System

Abstract

An object of a system according to an embodiment is to make it possible to visually understand the content of a call.SOLUTION: A system includes a voice analysis unit, a text conversion unit, a translation unit, a visual display unit, and a voice reproduction unit. The voice analysis unit analyzes a voice of a telephone. The text conversion unit converts the voice data analyzed by the voice analysis unit into text. The translation unit translates the text converted by the text conversion unit. The visual display unit visually displays the text translated by the translation unit. The voice reproduction unit reproduces the text converted by the text conversion unit as voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks a means to visually understand what is being said on the phone when the speaker cannot hear it, and there is room for improvement.

[0005] The system according to the embodiment aims to enable visual understanding of the contents of a telephone call. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech analysis unit, a text conversion unit, a translation unit, a visual display unit, and a speech playback unit. The speech analysis unit analyzes telephone speech. The text conversion unit converts speech data analyzed by the speech analysis unit into text. The translation unit translates the text converted by the text conversion unit. The visual display unit visually displays the text translated by the translation unit. The speech playback unit plays back the text converted by the text conversion unit as speech. [Effects of the Invention]

[0007] The system according to the embodiment can enable the contents of a call to be visually understood. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system for visually understanding telephone conversations. This system analyzes telephone speech, converts it into text, translates it, visually displays it, and plays it back as audio. For example, telephone speech is connected to a visual display device, and the speech is analyzed and converted into text. This text is played back as audio in the voice of the person making the call. Furthermore, even if the call is in a foreign language, dialect, or strong accent, it is translated in real time and visually displayed. This allows the system to visually understand telephone conversations, allowing even people who are not good at making phone calls to make calls with confidence. For example, even if the call is in a foreign language, dialect, or strong accent, real-time translation allows the call content to be accurately understood. Furthermore, because the speech is displayed visually, there is no need to listen repeatedly, which avoids being rude to the other party.

[0029] A telephone content visual understanding system according to an embodiment includes a voice analysis unit, a text conversion unit, a translation unit, a visual display unit, and a voice playback unit. The voice analysis unit analyzes voice over the telephone. For example, the voice analysis unit analyzes voice data using a voice recognition algorithm. The voice analysis unit can also analyze voice in real time. For example, the voice analysis unit processes voice data in real time and provides an analysis result immediately. The text conversion unit converts voice data analyzed by the voice analysis unit into text. For example, the text conversion unit converts voice data into text using a voice-to-text conversion algorithm. The text conversion unit can also convert into text in real time. For example, the text conversion unit converts voice data into text in real time and displays it immediately. The translation unit translates the text converted by the text conversion unit. For example, the translation unit translates the text using a translation engine used. The translation unit can also translate in real time. For example, the translation unit translates text in real time and displays it immediately. The visual display unit visually displays the text translated by the translation unit. For example, the visual display unit visually displays text using a display format or a display device. The visual display unit can also adjust the display content to suit the user's visual characteristics. For example, the visual display unit can adjust the font size and color to improve visibility. The audio playback unit plays back the text converted by the text conversion unit as audio. For example, the audio playback unit can convert text to audio using a speech synthesis algorithm and play back the audio in the voice of the person making the call. The audio playback unit can also adjust the tone and speed of the audio playback based on the user's emotions. For example, if the user is nervous, the audio playback unit can play back audio in a calm tone. As a result, the telephone content visual understanding system according to the embodiment can visually understand the telephone content, allowing even people who are not good at making phone calls to make calls with confidence.

[0030] The speech analysis unit can analyze telephone voice in real time. Real time means that the delay time is extremely short and processing is performed immediately. For example, the speech analysis unit processes voice data in real time and provides analysis results immediately. The speech analysis unit can also analyze voice data using natural language processing technology. For example, the speech analysis unit performs morphological analysis and grammatical analysis to analyze voice data. This allows for real-time voice analysis, enabling instant text conversion and translation.

[0031] The text conversion unit can convert the analyzed voice data into text in real time. Real-time means that the delay time is extremely short and the processing is immediate. For example, the text conversion unit can convert voice data into text in real time and display it immediately. The text conversion unit can also convert voice data into text using a machine learning algorithm. For example, the text conversion unit can analyze voice data and convert it into text using K-means or hierarchical clustering. This allows for real-time conversion into text, which can then be displayed visually immediately.

[0032] The translation unit can translate text in real time. Real time means that the delay time is extremely short and the processing is immediate. For example, the translation unit translates text in real time and displays it immediately. The translation unit can also extract frequently occurring keywords to improve translation accuracy. For example, the translation unit extracts frequently occurring keywords using a frequency threshold or co-occurrence network analysis and reflects them in the translation. This allows for real-time translation, making it possible to instantly understand foreign languages, dialects, and strong accents.

[0033] The visual display unit can visually display the translated text. Visual display refers to displaying text in a format that is easy to understand visually. For example, the visual display unit can visually display the text using a display format or a display device. The visual display unit can also adjust the displayed content to suit the user's visual characteristics. For example, the visual display unit can adjust the font size and color to improve visibility. In this way, by visually displaying the translated text, the content of the call can be visually understood.

[0034] The voice playback unit can play back the text as audio in the voice of the person on the other end of the call. The voice of the other person refers to audio generated based on a voice sample of the person on the other end of the call. For example, the voice playback unit converts the text into audio using a speech synthesis algorithm and plays back the audio in the voice of the person on the other end of the call. The voice playback unit can also play back the audio taking into account the method of acquiring the audio sample and the accuracy of the speech synthesis. For example, the voice playback unit acquires an audio sample of the other person's voice and generates audio based on the audio sample. This allows for more natural communication by playing back the text in the other person's voice.

[0035] The voice analysis unit can perform filtering to remove background noise during voice analysis. Background noise refers to unwanted environmental sounds contained in the voice data. For example, the voice analysis unit can detect surrounding environmental sounds and filter specific frequency bands to remove noise. The voice analysis unit can also separate the speaker's voice from background noise and analyze only the speaker's voice. Furthermore, the voice analysis unit can perform noise cancellation in real time to provide clear voice data. This enables clear voice analysis by removing background noise.

[0036] During voice analysis, the voice analysis unit can learn the characteristics of the speaker's voice and perform individually optimized analysis. Voice characteristics refer to the speaker's voice pitch, tone, pronunciation habits, etc. For example, the voice analysis unit learns the speaker's voice pitch and tone and performs individually optimized analysis. The voice analysis unit can also learn the speaker's pronunciation habits and improve analysis accuracy. Furthermore, the voice analysis unit can learn the strength and weakness of the speaker's voice and perform analysis at an appropriate volume. In this way, by learning the speaker's voice characteristics, analysis accuracy is improved.

[0037] During voice analysis, the voice analysis unit can adjust the analysis speed according to the speaker's speaking rate. Speaking rate refers to the speed at which the speaker speaks. For example, the voice analysis unit detects the speaker's speaking rate in real time and automatically adjusts the analysis speed. Furthermore, if the speaker's speaking rate is fast, the voice analysis unit can also speed up the analysis speed and perform analysis in real time. Furthermore, if the speaker's speaking rate is slow, the voice analysis unit can also perform detailed analysis by prioritizing analysis accuracy. This makes it possible to perform analysis in real time by adjusting the analysis speed according to the speaker's speaking rate.

