system

Abstract

The system according to the embodiment aims to enable hearing-impaired people to understand the content of spoken words in real time. [Solution] A system according to an embodiment includes a voice collection unit, a voice analysis unit, a text conversion unit, and a text display unit. The voice collection unit collects voice. The voice analysis unit analyzes the voice collected by the voice collection unit. The text conversion unit converts the voice analyzed by the voice analysis unit into text. The text display unit displays the text converted by the text conversion unit on an eyeglass-type device.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for hearing-impaired people to understand the content of spoken words in real time.

[0005] The system according to the embodiment aims to enable hearing-impaired people to understand the content of spoken words in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice collection unit, a voice analysis unit, a text conversion unit, and a text display unit. The voice collection unit collects voice. The voice analysis unit analyzes the voice collected by the voice collection unit. The text conversion unit converts the voice analyzed by the voice analysis unit into text. The text display unit displays the text converted by the text conversion unit on the eyeglass device. [Effects of the Invention]

[0007] The system according to the embodiment can enable the hearing impaired to understand the content of spoken words in real time. [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 supports timely communication for persons with hearing impairments. This system collects speech, analyzes it using AI, converts it into text, and displays the text on a glasses-type device. For example, a microphone collects spoken words, and AI analyzes the speech data. The analyzed speech data is then converted into text and displayed on a glasses-type device. This allows persons with hearing impairments to visually confirm the content of a conversation in real time. This allows the system to instantly understand the content of a conversation. This allows persons with hearing impairments to visually confirm the content of a conversation in real time. For example, in a meeting or everyday conversation, the system converts the other person's spoken words into text in real time and displays it on a glasses-type device, allowing persons with hearing impairments to instantly understand the content of the conversation. Furthermore, because the analysis of speech data and the conversion to text are performed by AI, high accuracy is possible. Furthermore, the glasses-type device is lightweight, easy to carry, and easy to use in everyday life. For example, it can be used in a variety of situations, such as on the go or at work. This broadens the scope of communication for persons with hearing impairments and promotes their social participation.

[0029] A communication support system according to an embodiment includes a voice collection unit, a voice analysis unit, a text conversion unit, and a text display unit. The voice collection unit collects voice. For example, the voice collection unit uses a microphone to pick up the voice of a person in a conversation and collects the voice data. The voice collection unit may also include a filtering function to remove ambient noise. For example, the voice collection unit may use a filter to remove noise such as wind or traffic noise. The voice analysis unit analyzes the collected voice data. The voice analysis unit analyzes spoken words using, for example, voice recognition technology and converts the content into text. For example, the voice analysis unit converts voice data such as "hello" into text such as "hello." The text conversion unit converts the analyzed voice data into text. The text conversion unit can perform highly accurate text conversion using, for example, voice recognition technology. The text display unit displays the converted text on the eyeglass device. For example, the text display unit displays text within the user's field of vision, allowing a hearing-impaired person to check the content of a conversation in real time. As a result, the communication support system according to the embodiment allows the hearing impaired person to visually check the content of the conversation in real time.

[0030] The sound collection unit has a filtering function for removing ambient noise. Ambient noise includes, for example, wind noise, human speech, and traffic noise. The sound collection unit can use, for example, a noise canceling filter for removing wind noise. The sound collection unit can also use spectral subtraction technology for removing human speech. Furthermore, the sound collection unit can use a bandpass filter for removing traffic noise. This removes ambient noise, thereby improving the accuracy of sound collection.

[0031] The text display unit has a function for adjusting the font size, color, or display position of the displayed text. Font sizes include, for example, minimum size, maximum size, and recommended size. The text display unit can adjust the font size according to, for example, the user's eyesight. The text display unit can also use, for example, RGB values, hue, and saturation as the color adjustment range. Furthermore, the text display unit can also use, for example, the top, bottom, left, right, and center position of the screen as the display position adjustment range. This improves the user's visual comfort.

[0032] The speech analysis unit has a function of learning specific speech patterns. The specific speech patterns include, for example, specific words, phrases, and tones. The speech analysis unit can use deep learning technology, for example, to learn specific words. The speech analysis unit can also use an HMM (Hidden Markov Model) to learn specific phrases. Furthermore, the speech analysis unit can also use an acoustic model to learn specific tones. In this way, by learning specific speech patterns, analysis accuracy is improved.

