specific expressions and improvements can be appropriately suggested by taking into account the geographical location information. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that suggests region-specific expressions and improvements in consideration of the geographical location information of the user when generating the report.

[0048] The generation unit can analyze the user's social media activities and suggest related improvements when generating the report. The generation unit can analyze the user's social media activities and suggest related improvements when generating the report, for example. The social media activities include the content of posts, comments, the number of likes, and the like. For example, the generation unit can suggest specific improvements based on social media terms frequently used by the user. The generation unit can also suggest improvements in pronunciation related to a specific topic from the user's social media activities. Furthermore, the generation unit can analyze the content of the user's social media posts and suggest related improvements in speaking. In this way, the analysis of social media activities can appropriately suggest related improvements. A part or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can generate a report using an AI model that analyzes the user's social media activities and suggests related improvements when generating the report.

[0049] The voice generating unit can suggest more specific pronunciation improvements by referring to the user's past pronunciation data when generating voice. The voice generating unit, for example, suggests more specific pronunciation improvements by referring to the user's past pronunciation data when generating voice. The past pronunciation data includes recording data and text logs. For example, the voice generating unit generates a model voice including specific pronunciation improvements based on the user's past pronunciation data. The voice generating unit can also generate a model voice that emphasizes weak points in pronunciation by referring to the user's past pronunciation data. Furthermore, the voice generating unit can also generate a model voice for improving the pronunciation of a specific phrase or expression from the user's past pronunciation data. As a result, more specific pronunciation improvements can be suggested by referring to the past pronunciation data. A part or all of the above-mentioned processing in the voice generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice generating unit can generate a voice using an AI model that suggests more specific pronunciation improvements by referring to the user's past pronunciation data when generating voice.

[0050] The voice generation unit can apply different voice generation algorithms according to the level of the language spoken by the user when generating voice. For example, the voice generation unit applies different voice generation algorithms according to the level of the language spoken by the user when generating voice. The language level includes CEFR (Common European Framework of Reference for Languages) level and TOEFL score. For example, the voice generation unit generates model voice that emphasizes basic pronunciation and grammar for beginners. The voice generation unit can also generate model voice including more advanced pronunciation and grammar for intermediate learners. Furthermore, the voice generation unit can also generate model voice including native-level pronunciation and grammar for advanced learners. Thereby, by applying a voice generation algorithm according to the language level of the user, a more appropriate model voice can be provided. Part or all of the above-mentioned processing in the voice generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice generation unit can generate voice using an AI model that applies different voice generation algorithms according to the level of the language spoken by the user when generating voice.

[0051] The voice generating unit can generate model voice including region-specific pronunciation and expressions by considering the geographical location information of the user at the time of voice generation. For example, the voice generating unit generates model voice including region-specific pronunciation and expressions by considering the geographical location information of the user at the time of voice generation. The geographical location information includes location estimation from GPS data and IP address. The region-specific pronunciation and expressions include Kansai dialect, Tohoku dialect, and the like. For example, when the user is in the Kansai region, the voice generating unit generates model voice including Kansai dialect pronunciation and expressions. Furthermore, when the user is in New York, the voice generating unit can also generate model voice including New York-specific pronunciation and expressions. Furthermore, when the user is in London, the voice generating unit can also generate model voice including London-specific pronunciation and expressions. In this way, by considering the geographical location information, it is possible to provide model voice including region-specific pronunciation and expressions. Part or all of the above-mentioned processing in the voice generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice generating unit can generate voice using an AI model that generates model voice including region-specific pronunciation and expressions by considering the geographical location information of the user at the time of voice generation.

[0052] The voice generation unit can analyze the social media activity of the user at the time of voice generation and generate a related model voice. For example, the voice generation unit can analyze the social media activity of the user at the time of voice generation and generate a related model voice. The social media activity includes the content of posts, comments, the number of likes, and the like. For example, the voice generation unit generates a model voice based on social media terms frequently used by the user. The voice generation unit can also generate a model voice related to a specific topic from the social media activity of the user. Furthermore, the voice generation unit can analyze the content of posts on social media of the user and generate a related model voice. In this way, it is possible to provide a related model voice by analyzing social media activities. A part or all of the above-mentioned processing in the voice generation unit may be performed, for example, using AI or may be performed without using AI. For example, the voice generation unit can generate a voice using an AI model that analyzes the social media activity of the user and generates a related model voice at the time of voice generation.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analyzer can also understand the context of the user's conversation and provide feedback according to the context. For example, if the user records a business meeting, the analyzer can understand the context and provide feedback on business terms and formal expressions. If the user records a casual conversation, the analyzer can understand the context and provide feedback on the use of expressions and slang appropriate for daily conversation. Furthermore, if the user is talking about a specific topic, the analyzer can provide feedback on technical terms and expressions related to that topic. This allows the user to learn appropriate expressions according to the context.

[0055] The generator can also track the user's learning progress and provide feedback according to the progress. For example, if the user shows a certain improvement in a particular pronunciation or expression, the generator can evaluate the progress and provide more advanced feedback as a next step. Also, if the user is struggling with a particular task, the generator can provide additional practice or advice on that task. Furthermore, the generator can suggest an individualized learning plan based on the user's learning history. This allows the user to learn effectively at their own pace.

[0056] The voice generating unit can also analyze the user's pronunciation characteristics and generate an individually customized model voice. For example, if the user has problems pronouncing a particular sound, the voice generating unit can generate a model voice that focuses on that sound. Also, if the user has difficulty with a particular accent or intonation, the voice generating unit can generate a model voice that emphasizes that part. Furthermore, the voice generating unit can generate an individually customized practice voice based on the user's pronunciation characteristics. This allows the user to effectively improve their weak points in their pronunciation.