[0038] During speech analysis, the speech analysis unit can improve analysis accuracy by taking into account the speaker's geographical background information. Geographical background information refers to the pronunciation and dialect specific to the speaker's region. For example, the speech analysis unit performs analysis taking into account specific dialects and accents based on the speaker's geographical background information. The speech analysis unit can also reflect specific phrases and expressions in the analysis based on the speaker's geographical background. Furthermore, the speech analysis unit can perform analysis by removing noise specific to the region. In this way, analysis accuracy is improved by taking into account the speaker's geographical background information.

[0039] During speech analysis, the speech analysis unit can improve analysis accuracy by referring to the speaker's past speech data. Past speech data refers to speech data previously spoken by the speaker. For example, the speech analysis unit improves analysis accuracy by learning specific pronunciations and phrases based on the speaker's past speech data. The speech analysis unit can also perform analysis by removing specific noise patterns by referring to the speaker's past speech data. Furthermore, the speech analysis unit can perform analysis corresponding to specific languages ​​or dialects based on the speaker's past speech data. In this way, analysis accuracy is improved by referring to the speaker's past speech data.

[0040] The speech analysis unit can incorporate a multilingual analysis function to support different languages ​​and dialects during speech analysis. A multilingual analysis function refers to an analysis function that supports multiple languages ​​and dialects. For example, the speech analysis unit incorporates a multilingual analysis function to support different languages ​​and dialects, thereby improving analysis accuracy. The speech analysis unit can also perform analysis using a dictionary to support a specific language or dialect. Furthermore, the speech analysis unit can also perform analysis using a speech model to support different languages ​​and dialects. This improves analysis accuracy by supporting different languages ​​and dialects.

[0041] The text conversion unit can use a dictionary for accurately converting technical terms and proper nouns during text conversion. Technical terms and proper nouns refer to specific terms and names associated with a particular field or individual. For example, the text conversion unit performs conversion using a dictionary for accurately converting technical terms and proper nouns. The text conversion unit can also improve conversion accuracy by using a dictionary specialized for a particular industry or field. Furthermore, the text conversion unit can learn technical terms and proper nouns frequently used by users and add them to the dictionary for conversion. This improves conversion accuracy by accurately converting technical terms and proper nouns.

[0042] The text conversion unit can insert appropriate punctuation marks by taking context into consideration when converting text. Taking context into consideration means processing by understanding the context and meaning of the sentence. For example, the text conversion unit can automatically insert appropriate punctuation marks by taking context into consideration. The text conversion unit can also analyze the rhythm and pauses of the speaker's speech to insert appropriate punctuation marks. Furthermore, the text conversion unit can also perform appropriate line breaks and paragraph divisions based on the context. In this way, natural-looking sentences can be generated by inserting punctuation marks by taking context into consideration.

[0043] During text conversion, the text conversion unit can generate natural-looking sentences that reflect the speaker's intention. Reflecting the speaker's intention means processing the sentences after understanding the speaker's intention and purpose. For example, the text conversion unit analyzes the speaker's intention and generates natural-looking sentences. The text conversion unit can also select appropriate expressions by taking into account the nuances and tone of the speaker's speech. Furthermore, the text conversion unit can generate sentences with a natural flow based on the context. This generates natural-looking sentences that reflect the speaker's intention, providing text that is easier to understand.

[0044] The text conversion unit can adjust the conversion speed according to the speaker's speaking rate during text conversion. Speaking rate refers to the speed at which the speaker speaks. For example, the text conversion unit detects the speaker's speaking rate in real time and automatically adjusts the conversion speed. Furthermore, if the speaker's speaking rate is fast, the text conversion unit can also speed up the conversion speed and perform conversion in real time. Furthermore, if the speaker's speaking rate is slow, the text conversion unit can also perform detailed conversion by prioritizing conversion accuracy. As a result, real-time conversion is possible by adjusting the conversion speed according to the speaker's speaking rate.

[0045] During text conversion, the text conversion unit can improve conversion accuracy by referring to the speaker's past speech data. Past speech data refers to audio data previously spoken by the speaker. For example, the text conversion unit improves conversion accuracy by learning specific pronunciations and phrases based on the speaker's past speech data. The text conversion unit can also perform conversion by removing specific noise patterns by referring to the speaker's past speech data. Furthermore, the text conversion unit can perform conversion that corresponds to a specific language or dialect based on the speaker's past speech data. In this way, by referring to the speaker's past speech data, conversion accuracy is improved.

[0046] During translation, the translation unit can use a dictionary to accurately translate technical terms and proper nouns. Technical terms and proper nouns refer to specific terms and names associated with a particular field or individual. For example, the translation unit performs translation using a dictionary to accurately translate technical terms and proper nouns. The translation unit can also improve translation accuracy by using a dictionary specialized for a particular industry or field. Furthermore, the translation unit can learn technical terms and proper nouns frequently used by users and add them to the dictionary when translating. This improves translation accuracy by accurately translating technical terms and proper nouns.

[0047] The translation unit can insert appropriate punctuation marks by taking context into consideration when translating. Taking context into consideration means processing by understanding the context and meaning of a sentence. For example, the translation unit can automatically insert appropriate punctuation marks by taking context into consideration. The translation unit can also analyze the rhythm and pauses of a speaker's speech and insert appropriate punctuation marks. Furthermore, the translation unit can also perform appropriate line breaks and paragraph divisions based on the context. In this way, natural-looking sentences can be generated by inserting punctuation marks by taking context into consideration.

[0048] During translation, the translation unit can generate natural-sounding sentences that reflect the speaker's intention. Reflecting the speaker's intention means processing the sentences after understanding the speaker's intention and purpose. For example, the translation unit analyzes the speaker's intention and generates natural-sounding sentences. The translation unit can also select appropriate expressions by taking into account the nuances and tone of the speaker's speech. Furthermore, the translation unit can generate sentences that flow naturally based on the context. This generates natural-sounding sentences that reflect the speaker's intention, providing translations that are easier to understand.

[0049] The translation unit can adjust the translation speed during translation according to the speaker's speaking speed. Speaking speed refers to the speed at which the speaker speaks. For example, the translation unit detects the speaker's speaking speed in real time and automatically adjusts the translation speed. Furthermore, if the speaker's speaking speed is fast, the translation unit can also speed up the translation speed and perform real-time translation. Furthermore, if the speaker's speaking speed is slow, the translation unit can also prioritize translation accuracy and perform detailed translation. This makes it possible to perform real-time translation by adjusting the translation speed according to the speaker's speaking speed.

[0050] The translation unit can incorporate a multilingual translation function to support different languages ​​and dialects during translation. A multilingual translation function refers to a translation function that supports multiple languages ​​and dialects. For example, the translation unit incorporates a multilingual translation function to support different languages ​​and dialects, thereby improving translation accuracy. The translation unit can also perform translation using a dictionary to support a specific language or dialect. Furthermore, the translation unit can also perform translation using a translation model to support different languages ​​and dialects. This improves translation accuracy by supporting different languages ​​and dialects.