[0033] The text display unit is designed to improve the battery life and durability of the eyeglass-type device. Battery life includes, for example, usage time, charging time, and battery capacity. The text display unit can extend battery life by using, for example, low-power display technology. Durability includes, for example, impact resistance, waterproofness, and heat resistance. For example, the text display unit can use tempered glass to improve impact resistance. Furthermore, the text display unit can be coated with a waterproof coating to improve waterproofness. This improves the battery life and durability of the eyeglass-type device.

[0034] The voice collection unit has a function of emphasizing and collecting a specific frequency band. The specific frequency band includes, for example, low frequency, high frequency, and mid frequency. The voice collection unit can, for example, emphasize and collect the frequency band of human voices to suppress background noise. The voice collection unit can also emphasize and collect a specific voice pattern (for example, a keyword in a conversation). Furthermore, the voice collection unit can also emphasize and collect a specific frequency band set by the user. Thus, by emphasizing a specific frequency band, the accuracy of voice collection is improved.

[0035] The voice collection unit has a function of dynamically adjusting the collection range. The collection range includes, for example, a collection distance and a collection angle. The voice collection unit can automatically adjust the collection range according to, for example, the user's position. The voice collection unit can also widen or narrow the collection range according to the situation of the conversation. Furthermore, the voice collection unit can manually adjust the collection range based on a user's instruction. This dynamically adjusts the collection range, thereby improving the efficiency of voice collection.

[0036] The voice collection unit evaluates the quality of the collected voice data in real time and selects the most appropriate collection method. The quality of the voice data includes, for example, the noise level and the clarity of the voice. The voice collection unit can, for example, evaluate the noise level of the collected voice data in real time and select a method with the least noise. The voice collection unit can also evaluate the clarity of the collected voice data and select a method for collecting the clearest voice. Furthermore, the voice collection unit can evaluate the signal strength of the collected voice data and select the optimal collection method. In this way, the optimal collection method can be selected by evaluating the quality of the voice data in real time.

[0037] The voice collection unit prioritizes collection of highly relevant voices based on the user's geographical location information. Geographical location information includes, for example, GPS data, location information services, and the like. For example, when the user is in a specific location, the voice collection unit can prioritize collection of voices related to that location. Furthermore, when the user is moving, the voice collection unit can also prioritize collection of voices related to the user's destination. Furthermore, when the user is participating in a specific event, the voice collection unit can also prioritize collection of voices related to the event. In this way, by taking geographical location information into consideration, highly relevant voices can be collected preferentially.

[0038] The audio collection unit analyzes the user's social media activity and collects related audio. Social media activity includes, for example, the content of posts, the number of likes, the number of comments, etc. The audio collection unit can collect, for example, audio related to places where the user has checked in on social media. The audio collection unit can also analyze the content of the user's posts on social media and collect related audio. Furthermore, the audio collection unit can also collect related audio by referring to the activities of the user's friends on social media. In this way, related audio can be collected by analyzing social media activity.

[0039] The voice collection unit customizes the collection method by reflecting the user's past feedback. Past feedback includes, for example, user reviews, survey results, and the like. The voice collection unit can adjust the collection method, for example, based on feedback provided by the user in the past. The voice collection unit can also preferentially collect specific voice patterns from the user's past feedback. Furthermore, the voice collection unit can analyze the user's past feedback and propose an optimal collection method. In this way, the collection method can be optimized by reflecting the past feedback.

[0040] The voice analysis unit learns specific voice patterns in real time to improve analysis accuracy. Learning in real time includes, for example, online learning and sequential learning. The voice analysis unit can, for example, learn specific keywords in a conversation in real time to improve analysis accuracy. The voice analysis unit can also learn the user's pronunciation habits in real time to improve analysis accuracy. Furthermore, the voice analysis unit can also learn background sounds and noise in real time to improve analysis accuracy. As a result, analysis accuracy is improved by learning specific voice patterns in real time.