[0057] The conversion unit can also convert the user's voice data into text in real time and provide on-the-spot feedback. For example, if the user uses a particular phrase during a conversation, the conversion unit can convert the phrase into text in real time and instantly suggest alternative expressions or improvements. Also, if the user is giving a presentation, the conversion unit can provide on-the-spot feedback on pronunciation and speaking style. Furthermore, the conversion unit allows the user to receive feedback in real time during practice, allowing them to make corrections and improvements immediately. This allows the user to receive effective feedback in real time.

[0058] The generator can also tailor the form of feedback to the user's learning style. For example, visual learners can be provided with feedback using diagrams and graphs. For auditory learners, audio feedback can be provided. Furthermore, practical learners can be provided with feedback that includes specific exercises or drills. In this way, appropriate feedback can be provided according to the user's learning style.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The conversion unit converts the voice recorded by the user into text. For example, the conversion unit converts the voice into text using a voice recognition technology. The voice recognition technology includes deep learning-based voice recognition and HMM (Hidden Markov Model)-based voice recognition. Step 2: The analysis unit uses the generative AI to analyze the text converted by the conversion unit and identify speaking habits and weak points in pronunciation. For example, the analysis unit uses natural language processing technology to analyze the text and identify speaking habits such as speech speed, intonation, and rhythm, as well as weak points in pronunciation such as the pronunciation of specific sounds and the placement of accents. Step 3: The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for each situation, such as business scenes and casual conversations. For example, the generation unit proposes an alternative expression such as "It's sunny today and it feels nice" for the phrase "The weather is nice today." Based on the analysis results, the generation unit generates a report that includes the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement. Step 4: The voice generation unit generates the model voice generated by the generation unit. For example, the voice generation unit cooperates with a voice output device such as a smartphone or a smart speaker to transmit the generated model voice to the user. This allows the user to obtain specific reference for improving their pronunciation and speaking style.

[0061] (Example 2) The English speaking improvement system according to the embodiment of the present invention is a system in which a user records a conversation or a single talk, analyzes the voice of the user using a generation AI, identifies the user's speaking habits and weak points in pronunciation, and proposes alternative phrases and improvements. This system converts the voice recorded by the user into text, and the generation AI analyzes it to identify the user's speaking habits and weak points in pronunciation and proposes alternative expressions. For example, if a user says, "The weather is nice today," the voice is converted into text, and the generation AI proposes an alternative expression such as "It's sunny today and it feels good." In addition, the generation AI generates a report based on the analysis result and provides it to the user. This report includes the user's speaking habits and weak points in pronunciation, alternative phrases, and improvements. For example, specific advice such as "You tend to use 'desu' a lot. If you use 'desu' instead, it will be a more natural expression" is described. In addition, the generation AI generates a model voice and transmits it to the user through a voice output device. This allows the user to obtain specific references for improving their pronunciation and speaking style. For example, by listening to a model voice saying "It's sunny today and it feels nice," the user can correct his / her own pronunciation. This system enables the user to understand the weaknesses of his / her own speaking style and pronunciation and learn specific ways to improve. This improves practical English speaking ability and enables more natural English conversation. In this way, the English speaking improvement system can identify the weaknesses of the user's speaking style and pronunciation and provide specific ways to improve them.

[0062] The English speaking improvement system according to the embodiment includes a conversion unit, an analysis unit, a generation unit, and a voice generation unit. The conversion unit converts a voice recorded by a user into text. For example, the conversion unit converts the voice into text using a voice recognition technology. The voice recognition technology includes deep learning-based voice recognition and HMM (Hidden Markov Model)-based voice recognition. The analysis unit uses a generation AI to analyze the text converted by the conversion unit and identify speaking habits and weak points in pronunciation. For example, the analysis unit uses a natural language processing technology to analyze the text and identify speaking habits such as speech speed, intonation, and rhythm, and weak points in pronunciation such as pronunciation of specific sounds and accent placement. The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for each situation, such as a business scene or a casual conversation. For example, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today." The generation unit generates a report including the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement based on the analysis results. The voice generation unit generates a model voice generated by the generation unit. For example, the voice generation unit cooperates with a voice output device such as a smartphone or a smart speaker, and transmits the generated model voice to the user. This allows the user to obtain specific references for improving their pronunciation and speaking style. As a result, the English speaking improvement system according to the embodiment can identify the weak points of the user's speaking style and pronunciation, and provide a specific method for improvement.

[0063] The conversion unit converts the voice recorded by the user into text. For example, the conversion unit converts the voice into text using a voice recognition technology. Voice recognition technologies include deep learning-based voice recognition and HMM (Hidden Markov Model)-based voice recognition. Specifically, deep learning-based voice recognition uses a neural network to analyze voice data and recognize phonemes and words with high accuracy. This allows the content spoken by the user to be accurately converted into text. In addition, HMM-based voice recognition converts voice into text by modeling the temporal fluctuation of the voice and probabilistically analyzing the voice signal. By combining these technologies, the conversion unit can convert voices of various environments and speakers into text with high accuracy. Furthermore, the conversion unit can use noise reduction technology to remove background noise and improve the accuracy of voice recognition. For example, even if the user records a voice in a noisy environment, clear voice data can be obtained by using noise reduction technology, and accurate text conversion can be achieved. This allows the conversion unit to accurately and quickly convert the user's voice into text and provide the data required for subsequent analysis and generation processes.