[0051] During translation, the translation unit can improve translation accuracy by referring to the speaker's past speech data. Past speech data refers to audio data previously spoken by the speaker. For example, the translation unit improves translation accuracy by learning specific pronunciations and phrases based on the speaker's past speech data. The translation unit can also refer to the speaker's past speech data and remove specific noise patterns before performing translation. Furthermore, the translation unit can also perform translation that corresponds to a specific language or dialect based on the speaker's past speech data. In this way, by referring to the speaker's past speech data, translation accuracy is improved.

[0052] The visual display unit can adjust the font size and color of the text to be displayed to suit the user's visual characteristics during visual display. Visual characteristics refer to characteristics related to the user's vision. For example, the visual display unit automatically adjusts the font size taking into account the user's visual characteristics. The visual display unit can also adjust the text color based on the user's visual characteristics. Furthermore, the visual display unit can adjust the background color and contrast based on the user's visual characteristics. In this way, adjusting the font size and color to suit the user's visual characteristics improves visibility.

[0053] The visual display unit can customize the displayed content based on the user's interests when visually displaying. Interests refer to information or themes in which the user is particularly interested. For example, the visual display unit can preferentially display information of interest based on the user's past operation history. The visual display unit can also display related information based on the user's interests. Furthermore, the visual display unit can customize the displayed content according to the user's interests. This allows the display content to be customized based on the user's interests, thereby providing more appropriate information.

[0054] The visual display unit can optimize the display content based on the user's past operation history when visually displaying. The past operation history refers to a record of operations previously performed by the user. For example, the visual display unit provides optimal display content based on the user's past operation history. The visual display unit can also refer to the user's past operation history and display related information. Furthermore, the visual display unit can customize the display content based on the user's past operation history. As a result, more appropriate information is provided by optimizing the display content based on the user's past operation history.

[0055] The visual display unit can select the optimal display method by taking into consideration the user's device information when visually displaying. Device information refers to the type and characteristics of the device used by the user. For example, the visual display unit provides the optimal display method based on the user's device information. The visual display unit can also adjust the display content to match the screen size of the user's device. Furthermore, the visual display unit can optimize the display content based on the resolution of the user's device. This improves visibility by selecting the optimal display method by taking into consideration the user's device information.

[0056] The visual display unit can make the displayed content multilingual in accordance with the user's language setting when visually displaying. The language setting refers to the setting of the language used by the user. For example, the visual display unit automatically translates the displayed content based on the user's language setting. The visual display unit can also provide a language switching function when the user uses multiple languages. Furthermore, the visual display unit can also make the displayed content multilingual in accordance with the user's language setting. This allows more appropriate information to be provided by making the displayed content multilingual in accordance with the user's language setting.

[0057] The visual display unit can customize the display content by taking into account the user's geographical background information when visually displaying the information. Geographical background information refers to pronunciations and dialects specific to the user's region. For example, the visual display unit displays related information based on the user's geographical background information. The visual display unit can also provide information related to a specific region based on the user's geographical background. Furthermore, the visual display unit can customize the display content by taking into account the user's geographical background information. In this way, by customizing the display content by taking into account the user's geographical background information, more appropriate information can be provided.

[0058] When playing back audio, the audio playback unit can learn the characteristics of the speaker's voice and perform individually optimized audio playback. Voice characteristics refer to the speaker's voice pitch, tone, pronunciation habits, etc. For example, the audio playback unit can learn the speaker's voice pitch and tone and perform individually optimized audio playback. The audio playback unit can also learn the speaker's pronunciation habits and improve playback accuracy. Furthermore, the audio playback unit can learn the speaker's voice dynamics and play back at an appropriate volume. In this way, by learning the speaker's voice characteristics, the accuracy of audio playback is improved.

[0059] The audio playback unit can adjust the playback speed according to the speaker's speaking rate when playing back audio. Speaking rate refers to the speed at which the speaker speaks. For example, the audio playback unit detects the speaker's speaking rate in real time and automatically adjusts the playback speed. Furthermore, if the speaker's speaking rate is fast, the audio playback unit can also speed up the playback speed and play back in real time. Furthermore, if the speaker's speaking rate is slow, the audio playback unit can also prioritize playback accuracy and play back in detail. As a result, real-time playback is possible by adjusting the playback speed according to the speaker's speaking rate.

[0060] The audio playback unit can improve playback accuracy by taking into account the speaker's geographical background information when playing back audio. Geographical background information refers to the pronunciation and dialect specific to the speaker's region. For example, the audio playback unit performs playback taking into account a specific dialect or accent based on the speaker's geographical background information. The audio playback unit can also reflect specific phrases and expressions in the playback based on the speaker's geographical background. Furthermore, the audio playback unit can also remove noise specific to the region when playing back audio. In this way, playback accuracy is improved by taking into account the speaker's geographical background information.

[0061] The audio playback unit can improve playback accuracy by referring to the speaker's past speech data when playing back audio. Past speech data refers to audio data previously spoken by the speaker. For example, the audio playback unit can improve playback accuracy by learning specific pronunciations and phrases based on the speaker's past speech data. The audio playback unit can also refer to the speaker's past speech data and remove specific noise patterns before playing back audio. Furthermore, the audio playback unit can also play back audio that corresponds to a specific language or dialect based on the speaker's past speech data. In this way, playback accuracy is improved by referring to the speaker's past speech data.

[0062] The audio playback unit can incorporate a multilingual playback function to support different languages ​​and dialects during audio playback. The multilingual playback function refers to a playback function that supports multiple languages ​​and dialects. For example, the audio playback unit incorporates a multilingual playback function to support different languages ​​and dialects, thereby improving playback accuracy. The audio playback unit can also perform playback using a dictionary to support a specific language or dialect. Furthermore, the audio playback unit can perform playback using a speech model to support different languages ​​and dialects. This improves playback accuracy by supporting different languages ​​and dialects.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] During voice analysis, the voice analysis unit can learn the characteristics of the speaker's voice and perform individually optimized analysis. For example, the voice analysis unit can learn the pitch and tone of the speaker's voice and perform individually optimized analysis. The voice analysis unit can also learn the speaker's pronunciation habits and improve analysis accuracy. Furthermore, the voice analysis unit can learn the strength and weakness of the speaker's voice and perform analysis at an appropriate volume. In this way, by learning the characteristics of the speaker's voice, analysis accuracy is improved.

[0065] The text conversion unit can use a dictionary for accurately converting technical terms and proper nouns during text conversion. For example, the text conversion unit performs conversion using a dictionary for accurately converting technical terms and proper nouns. The text conversion unit can also improve conversion accuracy by using a dictionary specialized for a specific industry or field. Furthermore, the text conversion unit can learn technical terms and proper nouns frequently used by users and add them to the dictionary for conversion. This improves conversion accuracy by accurately converting technical terms and proper nouns.