[0041] The speech analysis unit has a multilingual analysis function to support different languages ​​and dialects. The multilingual analysis function includes, for example, a language model and a translation algorithm. The speech analysis unit can, for example, analyze speech data in different languages ​​and convert it into text. The speech analysis unit can also analyze differences in dialects and accents and convert it into accurate text. Furthermore, if a user speaks multiple languages, the speech analysis unit can perform analysis corresponding to each language. Thus, the multilingual analysis function makes it possible to support different languages ​​and dialects.

[0042] The voice analysis unit applies advanced filtering technology to remove background sounds and noise. Examples of advanced filtering technology include noise canceling and spectral subtraction. The voice analysis unit can, for example, remove background sounds during conversation in real time to analyze clear voice data. The voice analysis unit can also extract and analyze important sounds even in noisy environments. Furthermore, the voice analysis unit can filter noise in specific frequency bands to improve analysis accuracy. As a result, analysis accuracy is improved by applying advanced filtering technology.

[0043] The voice analysis unit improves the accuracy of analysis by referring to the user's past conversation history. Past conversation history includes, for example, recorded data, text logs, etc. The voice analysis unit can, for example, prioritize the analysis of specific keywords or phrases based on the user's past conversation history. The voice analysis unit can also learn pronunciation habits from the user's past conversation history to improve the accuracy of analysis. Furthermore, the voice analysis unit can analyze the user's past conversation history and apply an optimal analysis algorithm. In this way, the analysis accuracy is improved by referring to the past conversation history.

[0044] The voice analysis unit adjusts the level of detail of the analysis results according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and advanced. For example, if the user has expertise, the voice analysis unit can provide detailed analysis results. Furthermore, if the user is a beginner, the voice analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the voice analysis unit can customize the display method of the analysis results according to the user's level of expertise. In this way, appropriate information can be provided by adjusting the level of detail of the analysis results according to the user's level of expertise.

[0045] The audio analysis unit optimizes the analysis algorithm based on the user's current environmental information. The current environmental information includes, for example, temperature, humidity, and noise level. For example, if the user is in a quiet environment, the audio analysis unit can apply an algorithm that performs detailed analysis. Also, if the user is in a noisy environment, the audio analysis unit can apply an algorithm that removes noise. Furthermore, the audio analysis unit can select the optimal analysis algorithm based on the user's current environmental information. This allows the analysis algorithm to be optimized by taking the current environmental information into consideration.

[0046] The text conversion unit has a function of highlighting specific keywords and phrases. Specific keywords and phrases include, for example, importance and frequency. The text conversion unit can, for example, highlight important keywords in a conversation. The text conversion unit can also highlight specific phrases set by the user. Furthermore, the text conversion unit can automatically highlight important information during text conversion. This allows important information to be emphasized by highlighting specific keywords and phrases.

[0047] The text conversion unit has a function for automatically translating into different languages. The automatic translation includes, for example, a translation algorithm, supported languages, and the like. The text conversion unit can, for example, translate the voice of a conversation into different languages ​​in real time and convert it into text. In addition, if a user speaks multiple languages, the text conversion unit can also perform text conversion corresponding to each language. Furthermore, the text conversion unit can analyze voice data in different languages ​​and convert it into accurate text. This enables multilingual support by automatically translating into different languages.

[0048] The text conversion unit improves conversion accuracy by referring to the user's past text conversion results. Past text conversion results include, for example, conversion logs and user feedback. The text conversion unit can, for example, preferentially convert specific keywords or phrases based on the user's past text conversion results. The text conversion unit can also learn the user's pronunciation habits from the user's past text conversion results and improve conversion accuracy. Furthermore, the text conversion unit can analyze the user's past text conversion results and apply the optimal conversion algorithm. In this way, conversion accuracy is improved by referring to the past text conversion results.

[0049] The text conversion unit determines the priority of text based on the time of submission by the user. The submission time includes, for example, the submission date and time, the submission deadline, etc. For example, if the user is in a hurry, the text conversion unit can convert text submitted earlier with priority. Furthermore, if the user is relaxed, the text conversion unit can convert all text equally regardless of the time of submission. Furthermore, the text conversion unit can convert the most important text with priority based on the time of submission by the user. In this way, by determining the priority of text based on the time of submission, important text can be converted with priority.