[0064] The analysis unit uses the generation AI to analyze the text converted by the conversion unit and identify speaking habits and weak points in pronunciation. For example, the analysis unit uses natural language processing technology to analyze the text and identify speaking habits such as speaking speed, intonation, and rhythm, and pronunciation weak points such as the pronunciation of specific sounds and the location of the accent. Specifically, the generation AI receives the user's text data as input and analyzes the speaking speed and intonation patterns. For example, it detects cases where the user tends to speak specific words quickly or when the intonation is flat. In addition, it identifies cases where specific phonemes or accent locations are not accurate for pronunciation weak points. For example, it analyzes cases where the pronunciation of "r" or "l" is unclear or where the accent is not placed in the correct position. Based on this information, the analysis unit can grasp the user's speaking style and pronunciation patterns in detail and identify areas for improvement. Furthermore, the analysis unit can relatively evaluate the user's speaking style and pronunciation characteristics by comparing them with past data and data of other users. This enables the analysis unit to accurately identify weak points in the user's speaking style and pronunciation and provide basic data for proposing specific methods for improvement.

[0065] The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for each situation, such as business scenes and casual conversations. For example, the generation unit proposes an alternative expression such as "It's sunny today and it feels good" for the phrase "The weather is nice today." The generation unit generates a report based on the analysis results, including the user's speaking habits and pronunciation weaknesses, alternative phrases, and improvements. Specifically, the generation AI considers the characteristics of the user's speaking style and pronunciation and proposes appropriate alternative expressions and improvement methods. For example, in business scenes, it proposes more formal expressions, and in casual conversations, it proposes more natural expressions. In addition, the generation unit specifically indicates the improvements in the user's speaking style and pronunciation and suggests how to practice. For example, it specifically indicates how to practice the pronunciation of specific phonemes and how to improve intonation. In addition, the generation unit tracks the user's progress and regularly updates the report to support continuous improvement. In this way, the generation unit can effectively support the improvement of the user's speaking style and pronunciation, and improve English speaking ability.

[0066] The voice generation unit generates the model voice generated by the generation unit. For example, the voice generation unit cooperates with a voice output device such as a smartphone or a smart speaker, and conveys the generated model voice to the user. Specifically, the voice generation unit generates the model voice based on the alternative expressions and improvements proposed by the generation unit. For example, for the phrase "The weather is nice today," the voice generation unit generates a model voice of "It's sunny today and it feels good" with correct intonation and pronunciation. The voice generation unit uses synthetic voice technology to generate natural and easy-to-listen voice. This allows the user to obtain specific references for improving their pronunciation and speaking style. Furthermore, the voice generation unit can receive feedback from the user and continuously improve the quality of the generated voice. For example, if the user feels that a particular voice is difficult to hear, the voice generation algorithm is adjusted based on the feedback to generate a voice that is easier to hear. In addition, the voice generation unit can provide a model voice with adjusted difficulty according to the user's progress. This allows the voice generation unit to effectively support the improvement of the user's English speaking ability and promote continuous learning.

[0067] The analysis unit can perform analysis by combining speech recognition technology and natural language processing technology. The analysis unit performs analysis by combining, for example, speech recognition technology and natural language processing technology. The speech recognition technology includes deep learning-based speech recognition and HMM (Hidden Markov Model)-based speech recognition. The natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, and the like. For example, the analysis unit converts speech into text using speech recognition technology and analyzes the text using natural language processing technology. This improves the accuracy of analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can convert speech into text and analyze the text using an AI model that combines speech recognition technology and natural language processing technology.

[0068] The generation unit can propose different alternative expressions for each situation, such as a business scene or a casual conversation. The generation unit proposes different alternative expressions for each situation, such as a business scene or a casual conversation. Business scenes include meetings, presentations, negotiations, etc. Casual conversations include daily conversations, conversations with friends, and chats. For example, in a business scene, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today." Also, in a casual conversation, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today." This improves the user's ability to express themselves. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can propose alternative expressions using an AI model that proposes different alternative expressions for each situation, such as a business scene or a casual conversation.

[0069] The voice generation unit can cooperate with a voice output device such as a smartphone or a smart speaker. The voice generation unit cooperates with a voice output device such as a smartphone or a smart speaker. The smartphone includes an iOS device or an Android device. The smart speaker includes an Amazon Echo or a Google Home. For example, the voice generation unit cooperates with a smartphone and transmits the generated voice of the model to the user through the smartphone's speaker. Also, the voice generation unit cooperates with a smart speaker and transmits the generated voice of the model to the user through the smart speaker's speaker. This allows the user to easily hear the generated voice of the model. A part or all of the above-mentioned processing in the voice generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice generation unit can transmit the generated voice of the model to the user using an AI model that cooperates with a voice output device such as a smartphone or a smart speaker.

[0070] The conversion unit can estimate the user's emotion and adjust the accuracy of the voice-to-text conversion based on the estimated user's emotion. The conversion unit, for example, estimates the user's emotion and adjusts the accuracy of the voice-to-text conversion based on the estimated user's emotion. The user's emotion includes joy, sadness, anger, and the like. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is nervous, the conversion unit increases the sensitivity of the voice recognition to perform more accurate text conversion. In addition, when the user is relaxed, the conversion unit can also perform text conversion with an emphasis on natural speaking. Furthermore, when the user is in a hurry, the conversion unit can use a simplified conversion algorithm to perform text conversion quickly. This allows more accurate text conversion by adjusting the accuracy of the text conversion according to the user's emotion. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversion unit can perform text conversion using an AI model that estimates a user's emotions and adjusts the accuracy of the speech-to-text conversion based on the estimated user's emotions.

[0071] The conversion unit can automatically remove background noise from the voice to perform more accurate text conversion. The conversion unit can, for example, automatically remove background noise from the voice to perform more accurate text conversion. Background noise includes environmental sounds, noise, echoes, and the like. For example, the conversion unit filters ambient noise during recording to obtain clear voice data. The conversion unit can also remove noise in a specific frequency band during voice recognition to improve the accuracy of text conversion. Furthermore, the conversion unit can also apply noise reduction technology after recording to convert voice data with minimal noise into text. This improves the accuracy of text conversion by removing background noise. Some or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can perform text conversion using an AI model that automatically removes background noise from the voice to perform more accurate text conversion.