[0066] The translation unit can insert appropriate punctuation marks while taking the context into consideration when translating. For example, the translation unit automatically inserts appropriate punctuation marks while taking the context into consideration. The translation unit can also analyze the rhythm and pauses of the speaker's speech and insert appropriate punctuation marks. Furthermore, the translation unit can also perform appropriate line breaks and paragraph divisions based on the context. As a result, natural-looking sentences are generated by inserting punctuation marks while taking the context into consideration.

[0067] The visual display unit can adjust the font size and color of the displayed text to suit the user's visual characteristics during visual display. For example, the visual display unit automatically adjusts the font size taking into account the user's visual characteristics. The visual display unit can also adjust the text color based on the user's visual characteristics. Furthermore, the visual display unit can adjust the background color and contrast based on the user's visual characteristics. In this way, adjusting the font size and color to suit the user's visual characteristics improves visibility.

[0068] During audio playback, the audio playback unit can learn the characteristics of the speaker's voice and perform individually optimized audio playback. For example, the audio playback unit can learn the pitch and tone of the speaker's voice and perform individually optimized audio playback. The audio playback unit can also learn the speaker's pronunciation habits and improve playback accuracy. Furthermore, the audio playback unit can learn the strength and weakness of the speaker's voice and play back at an appropriate volume. In this way, by learning the characteristics of the speaker's voice, the accuracy of audio playback is improved.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The voice analysis unit analyzes the voice of the call. For example, the voice analysis unit can analyze the voice data using a voice recognition algorithm and analyze the voice in real time. The voice data is processed in real time and the analysis result is provided immediately. Step 2: The text conversion unit converts the voice data analyzed by the voice analysis unit into text. For example, the voice data can be converted into text using a voice-to-text conversion algorithm, and the text can be converted in real time. The voice data can be converted into text in real time and displayed immediately. Step 3: The translation unit translates the text converted by the text conversion unit. For example, the translation unit may translate the text using a translation engine in real time. The text is translated in real time and displayed immediately. Step 4: The visual display unit visually displays the text translated by the translation unit. For example, the visual display unit may use a display format or device to visually display the text, and may adjust the displayed content to suit the user's visual characteristics. For example, the visual display unit may adjust the font size and color to improve visibility. Step 5: The voice playback unit plays back the text converted by the text conversion unit as voice. For example, it uses a speech synthesis algorithm to convert the text into voice and plays it back in the voice of the person on the phone. It can also adjust the tone and speed of the voice playback based on the user's emotions. For example, if the user is nervous, the voice will be played back in a calm tone.

[0071] (Example 2) A system according to an embodiment of the present invention is a system for visually understanding telephone conversations. This system analyzes telephone speech, converts it into text, translates it, visually displays it, and plays it back as audio. For example, telephone speech is connected to a visual display device, and the speech is analyzed and converted into text. This text is played back as audio in the voice of the person making the call. Furthermore, even if the call is in a foreign language, dialect, or strong accent, it is translated in real time and visually displayed. This allows the system to visually understand telephone conversations, allowing even people who are not good at making phone calls to make calls with confidence. For example, even if the call is in a foreign language, dialect, or strong accent, real-time translation allows the call content to be accurately understood. Furthermore, because the speech is displayed visually, there is no need to listen repeatedly, which avoids being rude to the other party.

[0072] A telephone content visual understanding system according to an embodiment includes a voice analysis unit, a text conversion unit, a translation unit, a visual display unit, and a voice playback unit. The voice analysis unit analyzes voice over the telephone. For example, the voice analysis unit analyzes voice data using a voice recognition algorithm. The voice analysis unit can also analyze voice in real time. For example, the voice analysis unit processes voice data in real time and provides an analysis result immediately. The text conversion unit converts voice data analyzed by the voice analysis unit into text. For example, the text conversion unit converts voice data into text using a voice-to-text conversion algorithm. The text conversion unit can also convert into text in real time. For example, the text conversion unit converts voice data into text in real time and displays it immediately. The translation unit translates the text converted by the text conversion unit. For example, the translation unit translates the text using a translation engine used. The translation unit can also translate in real time. For example, the translation unit translates text in real time and displays it immediately. The visual display unit visually displays the text translated by the translation unit. For example, the visual display unit visually displays text using a display format or a display device. The visual display unit can also adjust the display content to suit the user's visual characteristics. For example, the visual display unit can adjust the font size and color to improve visibility. The audio playback unit plays back the text converted by the text conversion unit as audio. For example, the audio playback unit can convert text to audio using a speech synthesis algorithm and play back the audio in the voice of the person making the call. The audio playback unit can also adjust the tone and speed of the audio playback based on the user's emotions. For example, if the user is nervous, the audio playback unit can play back audio in a calm tone. As a result, the telephone content visual understanding system according to the embodiment can visually understand the telephone content, allowing even people who are not good at making phone calls to make calls with confidence.

[0073] The speech analysis unit can analyze telephone voice in real time. Real time means that the delay time is extremely short and processing is performed immediately. For example, the speech analysis unit processes voice data in real time and provides analysis results immediately. The speech analysis unit can also analyze voice data using natural language processing technology. For example, the speech analysis unit performs morphological analysis and grammatical analysis to analyze voice data. This allows for real-time voice analysis, enabling instant text conversion and translation.

[0074] The text conversion unit can convert the analyzed voice data into text in real time. Real-time means that the delay time is extremely short and the processing is immediate. For example, the text conversion unit can convert voice data into text in real time and display it immediately. The text conversion unit can also convert voice data into text using a machine learning algorithm. For example, the text conversion unit can analyze voice data and convert it into text using K-means or hierarchical clustering. This allows for real-time conversion into text, which can then be displayed visually immediately.

[0075] The translation unit can translate text in real time. Real time means that the delay time is extremely short and the processing is immediate. For example, the translation unit translates text in real time and displays it immediately. The translation unit can also extract frequently occurring keywords to improve translation accuracy. For example, the translation unit extracts frequently occurring keywords using a frequency threshold or co-occurrence network analysis and reflects them in the translation. This allows for real-time translation, making it possible to instantly understand foreign languages, dialects, and strong accents.

[0076] The visual display unit can visually display the translated text. Visual display refers to displaying text in a format that is easy to understand visually. For example, the visual display unit can visually display the text using a display format or a display device. The visual display unit can also adjust the displayed content to suit the user's visual characteristics. For example, the visual display unit can adjust the font size and color to improve visibility. In this way, by visually displaying the translated text, the content of the call can be visually understood.

[0077] The voice playback unit can play back the text as audio in the voice of the person on the other end of the call. The voice of the other person refers to audio generated based on a voice sample of the person on the other end of the call. For example, the voice playback unit converts the text into audio using a speech synthesis algorithm and plays back the audio in the voice of the person on the other end of the call. The voice playback unit can also play back the audio taking into account the method of acquiring the audio sample and the accuracy of the speech synthesis. For example, the voice playback unit acquires an audio sample of the other person's voice and generates audio based on the audio sample. This allows for more natural communication by playing back the text in the other person's voice.

[0078] The voice analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated user emotions. Estimating emotions refers to recognizing the user's emotions using a voice analysis algorithm. For example, the voice analysis unit analyzes the user's voice data and estimates the emotions. The voice analysis unit can also adjust the accuracy of the voice analysis based on the estimated emotions. For example, if the user is nervous, the accuracy of the voice analysis can be increased to perform a more accurate analysis. Furthermore, if the user is relaxed, the analysis speed can be prioritized to provide analysis results quickly. Furthermore, if the user is feeling stressed, noise removal can be strengthened to perform a clear voice analysis. In this way, adjusting the accuracy of the voice analysis according to the user's emotions enables more accurate analysis.

[0079] The voice analysis unit can perform filtering to remove background noise during voice analysis. Background noise refers to unwanted environmental sounds contained in the voice data. For example, the voice analysis unit can detect surrounding environmental sounds and filter specific frequency bands to remove noise. The voice analysis unit can also separate the speaker's voice from background noise and analyze only the speaker's voice. Furthermore, the voice analysis unit can perform noise cancellation in real time to provide clear voice data. This enables clear voice analysis by removing background noise.

[0080] During voice analysis, the voice analysis unit can learn the characteristics of the speaker's voice and perform individually optimized analysis. Voice characteristics refer to the speaker's voice pitch, tone, pronunciation habits, etc. For example, the voice analysis unit learns the speaker's voice pitch and tone and performs individually optimized analysis. The voice analysis unit can also learn the speaker's pronunciation habits and improve analysis accuracy. Furthermore, the voice analysis unit can learn the strength and weakness of the speaker's voice and perform analysis at an appropriate volume. In this way, by learning the speaker's voice characteristics, analysis accuracy is improved.

[0081] During voice analysis, the voice analysis unit can adjust the analysis speed according to the speaker's speaking rate. Speaking rate refers to the speed at which the speaker speaks. For example, the voice analysis unit detects the speaker's speaking rate in real time and automatically adjusts the analysis speed. Furthermore, if the speaker's speaking rate is fast, the voice analysis unit can also speed up the analysis speed and perform analysis in real time. Furthermore, if the speaker's speaking rate is slow, the voice analysis unit can also perform detailed analysis by prioritizing analysis accuracy. This makes it possible to perform analysis in real time by adjusting the analysis speed according to the speaker's speaking rate.

[0082] The voice analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Estimating emotions refers to recognizing the user's emotions using a voice analysis algorithm. For example, the voice analysis unit analyzes the user's voice data and estimates emotions. The voice analysis unit can also adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Alternatively, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is feeling stressed, a visually soothing color can be used to display the results. This allows for more appropriate display by adjusting the display method according to the user's emotions.

[0083] During speech analysis, the speech analysis unit can improve analysis accuracy by taking into account the speaker's geographical background information. Geographical background information refers to the pronunciation and dialect specific to the speaker's region. For example, the speech analysis unit performs analysis taking into account specific dialects and accents based on the speaker's geographical background information. The speech analysis unit can also reflect specific phrases and expressions in the analysis based on the speaker's geographical background. Furthermore, the speech analysis unit can perform analysis by removing noise specific to the region. In this way, analysis accuracy is improved by taking into account the speaker's geographical background information.

[0084] During speech analysis, the speech analysis unit can improve analysis accuracy by referring to the speaker's past speech data. Past speech data refers to speech data previously spoken by the speaker. For example, the speech analysis unit improves analysis accuracy by learning specific pronunciations and phrases based on the speaker's past speech data. The speech analysis unit can also perform analysis by removing specific noise patterns by referring to the speaker's past speech data. Furthermore, the speech analysis unit can perform analysis corresponding to specific languages ​​or dialects based on the speaker's past speech data. In this way, analysis accuracy is improved by referring to the speaker's past speech data.

[0085] The speech analysis unit can incorporate a multilingual analysis function to support different languages ​​and dialects during speech analysis. A multilingual analysis function refers to an analysis function that supports multiple languages ​​and dialects. For example, the speech analysis unit incorporates a multilingual analysis function to support different languages ​​and dialects, thereby improving analysis accuracy. The speech analysis unit can also perform analysis using a dictionary to support a specific language or dialect. Furthermore, the speech analysis unit can also perform analysis using a speech model to support different languages ​​and dialects. This improves analysis accuracy by supporting different languages ​​and dialects.

[0086] The text conversion unit can estimate the user's emotion and adjust the expression method of the text conversion based on the estimated user's emotion. Estimating emotion refers to recognizing the user's emotion using a voice analysis algorithm. For example, the text conversion unit analyzes the user's voice data and estimates the emotion. The text conversion unit can also adjust the expression method of the text conversion based on the estimated emotion. For example, if the user is nervous, a simple and easy-to-understand expression method can be provided. Alternatively, if the user is relaxed, an expression method including detailed information can be provided. Furthermore, if the user is stressed, text can be displayed in a visually soothing font and color. This allows for more appropriate text display by adjusting the expression method of the text conversion according to the user's emotion.

[0087] The text conversion unit can use a dictionary for accurately converting technical terms and proper nouns during text conversion. Technical terms and proper nouns refer to specific terms and names associated with a particular field or individual. For example, the text conversion unit performs conversion using a dictionary for accurately converting technical terms and proper nouns. The text conversion unit can also improve conversion accuracy by using a dictionary specialized for a particular industry or field. Furthermore, the text conversion unit can learn technical terms and proper nouns frequently used by users and add them to the dictionary for conversion. This improves conversion accuracy by accurately converting technical terms and proper nouns.

[0088] The text conversion unit can insert appropriate punctuation marks by taking context into consideration when converting text. Taking context into consideration means processing by understanding the context and meaning of the sentence. For example, the text conversion unit can automatically insert appropriate punctuation marks by taking context into consideration. The text conversion unit can also analyze the rhythm and pauses of the speaker's speech to insert appropriate punctuation marks. Furthermore, the text conversion unit can also perform appropriate line breaks and paragraph divisions based on the context. In this way, natural-looking sentences can be generated by inserting punctuation marks by taking context into consideration.

[0089] During text conversion, the text conversion unit can generate natural-looking sentences that reflect the speaker's intention. Reflecting the speaker's intention means processing the sentences after understanding the speaker's intention and purpose. For example, the text conversion unit analyzes the speaker's intention and generates natural-looking sentences. The text conversion unit can also select appropriate expressions by taking into account the nuances and tone of the speaker's speech. Furthermore, the text conversion unit can generate sentences with a natural flow based on the context. This generates natural-looking sentences that reflect the speaker's intention, providing text that is easier to understand.

[0090] The text conversion unit can estimate the user's emotion and adjust the length of the text based on the estimated user emotion. Estimating emotion refers to recognizing the user's emotion using a voice analysis algorithm. For example, the text conversion unit analyzes the user's voice data and estimates the emotion. The text conversion unit can also adjust the length of the text based on the estimated emotion. For example, if the user is in a hurry, the text conversion unit can generate short, to-the-point text. If the user is relaxed, the text conversion unit can generate longer text containing detailed information. Furthermore, if the user is stressed, the text can be displayed in a visually soothing font and color. This allows for more appropriate text display by adjusting the length of the text according to the user's emotion.