[0072] The conversion unit can dynamically adjust the conversion algorithm according to the user's speaking speed and volume when recording the voice. For example, the conversion unit dynamically adjusts the conversion algorithm according to the user's speaking speed and volume when recording the voice. The speaking speed includes the number of words and the number of syllables per unit time. The volume includes measurement in decibels (dB) and adjustment of the volume level. For example, when the user speaks quickly, the conversion unit increases the sampling rate of the voice recognition to improve the accuracy of the text conversion. In addition, when the user speaks softly, the conversion unit can also increase the sensitivity of the voice recognition to perform accurate text conversion. Furthermore, the conversion unit can dynamically adjust the parameters of the conversion algorithm according to the user's speaking speed to achieve optimal text conversion. As a result, optimal text conversion is possible by adjusting the conversion algorithm according to the user's speaking speed and volume. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can perform text conversion using an AI model that dynamically adjusts the conversion algorithm according to the user's speaking speed and volume.

[0073] The conversion unit can estimate the user's emotion and adjust the display method of the converted text based on the estimated user's emotion. The conversion unit, for example, estimates the user's emotion and adjusts the display method of the converted text based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the conversion unit displays text using a simple, highly visible font when the user is nervous. In addition, the conversion unit can also display text using a colorful and fun font when the user is relaxed. Furthermore, the conversion unit can also display text with important parts highlighted when the user is in a hurry. This improves visibility by adjusting the display method of the text according to the user's emotion. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can display text using an AI model that estimates the user's emotion and adjusts the display method of the converted text based on the estimated user's emotion.

[0074] The conversion unit can appropriately convert dialects and expressions specific to a region based on the geographical location information of the user when recording the voice. For example, the conversion unit appropriately converts dialects and expressions specific to a region in consideration of the geographical location information of the user when recording the voice. The geographical location information includes location estimation from GPS data and IP addresses. The dialects and expressions specific to a region include Kansai dialect and Tohoku dialect. For example, when the user is in the Kansai region, the conversion unit performs text conversion taking into account the dialect of the Kansai dialect. Furthermore, when the user is in New York, the conversion unit can also appropriately convert expressions specific to New York. Furthermore, when the user is in London, the conversion unit can also perform text conversion taking into account pronunciations and expressions specific to London. In this way, dialects and expressions specific to a region can be appropriately converted by taking into account the geographical location information. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can perform text conversion using an AI model that appropriately converts dialects and expressions specific to a region in consideration of the geographical location information of the user when recording the voice.

[0075] The conversion unit can analyze the social media activity of the user when recording the voice, and convert related terms and phrases preferentially. The conversion unit can, for example, analyze the social media activity of the user when recording the voice, and convert related terms and phrases preferentially. Social media activity includes the content of posts, comments, the number of likes, and the like. For example, the conversion unit preferentially converts social media terms frequently used by the user into text. The conversion unit can also preferentially convert phrases related to a specific topic from the social media activity of the user. Furthermore, the conversion unit can also analyze the content of posts on the social media of the user and appropriately convert related terms into text. In this way, by analyzing the social media activity, related terms and phrases can be preferentially converted. A part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can perform text conversion using an AI model that analyzes the social media activity of the user when recording the voice and preferentially converts related terms and phrases.

[0076] The analysis unit can estimate the user's emotion and identify the speaking habit and the weak point of the pronunciation based on the estimated emotion of the user. The analysis unit, for example, estimates the user's emotion and identifies the speaking habit and the weak point of the pronunciation based on the estimated emotion of the user. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is nervous, the analysis unit identifies the speaking habit specific to the nervous state. In addition, when the user is relaxed, the analysis unit can also identify the weak point of the pronunciation when the user is relaxed. Furthermore, when the user is in a hurry, the analysis unit can also identify the weak point of the pronunciation when speaking in a hurry. This enables more appropriate analysis by identifying the speaking habit and the weak point of the pronunciation according to the emotion of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can perform analysis using an AI model that estimates the user's emotions and identifies speaking habits and pronunciation weaknesses based on the estimated user's emotions.

[0077] The analysis unit can improve the analysis accuracy by referring to the user's past conversation data during the analysis. The analysis unit can improve the analysis accuracy by referring to the user's past conversation data, for example, during the analysis. The past conversation data includes recording data and text logs. For example, the analysis unit can identify a speaking pattern based on the user's past conversation data and improve the analysis accuracy. The analysis unit can also more accurately identify weak points in pronunciation by referring to the user's past pronunciation data. Furthermore, the analysis unit can analyze the frequency of use of specific phrases and expressions from the user's past conversation data and reflect it in the analysis. As a result, the analysis accuracy is improved by referring to the past conversation data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that improves the analysis accuracy by referring to the user's past conversation data during the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the level of the language spoken by the user during analysis. For example, the analysis unit applies different analysis algorithms depending on the level of the language spoken by the user during analysis. The language level includes CEFR (Common European Framework of Reference for Languages) level and TOEFL score. For example, the analysis unit applies an algorithm that analyzes basic pronunciation and grammar for beginners. The analysis unit can also apply an algorithm that analyzes more advanced pronunciation and grammar for intermediate learners. Furthermore, the analysis unit can apply an algorithm that analyzes native-level pronunciation and grammar for advanced learners. This enables more appropriate analysis by applying an analysis algorithm according to the language level of the user. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that applies different analysis algorithms depending on the level of the language spoken by the user during analysis.

[0079] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the analysis unit provides a simple and highly visible display method when the user is nervous. In addition, the analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This improves visibility by adjusting the display method of the analysis result according to the user's emotion. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform display using an AI model that estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion.

[0080] The analysis unit can analyze the region-specific pronunciation and expressions based on the geographical location information of the user during the analysis. For example, the analysis unit analyzes the region-specific pronunciation and expressions taking into account the geographical location information of the user during the analysis. The geographical location information includes GPS data and location estimation from an IP address. The region-specific pronunciation and expressions include Kansai dialect, Tohoku dialect, and the like. For example, when the user is in the Kansai region, the analysis unit analyzes the Kansai dialect pronunciation and expressions. Furthermore, when the user is in New York, the analysis unit can also analyze New York-specific pronunciation and expressions. Furthermore, when the user is in London, the analysis unit can also analyze London-specific pronunciation and expressions. This allows the region-specific pronunciation and expressions to be appropriately analyzed by taking into account the geographical location information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that analyzes the region-specific pronunciation and expressions taking into account the geographical location information of the user during the analysis.

[0081] The analysis unit can analyze the user's social media activity during the analysis and identify related speaking habits and pronunciation weaknesses. The analysis unit can, for example, analyze the user's social media activity during the analysis and identify related speaking habits and pronunciation weaknesses. The social media activity includes the content of posts, comments, the number of likes, and the like. For example, the analysis unit can analyze social media terms frequently used by the user and identify speaking habits. The analysis unit can also identify pronunciation weaknesses related to a specific topic from the user's social media activity. Furthermore, the analysis unit can analyze the content of the user's social media posts and identify related speaking habits and pronunciation weaknesses. In this way, the analysis of the social media activity can identify related speaking habits and pronunciation weaknesses. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that analyzes the user's social media activity during the analysis and identifies related speaking habits and pronunciation weaknesses.

[0082] The generation unit can estimate the user's emotion and adjust the report expression method based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the report expression method based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is nervous, the generation AI generates a simple and highly visible report. In addition, when the user is relaxed, the generation AI can also generate a report including detailed information. Furthermore, when the user is in a hurry, the generation AI can also generate a report that covers the main points. Thereby, the visibility is improved by adjusting the report expression method according to the user's emotion. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that estimates the user's emotion and adjusts the report expression method based on the estimated user's emotion.

[0083] The generation unit can suggest more specific improvements by referring to the user's past conversation data when generating a report. The generation unit, for example, suggests more specific improvements by referring to the user's past conversation data when generating a report. The past conversation data includes recording data and text logs. For example, the generation unit suggests specific improvements to pronunciation based on the user's past conversation data. The generation unit can also refer to the user's past pronunciation data to more accurately identify weak points in pronunciation. Furthermore, the generation unit can analyze the frequency of use of specific phrases and expressions from the user's past conversation data and suggest improvements. This makes it possible to suggest more specific improvements by referring to the past conversation data. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that suggests more specific improvements by referring to the user's past conversation data when generating a report.

[0084] The generation unit can apply different report formats according to the level of the language spoken by the user when generating a report. For example, the generation unit applies different report formats according to the level of the language spoken by the user when generating a report. The language level includes CEFR (Common European Framework of Reference for Languages) level and TOEFL score. For example, the generation unit generates a report including basic pronunciation and grammar improvement points for beginners by using the generation AI. The generation unit can also generate a report including more advanced pronunciation and grammar improvement points for intermediate learners by using the generation AI. Furthermore, the generation unit can also generate a report including native-level pronunciation and grammar improvement points for advanced learners by using the generation AI. As a result, a more appropriate report is generated by applying a report format according to the language level of the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that applies different report formats according to the level of the language spoken by the user when generating a report.

[0085] The generation unit can estimate the user's emotion and adjust the length of the report based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the report based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is nervous, the generation AI generates a short report that is to the point. Also, when the user is relaxed, the generation AI can generate a longer report that includes detailed explanations. Furthermore, when the user is in a hurry, the generation AI can generate a short report that can be read quickly. This improves visibility by adjusting the length of the report according to the user's emotion. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that estimates the user's emotion and adjusts the length of the report based on the estimated user's emotion.

[0086] The generation unit can suggest region-specific expressions and improvements based on the geographical location information of the user when generating the report. For example, the generation unit suggests region-specific expressions and improvements in consideration of the geographical location information of the user when generating the report. The geographical location information includes GPS data and location estimation from an IP address. The region-specific expressions and improvements include Kansai dialect and Tohoku dialect. For example, when the user is in the Kansai region, the generation unit suggests improvements to the pronunciation and expressions of the Kansai dialect. Furthermore, when the user is in New York, the generation unit can also suggest improvements to the pronunciation and expressions specific to New York. Furthermore, when the user is in London, the generation unit can also suggest improvements to the pronunciation and expressions specific to London. In this way, region-specific expressions and improvements can be appropriately suggested by taking into account the geographical location information. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that suggests region-specific expressions and improvements in consideration of the geographical location information of the user when generating the report.

[0087] The generation unit can analyze the user's social media activities and suggest related improvements when generating the report. The generation unit can analyze the user's social media activities and suggest related improvements when generating the report, for example. The social media activities include the content of posts, comments, the number of likes, and the like. For example, the generation unit can suggest specific improvements based on social media terms frequently used by the user. The generation unit can also suggest improvements in pronunciation related to a specific topic from the user's social media activities. Furthermore, the generation unit can analyze the content of the user's social media posts and suggest related improvements in speaking. In this way, the analysis of social media activities can appropriately suggest related improvements. A part or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can generate a report using an AI model that analyzes the user's social media activities and suggests related improvements when generating the report.