[0091] The text conversion unit can adjust the conversion speed according to the speaker's speaking rate during text conversion. Speaking rate refers to the speed at which the speaker speaks. For example, the text conversion unit detects the speaker's speaking rate in real time and automatically adjusts the conversion speed. Furthermore, if the speaker's speaking rate is fast, the text conversion unit can also speed up the conversion speed and perform conversion in real time. Furthermore, if the speaker's speaking rate is slow, the text conversion unit can also perform detailed conversion by prioritizing conversion accuracy. As a result, real-time conversion is possible by adjusting the conversion speed according to the speaker's speaking rate.

[0092] During text conversion, the text conversion unit can improve conversion accuracy by referring to the speaker's past speech data. Past speech data refers to audio data previously spoken by the speaker. For example, the text conversion unit improves conversion accuracy by learning specific pronunciations and phrases based on the speaker's past speech data. The text conversion unit can also perform conversion by removing specific noise patterns by referring to the speaker's past speech data. Furthermore, the text conversion unit can perform conversion that corresponds to a specific language or dialect based on the speaker's past speech data. In this way, by referring to the speaker's past speech data, conversion accuracy is improved.

[0093] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. Estimating emotions refers to recognizing the user's emotions using a voice analysis algorithm. For example, the translation unit analyzes the user's voice data and estimates the emotions. The translation unit can also adjust the translation expression based on the estimated emotions. For example, if the user is nervous, a simple and easy-to-understand expression can be provided. Alternatively, if the user is relaxed, an expression containing more detailed information can be provided. Furthermore, if the user is stressed, the translation can be displayed in a visually soothing font and color. This allows the translation expression to be adjusted according to the user's emotions, thereby providing a more appropriate translation.

[0094] During translation, the translation unit can use a dictionary to accurately translate technical terms and proper nouns. Technical terms and proper nouns refer to specific terms and names associated with a particular field or individual. For example, the translation unit performs translation using a dictionary to accurately translate technical terms and proper nouns. The translation unit can also improve translation accuracy by using a dictionary specialized for a particular industry or field. Furthermore, the translation unit can learn technical terms and proper nouns frequently used by users and add them to the dictionary when translating. This improves translation accuracy by accurately translating technical terms and proper nouns.

[0095] The translation unit can insert appropriate punctuation marks by taking context into consideration when translating. Taking context into consideration means processing by understanding the context and meaning of a sentence. For example, the translation unit can automatically insert appropriate punctuation marks by taking context into consideration. The translation unit can also analyze the rhythm and pauses of a speaker's speech and insert appropriate punctuation marks. Furthermore, the translation unit can also perform appropriate line breaks and paragraph divisions based on the context. In this way, natural-looking sentences can be generated by inserting punctuation marks by taking context into consideration.

[0096] During translation, the translation unit can generate natural-sounding sentences that reflect the speaker's intention. Reflecting the speaker's intention means processing the sentences after understanding the speaker's intention and purpose. For example, the translation unit analyzes the speaker's intention and generates natural-sounding sentences. The translation unit can also select appropriate expressions by taking into account the nuances and tone of the speaker's speech. Furthermore, the translation unit can generate sentences that flow naturally based on the context. This generates natural-sounding sentences that reflect the speaker's intention, providing translations that are easier to understand.

[0097] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. Estimating emotions refers to recognizing the user's emotions using a voice analysis algorithm. For example, the translation unit analyzes the user's voice data to estimate emotions. The translation unit can also adjust the length of the translation based on the estimated emotions. For example, if the user is in a hurry, the translation unit can provide a short, to-the-point translation. If the user is relaxed, the translation unit can provide a longer translation with more detailed information. Furthermore, if the user is stressed, the translation can be displayed in a visually soothing font and color. This allows the translation length to be adjusted according to the user's emotions, resulting in a more appropriate translation.

[0098] The translation unit can adjust the translation speed during translation according to the speaker's speaking speed. Speaking speed refers to the speed at which the speaker speaks. For example, the translation unit detects the speaker's speaking speed in real time and automatically adjusts the translation speed. Furthermore, if the speaker's speaking speed is fast, the translation unit can also speed up the translation speed and perform real-time translation. Furthermore, if the speaker's speaking speed is slow, the translation unit can also prioritize translation accuracy and perform detailed translation. This makes it possible to perform real-time translation by adjusting the translation speed according to the speaker's speaking speed.

[0099] The translation unit can incorporate a multilingual translation function to support different languages ​​and dialects during translation. A multilingual translation function refers to a translation function that supports multiple languages ​​and dialects. For example, the translation unit incorporates a multilingual translation function to support different languages ​​and dialects, thereby improving translation accuracy. The translation unit can also perform translation using a dictionary to support a specific language or dialect. Furthermore, the translation unit can also perform translation using a translation model to support different languages ​​and dialects. This improves translation accuracy by supporting different languages ​​and dialects.

[0100] During translation, the translation unit can improve translation accuracy by referring to the speaker's past speech data. Past speech data refers to audio data previously spoken by the speaker. For example, the translation unit improves translation accuracy by learning specific pronunciations and phrases based on the speaker's past speech data. The translation unit can also refer to the speaker's past speech data and remove specific noise patterns before performing translation. Furthermore, the translation unit can also perform translation that corresponds to a specific language or dialect based on the speaker's past speech data. In this way, by referring to the speaker's past speech data, translation accuracy is improved.

[0101] The visual display unit can estimate the user's emotion and adjust the format of the display content based on the estimated user's emotion. Estimating emotion refers to recognizing the user's emotion using a voice analysis algorithm. For example, the visual display unit analyzes the user's voice data to estimate the emotion. The visual display unit can also adjust the format of the display content based on the estimated emotion. For example, if the user is nervous, a simple, highly visible format can be provided. Alternatively, if the user is relaxed, a format including detailed information can be provided. Furthermore, if the user is stressed, a visually soothing color can be used to display the content. This allows for a more appropriate display by adjusting the format of the display content according to the user's emotion.

[0102] The visual display unit can adjust the font size and color of the text to be displayed to suit the user's visual characteristics during visual display. Visual characteristics refer to characteristics related to the user's vision. For example, the visual display unit automatically adjusts the font size taking into account the user's visual characteristics. The visual display unit can also adjust the text color based on the user's visual characteristics. Furthermore, the visual display unit can adjust the background color and contrast based on the user's visual characteristics. In this way, adjusting the font size and color to suit the user's visual characteristics improves visibility.

[0103] The visual display unit can customize the displayed content based on the user's interests when visually displaying. Interests refer to information or themes in which the user is particularly interested. For example, the visual display unit can preferentially display information of interest based on the user's past operation history. The visual display unit can also display related information based on the user's interests. Furthermore, the visual display unit can customize the displayed content according to the user's interests. This allows the display content to be customized based on the user's interests, thereby providing more appropriate information.