[0088] The voice generation unit can estimate the user's emotion and adjust the tone and speed of the model voice based on the estimated user's emotion. The voice generation unit, for example, estimates the user's emotion and adjusts the tone and speed of the model voice based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the voice generation unit generates a model voice with a calm tone and slow voice when the user is nervous. In addition, the voice generation unit can also generate a model voice with a bright tone and natural speed when the user is relaxed. Furthermore, the voice generation unit can also generate a quick and concise model voice when the user is in a hurry. As a result, a more appropriate model voice can be provided by adjusting the tone and speed of the model voice according to the user's emotion. A part or all of the above-mentioned processing in the voice generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the speech generator can generate speech using an AI model that estimates a user's emotions and adjusts the tone and speed of the model speech based on the estimated user's emotions.

[0089] The voice generating unit can suggest more specific pronunciation improvements by referring to the user's past pronunciation data when generating voice. The voice generating unit, for example, suggests more specific pronunciation improvements by referring to the user's past pronunciation data when generating voice. The past pronunciation data includes recording data and text logs. For example, the voice generating unit generates a model voice including specific pronunciation improvements based on the user's past pronunciation data. The voice generating unit can also generate a model voice that emphasizes weak points in pronunciation by referring to the user's past pronunciation data. Furthermore, the voice generating unit can also generate a model voice for improving the pronunciation of a specific phrase or expression from the user's past pronunciation data. As a result, more specific pronunciation improvements can be suggested by referring to the past pronunciation data. A part or all of the above-mentioned processing in the voice generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice generating unit can generate a voice using an AI model that suggests more specific pronunciation improvements by referring to the user's past pronunciation data when generating voice.

[0090] The voice generation unit can apply different voice generation algorithms according to the level of the language spoken by the user when generating voice. For example, the voice generation unit applies different voice generation algorithms according to the level of the language spoken by the user when generating voice. The language level includes CEFR (Common European Framework of Reference for Languages) level and TOEFL score. For example, the voice generation unit generates model voice that emphasizes basic pronunciation and grammar for beginners. The voice generation unit can also generate model voice including more advanced pronunciation and grammar for intermediate learners. Furthermore, the voice generation unit can also generate model voice including native-level pronunciation and grammar for advanced learners. Thereby, by applying a voice generation algorithm according to the language level of the user, a more appropriate model voice can be provided. Part or all of the above-mentioned processing in the voice generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice generation unit can generate voice using an AI model that applies different voice generation algorithms according to the level of the language spoken by the user when generating voice.

[0091] The voice generating unit can estimate the user's emotion and adjust the length of the model voice based on the estimated user's emotion. The voice generating unit, for example, estimates the user's emotion and adjusts the length of the model voice based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the voice generating unit generates a model voice that is short and to the point when the user is nervous. In addition, the voice generating unit can also generate a longer model voice that includes detailed explanations when the user is relaxed. Furthermore, the voice generating unit can also generate a short model voice that can be listened to quickly when the user is in a hurry. This allows the length of the model voice to be adjusted according to the user's emotion, thereby providing a more appropriate model voice. A part or all of the above-mentioned processing in the voice generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice generation unit can generate voice using an AI model that estimates a user's emotions and adjusts the length of the sample voice based on the estimated user's emotions.

[0092] The voice generating unit can generate model voice including region-specific pronunciation and expressions by considering the geographical location information of the user at the time of voice generation. For example, the voice generating unit generates model voice including region-specific pronunciation and expressions by considering the geographical location information of the user at the time of voice generation. The geographical location information includes location estimation from GPS data and IP address. The region-specific pronunciation and expressions include Kansai dialect, Tohoku dialect, and the like. For example, when the user is in the Kansai region, the voice generating unit generates model voice including Kansai dialect pronunciation and expressions. Furthermore, when the user is in New York, the voice generating unit can also generate model voice including New York-specific pronunciation and expressions. Furthermore, when the user is in London, the voice generating unit can also generate model voice including London-specific pronunciation and expressions. In this way, by considering the geographical location information, it is possible to provide model voice including region-specific pronunciation and expressions. Part or all of the above-mentioned processing in the voice generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice generating unit can generate voice using an AI model that generates model voice including region-specific pronunciation and expressions by considering the geographical location information of the user at the time of voice generation.

[0093] The voice generation unit can analyze the social media activity of the user at the time of voice generation and generate a related model voice. For example, the voice generation unit can analyze the social media activity of the user at the time of voice generation and generate a related model voice. The social media activity includes the content of posts, comments, the number of likes, and the like. For example, the voice generation unit generates a model voice based on social media terms frequently used by the user. The voice generation unit can also generate a model voice related to a specific topic from the social media activity of the user. Furthermore, the voice generation unit can analyze the content of posts on social media of the user and generate a related model voice. In this way, it is possible to provide a related model voice by analyzing social media activities. A part or all of the above-mentioned processing in the voice generation unit may be performed, for example, using AI or may be performed without using AI. For example, the voice generation unit can generate a voice using an AI model that analyzes the social media activity of the user and generates a related model voice at the time of voice generation.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The analyzer can also understand the context of the user's conversation and provide feedback according to the context. For example, if the user records a business meeting, the analyzer can understand the context and provide feedback on business terms and formal expressions. If the user records a casual conversation, the analyzer can understand the context and provide feedback on the use of expressions and slang appropriate for daily conversation. Furthermore, if the user is talking about a specific topic, the analyzer can provide feedback on technical terms and expressions related to that topic. This allows the user to learn appropriate expressions according to the context.

[0096] The generator can also track the user's learning progress and provide feedback according to the progress. For example, if the user shows a certain improvement in a particular pronunciation or expression, the generator can evaluate the progress and provide more advanced feedback as a next step. Also, if the user is struggling with a particular task, the generator can provide additional practice or advice on that task. Furthermore, the generator can suggest an individualized learning plan based on the user's learning history. This allows the user to learn effectively at their own pace.

[0097] The voice generating unit can also analyze the user's pronunciation characteristics and generate an individually customized model voice. For example, if the user has problems pronouncing a particular sound, the voice generating unit can generate a model voice that focuses on that sound. Also, if the user has difficulty with a particular accent or intonation, the voice generating unit can generate a model voice that emphasizes that part. Furthermore, the voice generating unit can generate an individually customized practice voice based on the user's pronunciation characteristics. This allows the user to effectively improve their weak points in their pronunciation.

[0098] The conversion unit can also convert the user's voice data into text in real time and provide on-the-spot feedback. For example, if the user uses a particular phrase during a conversation, the conversion unit can convert the phrase into text in real time and instantly suggest alternative expressions or improvements. Also, if the user is giving a presentation, the conversion unit can provide on-the-spot feedback on pronunciation and speaking style. Furthermore, the conversion unit allows the user to receive feedback in real time during practice, allowing them to make corrections and improvements immediately. This allows the user to receive effective feedback in real time.

[0099] The analysis unit can also estimate the user's emotions and adjust the tone and content of the feedback based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide encouraging feedback in a gentle tone. If the user is relaxed, the analysis unit can provide detailed feedback. Furthermore, if the user is in a hurry, the analysis unit can provide brief, to-the-point feedback. This makes it possible to provide appropriate feedback according to the user's emotions.

[0100] The generation unit can also estimate the user's emotions and adjust the content and format of the report based on the estimated emotions. For example, if the user is nervous, the generation unit can provide a simple, highly visible report. If the user is relaxed, the generation unit can also provide a report including detailed information. Furthermore, if the user is in a hurry, the generation unit can also provide a concise report that focuses on the main points. In this way, it is possible to provide an appropriate report according to the user's emotions.

[0101] The voice generation unit can also estimate the user's emotions and adjust the tone and speed of the model voice based on the estimated emotions. For example, if the user is nervous, the voice generation unit can generate a model voice with a calm tone and slow speed. If the user is relaxed, the voice generation unit can also generate a model voice with a bright tone and natural speed. Furthermore, if the user is in a hurry, the voice generation unit can generate a quick and concise model voice. This makes it possible to provide an appropriate model voice according to the user's emotions.

[0102] The conversion unit can also estimate the user's emotions and adjust the accuracy of the speech-to-text conversion based on the estimated emotions. For example, if the user is nervous, the conversion unit can increase the sensitivity of the speech recognition to perform more accurate text conversion. Also, if the user is relaxed, the conversion unit can perform text conversion with an emphasis on natural speaking. Furthermore, if the user is in a hurry, a simplified conversion algorithm can be used to perform text conversion quickly. This allows for more accurate text conversion by adjusting the accuracy of the text conversion according to the user's emotions.

[0103] The analysis unit can also estimate the user's emotions and identify speaking habits and weak points in pronunciation based on the estimated emotions. For example, if the user is nervous, the analysis unit can identify speaking habits specific to nervousness. Also, if the user is relaxed, the analysis unit can identify weak points in pronunciation when relaxed. Furthermore, if the user is in a hurry, the analysis unit can identify weak points in pronunciation when speaking in a hurry. This allows for more appropriate analysis by identifying speaking habits and weak points in pronunciation according to the user's emotions.

[0104] The generator can also tailor the form of feedback to the user's learning style. For example, visual learners can be provided with feedback using diagrams and graphs. For auditory learners, audio feedback can be provided. Furthermore, practical learners can be provided with feedback that includes specific exercises or drills. In this way, appropriate feedback can be provided according to the user's learning style.

[0105] The process flow of the second embodiment will be briefly described below.

[0106] Step 1: The conversion unit converts the voice recorded by the user into text. For example, the conversion unit converts the voice into text using a voice recognition technology. The voice recognition technology includes deep learning-based voice recognition and HMM (Hidden Markov Model)-based voice recognition. Step 2: The analysis unit uses the generative AI to analyze the text converted by the conversion unit and identify speaking habits and weak points in pronunciation. For example, the analysis unit uses natural language processing technology to analyze the text and identify speaking habits such as speech speed, intonation, and rhythm, as well as weak points in pronunciation such as the pronunciation of specific sounds and the placement of accents. Step 3: The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for each situation, such as business scenes and casual conversations. For example, the generation unit proposes an alternative expression such as "It's sunny today and it feels nice" for the phrase "The weather is nice today." Based on the analysis results, the generation unit generates a report that includes the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement. Step 4: The voice generation unit generates the model voice generated by the generation unit. For example, the voice generation unit cooperates with a voice output device such as a smartphone or a smart speaker to transmit the generated model voice to the user. This allows the user to obtain specific reference for improving their pronunciation and speaking style.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0109] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements including the above-mentioned conversion unit, analysis unit, generation unit, and voice generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the conversion unit is realized by the control unit 46A of the smart device 14, and converts the voice recorded by the user into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the text using a generation AI to identify speaking habits and weak points in pronunciation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a report based on the analysis results. The voice generation unit is realized, for example, by the control unit 46A of the smart device 14, and conveys the generated model voice to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0118] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the above-mentioned conversion unit, analysis unit, generation unit, and voice generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the conversion unit is realized by the control unit 46A of the smart glasses 214, and converts the voice recorded by the user into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the text using a generation AI to identify speaking habits and weak points in pronunciation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a report based on the analysis result. The voice generation unit is realized, for example, by the control unit 46A of the smart glasses 214, and conveys the generated model voice to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0130] 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.

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

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

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

[0134] 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.