[0104] The visual display unit can optimize the display content based on the user's past operation history when visually displaying. The past operation history refers to a record of operations previously performed by the user. For example, the visual display unit provides optimal display content based on the user's past operation history. The visual display unit can also refer to the user's past operation history and display related information. Furthermore, the visual display unit can customize the display content based on the user's past operation history. As a result, more appropriate information is provided by optimizing the display content based on the user's past operation history.

[0105] The visual display unit can estimate the user's emotions and prioritize the display content based on the estimated user emotions. Estimating emotions refers to recognizing the user's emotions using a voice analysis algorithm. For example, the visual display unit analyzes the user's voice data to estimate the emotion. The visual display unit can also prioritize the display content based on the estimated emotion. For example, if the user is nervous, important information can be displayed with priority. Also, if the user is relaxed, display content including detailed information can be provided. Furthermore, if the user is stressed, visually calming information can be displayed with priority. In this way, by prioritizing the display content according to the user's emotions, more appropriate information can be provided.

[0106] The visual display unit can select the optimal display method by taking into consideration the user's device information when visually displaying. Device information refers to the type and characteristics of the device used by the user. For example, the visual display unit provides the optimal display method based on the user's device information. The visual display unit can also adjust the display content to match the screen size of the user's device. Furthermore, the visual display unit can optimize the display content based on the resolution of the user's device. This improves visibility by selecting the optimal display method by taking into consideration the user's device information.

[0107] The visual display unit can make the displayed content multilingual in accordance with the user's language setting when visually displaying. The language setting refers to the setting of the language used by the user. For example, the visual display unit automatically translates the displayed content based on the user's language setting. The visual display unit can also provide a language switching function when the user uses multiple languages. Furthermore, the visual display unit can also make the displayed content multilingual in accordance with the user's language setting. This allows more appropriate information to be provided by making the displayed content multilingual in accordance with the user's language setting.

[0108] The visual display unit can customize the display content by taking into account the user's geographical background information when visually displaying the information. Geographical background information refers to pronunciations and dialects specific to the user's region. For example, the visual display unit displays related information based on the user's geographical background information. The visual display unit can also provide information related to a specific region based on the user's geographical background. Furthermore, the visual display unit can customize the display content by taking into account the user's geographical background information. In this way, by customizing the display content by taking into account the user's geographical background information, more appropriate information can be provided.

[0109] The audio playback unit can estimate the user's emotion and adjust the tone and speed of audio playback based on the estimated user's emotion. Estimating emotion refers to recognizing the user's emotion using an audio analysis algorithm. For example, the audio playback unit analyzes the user's voice data and estimates the emotion. The audio playback unit can also adjust the tone and speed of audio playback based on the estimated emotion. For example, if the user is nervous, the audio can be played back in a calm tone. If the user is relaxed, the audio can be played back in a bright tone. Furthermore, if the user is in a hurry, the audio can be played back at a faster playback speed. This allows for more appropriate audio playback by adjusting the tone and speed of audio playback according to the user's emotion.

[0110] When playing back audio, the audio playback unit can learn the characteristics of the speaker's voice and perform individually optimized audio playback. Voice characteristics refer to the speaker's voice pitch, tone, pronunciation habits, etc. For example, the audio playback unit can learn the speaker's voice pitch and tone and perform individually optimized audio playback. The audio playback unit can also learn the speaker's pronunciation habits and improve playback accuracy. Furthermore, the audio playback unit can learn the speaker's voice dynamics and play back at an appropriate volume. In this way, by learning the speaker's voice characteristics, the accuracy of audio playback is improved.

[0111] The audio playback unit can adjust the playback speed according to the speaker's speaking rate when playing back audio. Speaking rate refers to the speed at which the speaker speaks. For example, the audio playback unit detects the speaker's speaking rate in real time and automatically adjusts the playback speed. Furthermore, if the speaker's speaking rate is fast, the audio playback unit can also speed up the playback speed and play back in real time. Furthermore, if the speaker's speaking rate is slow, the audio playback unit can also prioritize playback accuracy and play back in detail. As a result, real-time playback is possible by adjusting the playback speed according to the speaker's speaking rate.

[0112] The audio playback unit can estimate the user's emotions and determine the priority of audio to be played back based on the estimated user emotions. Estimating emotions refers to recognizing the user's emotions using an audio analysis algorithm. For example, the audio playback unit analyzes the user's audio data and estimates the emotions. The audio playback unit can also determine the priority of audio to be played back based on the estimated emotions. For example, if the user is nervous, important information can be played back with priority. Also, if the user is relaxed, audio containing detailed information can be played back with priority. Furthermore, if the user is stressed, visually soothing audio can be played with priority. In this way, by determining the priority of audio to be played back according to the user's emotions, more appropriate audio playback is possible.

[0113] The audio playback unit can improve playback accuracy by taking into account the speaker's geographical background information when playing back audio. Geographical background information refers to the pronunciation and dialect specific to the speaker's region. For example, the audio playback unit performs playback taking into account a specific dialect or accent based on the speaker's geographical background information. The audio playback unit can also reflect specific phrases and expressions in the playback based on the speaker's geographical background. Furthermore, the audio playback unit can also remove noise specific to the region when playing back audio. In this way, playback accuracy is improved by taking into account the speaker's geographical background information.

[0114] The audio playback unit can improve playback accuracy by referring to the speaker's past speech data when playing back audio. Past speech data refers to audio data previously spoken by the speaker. For example, the audio playback unit can improve playback accuracy by learning specific pronunciations and phrases based on the speaker's past speech data. The audio playback unit can also refer to the speaker's past speech data and remove specific noise patterns before playing back audio. Furthermore, the audio playback unit can also play back audio that corresponds to a specific language or dialect based on the speaker's past speech data. In this way, playback accuracy is improved by referring to the speaker's past speech data.

[0115] The audio playback unit can incorporate a multilingual playback function to support different languages ​​and dialects during audio playback. The multilingual playback function refers to a playback function that supports multiple languages ​​and dialects. For example, the audio playback unit incorporates a multilingual playback function to support different languages ​​and dialects, thereby improving playback accuracy. The audio playback unit can also perform playback using a dictionary to support a specific language or dialect. Furthermore, the audio playback unit can perform playback using a speech model to support different languages ​​and dialects. This improves playback accuracy by supporting different languages ​​and dialects. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned voice analysis unit, text conversion unit, translation unit, visual display unit, and audio playback unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the smart device 14 and analyzes telephone voice. The text conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts voice data into text. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the text. The visual display unit is realized by the display 40A of the smart device 14 and visually displays the translated text. The audio playback unit is realized by the speaker 40B of the smart device 14 and plays the text as audio. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned voice analysis unit, text conversion unit, translation unit, visual display unit, and audio playback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes telephone voice. The text conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts voice data into text. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the text. The visual display unit is realized by the display of the smart glasses 214 and visually displays the translated text. The audio playback unit is realized by the speaker 240 of the smart glasses 214 and plays the text as audio. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned voice analysis unit, text conversion unit, translation unit, visual display unit, and voice playback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes telephone voice. The text conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts voice data into text. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the text. The visual display unit is realized by the display 343 of the headset type terminal 314 and visually displays the translated text. The voice playback unit is realized by the speaker 240 of the headset type terminal 314 and plays the text as voice. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice analysis unit, text conversion unit, translation unit, visual display unit, and voice playback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the robot 414 and analyzes telephone voice. The text conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts voice data into text. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the text. The visual display unit is realized by the display device of the robot 414 and visually displays the translated text. The voice playback unit is realized by the speaker 240 of the robot 414 and plays the text as voice.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The voice analysis unit can estimate the user's emotions and adjust the reliability of the analysis results based on the estimated emotions. For example, if the user is nervous, a more rigorous analysis can be performed to increase the reliability of the analysis results. Also, if the user is relaxed, analysis speed can be prioritized to provide analysis results quickly. Furthermore, if the user is feeling stressed, noise removal can be strengthened to perform clear voice analysis. This allows for more accurate analysis by adjusting the reliability of the analysis results according to the user's emotions.