[0135] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the above-mentioned conversion unit, analysis unit, generation unit, and voice generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the conversion unit is realized by the control unit 46A of the headset type terminal 314, and converts the voice recorded by the user into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the text using a generation AI to identify speaking habits and weak points in pronunciation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a report based on the analysis results. The voice generation unit is realized, for example, by the control unit 46A of the headset type terminal 314, and conveys the generated model voice to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

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

[0146] 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.

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

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

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

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] 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.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] Each of the multiple elements including the above-mentioned conversion unit, analysis unit, generation unit, and voice generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the conversion unit is realized by the control unit 46A of the robot 414, and converts the voice recorded by the user into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the text using a generation AI to identify speaking habits and weak points in pronunciation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a report based on the analysis results. The voice generation unit is realized, for example, by the control unit 46A of the robot 414, and conveys the generated model voice to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0161] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0165] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0167] 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.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0170] 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.

[0171] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0172] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0176] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0178] (Appendix 1) A converter for converting speech into text; an analysis unit that analyzes the text converted by the conversion unit and identifies speaking habits and pronunciation weaknesses; a generation unit that generates a report based on the analysis result obtained by the analysis unit; A voice generating unit that generates a voice of the model generated by the generating unit. A system characterized by: (Appendix 2) The analysis unit is Combining voice recognition technology and natural language processing technology for analysis 2. The system of claim 1, (Appendix 3) The generation unit is Suggest different alternative expressions for different situations, such as business or casual conversation 2. The system of claim 1, (Appendix 4) The voice generating unit is Link with audio output devices such as smartphones or smart speakers 2. The system of claim 1, (Appendix 5) The conversion unit is Estimating a user's emotion and adjusting the accuracy of speech-to-text conversion based on the estimated user's emotion 2. The system of claim 1, (Appendix 6) The conversion unit is Automatically remove background noise from audio for more accurate text conversion 2. The system of claim 1, (Appendix 7) The conversion unit is When recording audio, the conversion algorithm dynamically adjusts according to the user's speaking rate and volume. 2. The system of claim 1, (Appendix 8) The conversion unit is Inferring a user's emotion and adjusting the presentation of the converted text based on the estimated user's emotion 2. The system of claim 1, (Appendix 9) The conversion unit is When recording audio, it will convert dialects and regional expressions appropriately based on the user's geographic location 2. The system of claim 1, (Appendix 10) The conversion unit is When recording audio, it analyzes the user's social media activity and prioritizes conversion of relevant terms and phrases 2. The system of claim 1, (Appendix 11) The analysis unit is Estimate the user's emotions and identify speaking habits and pronunciation weaknesses based on the estimated user emotions 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, the accuracy of the analysis is improved by referring to the user's past conversation data. 2. The system of claim 1, (Appendix 13) The analysis unit is During parsing, different parsing algorithms are applied depending on the language level of the user. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During parsing, the system analyzes regional pronunciations and expressions based on the user's geographic location. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, the user's social media activity is analyzed to identify relevant speaking habits and pronunciation weaknesses. 2. The system of claim 1, (Appendix 17) The generation unit is Infer user sentiment and adjust report presentation based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The generation unit is When generating reports, the system refers to the user's past conversation data to suggest more specific improvements. 2. The system of claim 1, (Appendix 19) The generation unit is When generating reports, apply different report formats depending on the language level of the user. 2. The system of claim 1, (Appendix 20) The generation unit is Infer user sentiment and adjust report length based on the inferred user sentiment 2. The system of claim 1, (Appendix 21) The generation unit is When generating reports, suggest localized expressions and improvements based on the user's geographic location 2. The system of claim 1, (Appendix 22) The generation unit is Analyze users' social media activity and suggest relevant improvements when generating reports 2. The system of claim 1, (Appendix 23) The voice generating unit is Infer the user's emotions and adjust the tone and speed of the model voice based on the estimated user emotions. 2. The system of claim 1, (Appendix 24) The voice generating unit is When generating speech, the system refers to the user's past pronunciation data to suggest more specific pronunciation improvements. 2. The system of claim 1, (Appendix 25) The voice generating unit is When generating speech, different speech generation algorithms are applied depending on the level of the user's spoken language. 2. The system of claim 1, (Appendix 26) The voice generating unit is Estimate the user's emotion and adjust the length of the sample audio based on the estimated user's emotion. 2. The system of claim 1, (Appendix 27) The voice generating unit is When generating speech, the system takes into account the user's geographic location information to generate model speech that includes regional pronunciation and expressions. 2. The system of claim 1, (Appendix 28) The voice generating unit is When generating speech, the system analyzes the user's social media activity and generates relevant model speech. 2. The system of claim 1, [Explanation of symbols]

[0179] 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 converter for converting speech into text; an analysis unit that analyzes the text converted by the conversion unit and identifies speaking habits and weak points in pronunciation; a generation unit that generates a report based on the analysis result obtained by the analysis unit; A voice generating unit that generates a voice of the model generated by the generating unit. A system characterized by:

2. The analysis unit is Combining voice recognition technology and natural language processing technology for analysis 2. The system of claim 1 .

3. The generation unit is Suggest different alternative expressions for different situations, such as business or casual conversation 2. The system of claim 1 .

4. The voice generating unit is Link with audio output devices such as smartphones or smart speakers 2. The system of claim 1 .

5. The conversion unit is Estimating a user's emotion and adjusting the accuracy of speech-to-text conversion based on the estimated user's emotion 2. The system of claim 1 .

6. The conversion unit is Automatically remove background noise from audio for more accurate text conversion 2. The system of claim 1 .

7. The conversion unit is When recording audio, the conversion algorithm dynamically adjusts according to the user's speaking rate and volume.

2. The system of claim 1 .

8. The conversion unit is Inferring a user's emotion and adjusting the presentation of the converted text based on the estimated user's emotion 2. The system of claim 1 .

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