[0118] During voice analysis, the voice analysis unit can learn the characteristics of the speaker's voice and perform individually optimized analysis. For example, the voice analysis unit can learn the pitch and tone of the speaker's voice and perform individually optimized analysis. The voice analysis unit can also learn the speaker's pronunciation habits and improve analysis accuracy. Furthermore, the voice analysis unit can learn the strength and weakness of the speaker's voice and perform analysis at an appropriate volume. In this way, by learning the characteristics of the speaker's voice, analysis accuracy is improved.

[0119] The text conversion unit can estimate the user's emotions and adjust the expression method of the text conversion based on the estimated emotions. For example, if the user is nervous, a simple and easy-to-understand expression method can be provided. If the user is relaxed, an expression method including detailed information can be provided. Furthermore, if the user is feeling stressed, text can be displayed in a visually soothing font and color. This allows for more appropriate text display by adjusting the expression method of the text conversion according to the user's emotions.

[0120] The text conversion unit can use a dictionary for accurately converting technical terms and proper nouns during text conversion. For example, the text conversion unit performs conversion using a dictionary for accurately converting technical terms and proper nouns. The text conversion unit can also improve conversion accuracy by using a dictionary specialized for a specific industry or field. Furthermore, the text conversion unit can learn technical terms and proper nouns frequently used by users and add them to the dictionary for conversion. This improves conversion accuracy by accurately converting technical terms and proper nouns.

[0121] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated emotions. For example, if the user is nervous, a simple and easy-to-understand expression can be provided. If the user is relaxed, an expression containing detailed information can be provided. Furthermore, if the user is stressed, the translation can be displayed in a visually soothing font and color. In this way, by adjusting the translation expression according to the user's emotions, a more appropriate translation can be provided.

[0122] The translation unit can insert appropriate punctuation marks while taking the context into consideration when translating. For example, the translation unit automatically inserts appropriate punctuation marks while taking the context into consideration. The translation unit can also analyze the rhythm and pauses of the speaker's speech and insert appropriate punctuation marks. Furthermore, the translation unit can also perform appropriate line breaks and paragraph divisions based on the context. As a result, natural-looking sentences are generated by inserting punctuation marks while taking the context into consideration.

[0123] The visual display unit can estimate the user's emotions and adjust the format of the display content based on the estimated emotions. For example, if the user is nervous, a simple, highly visible format can be provided. Alternatively, if the user is relaxed, a format including detailed information can be provided. Furthermore, if the user is feeling stressed, the display can be displayed in a visually soothing color scheme. This allows for more appropriate display by adjusting the format of the display content according to the user's emotions.

[0124] The visual display unit can adjust the font size and color of the displayed text to suit the user's visual characteristics during visual display. For example, the visual display unit automatically adjusts the font size taking into account the user's visual characteristics. The visual display unit can also adjust the text color based on the user's visual characteristics. Furthermore, the visual display unit can adjust the background color and contrast based on the user's visual characteristics. In this way, adjusting the font size and color to suit the user's visual characteristics improves visibility.

[0125] The audio playback unit can estimate the user's emotions and adjust the tone and speed of audio playback based on the estimated emotions. For example, if the user is nervous, audio can be played back in a calm tone. If the user is relaxed, audio can be played back in a bright tone. Furthermore, if the user is in a hurry, audio can be played back at a faster playback speed. In this way, more appropriate audio playback is possible by adjusting the tone and speed of audio playback according to the user's emotions.

[0126] During audio playback, the audio playback unit can learn the characteristics of the speaker's voice and perform individually optimized audio playback. For example, the audio playback unit can learn the pitch and tone of the speaker's voice and perform individually optimized audio playback. The audio playback unit can also learn the speaker's pronunciation habits and improve playback accuracy. Furthermore, the audio playback unit can learn the strength and weakness of the speaker's voice and play back at an appropriate volume. In this way, by learning the characteristics of the speaker's voice, the accuracy of audio playback is improved.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The voice analysis unit analyzes the voice of the call. For example, the voice analysis unit can analyze the voice data using a voice recognition algorithm and analyze the voice in real time. The voice data is processed in real time and the analysis result is provided immediately. Step 2: The text conversion unit converts the voice data analyzed by the voice analysis unit into text. For example, the voice data can be converted into text using a voice-to-text conversion algorithm, and the text can be converted in real time. The voice data can be converted into text in real time and displayed immediately. Step 3: The translation unit translates the text converted by the text conversion unit. For example, the translation unit may translate the text using a translation engine in real time. The text is translated in real time and displayed immediately. Step 4: The visual display unit visually displays the text translated by the translation unit. For example, the visual display unit may use a display format or device to visually display the text, and may adjust the displayed content to suit the user's visual characteristics. For example, the visual display unit may adjust the font size and color to improve visibility. Step 5: The voice playback unit plays back the text converted by the text conversion unit as voice. For example, it uses a speech synthesis algorithm to convert the text into voice and plays it back in the voice of the person on the phone. It can also adjust the tone and speed of the voice playback based on the user's emotions. For example, if the user is nervous, the voice will be played back in a calm tone.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] 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.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] 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.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] 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.

[0174] 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.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0189] 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.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0192] 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.

[0193] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0194] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0200] [Explanation of symbols]

[0201] 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 voice analysis unit that analyzes the voice of the telephone; a text conversion unit that converts the voice data analyzed by the voice analysis unit into text; a translation unit that translates the text converted by the text conversion unit; a visual display unit that visually displays the text translated by the translation unit; a voice playback unit that plays back the text converted by the text conversion unit as voice. A system characterized by:

2. The voice analysis unit Analyzes telephone voice in real time 2. The system of claim 1.

3. The text conversion unit Convert analyzed voice data into text in real time 2. The system of claim 1.

4. The translation unit Translate text in real time 2. The system of claim 1.

5. The visual display unit is Visually display the translated text 2. The system of claim 1.

6. The audio playback unit Play text as audio in the caller's voice 2. The system of claim 1.

7. The voice analysis unit Estimate the user's emotions and adjust the accuracy of voice analysis based on the estimated user emotions.

2. The system of claim 1.

8. The voice analysis unit When analyzing audio, filtering is performed to remove background noise.

2. The system of claim 1.

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