[0050] The text conversion unit adjusts the order of text based on the user's relevance. Relevance includes, for example, the degree of content match and relevance score. The text conversion unit can, for example, preferentially convert text that the user considers important. The text conversion unit can also preferentially convert highly relevant text based on the user's past behavior history. Furthermore, the text conversion unit can also preferentially convert the most relevant text based on the user's current situation. In this way, important information can be preferentially displayed by adjusting the order of text based on relevance.

[0051] The text conversion unit adjusts the use of technical terms in the text according to the user's level of expertise. Technical terms include, for example, technical terms, industry jargon, etc. For example, if the user has specialized knowledge, the text conversion unit can provide text that uses a lot of technical terms. In addition, if the user is a beginner, the text conversion unit can provide concise and easy-to-understand text. Furthermore, the text conversion unit can adjust the use of technical terms in the text according to the user's level of expertise. As a result, appropriate information can be provided by adjusting the use of technical terms according to the user's level of expertise.

[0052] The text display unit customizes the display method based on the user's visual acuity information. The visual acuity information includes, for example, the results of a visual acuity test, a diagnosis by an ophthalmologist, etc. For example, if the user has poor visual acuity, the text display unit can display text in a large font size. Alternatively, if the user has good visual acuity, the text display unit can display text in a standard font size. Furthermore, the text display unit can select the optimal font size and display method based on the user's visual acuity information. This allows the optimal display method to be provided by taking the visual acuity information into consideration.

[0053] The text display unit has a function of automatically adjusting the display brightness according to the user's current ambient light. The ambient light includes, for example, a light sensor, an illuminance meter, etc. For example, the text display unit can automatically increase the display brightness when the user is in a bright environment. The text display unit can also automatically decrease the display brightness when the user is in a dark environment. Furthermore, the text display unit can automatically adjust the display brightness to an optimum level based on the user's current ambient light. This improves visibility by automatically adjusting the brightness according to the ambient light.

[0054] The text display unit selects the optimal display method by referring to the user's past display settings. The past display settings include, for example, a user profile and a setting log. The text display unit can, for example, select the optimal font size and color based on the user's past display settings. The text display unit can also preferentially apply a specific display method from the user's past display settings. Furthermore, the text display unit can analyze the user's past display settings and suggest the optimal display method. In this way, the optimal display method can be provided by referring to the past display settings.

[0055] The text display unit selects the most appropriate display method based on the user's device information. The device information includes, for example, the device type, specifications, OS version, etc. For example, if the user is using a smartphone, the text display unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the text display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the text display unit can provide a simple and highly visible display method. This allows the optimal display method to be provided by taking device information into consideration.

[0056] The text display unit supports multiple languages ​​in display content according to the user's language setting. The language setting includes, for example, the user's preferred language and the device's language setting. The text display unit can automatically set the display content based on, for example, the user's device's language setting. The text display unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the text display unit can provide the display content in that language. This improves user convenience by supporting multiple languages ​​according to the language setting.

[0057] The text display unit customizes the display method by reflecting user feedback. The feedback includes, for example, user reviews, survey results, etc. The text display unit can adjust the display method based on, for example, the feedback provided by the user. The text display unit can also preferentially apply a specific display method based on the user feedback. Furthermore, the text display unit can analyze the user feedback and suggest the optimal display method. In this way, the optimal display method can be provided by reflecting the feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The voice analysis unit can also improve analysis accuracy by referring to the user's past conversation history. For example, it can prioritize analysis of specific keywords or phrases based on the user's past conversation history. It can also learn pronunciation habits from the user's past conversation history to improve analysis accuracy. It can also analyze the user's past conversation history and apply the optimal analysis algorithm. In this way, analysis accuracy can be improved by referring to the past conversation history.

[0060] The text display unit can also customize the display method based on the user's visual acuity information. For example, if the user has poor visual acuity, it can display text in a large font size. On the other hand, if the user has good visual acuity, it can display text in a standard font size. Furthermore, it can select the optimal font size and display method based on the user's visual acuity information. This allows the optimal display method to be provided by taking visual acuity information into consideration.

[0061] The voice collection unit can also prioritize collection of highly relevant voices based on the user's geographical location information. For example, if the user is in a specific location, voices related to that location can be collected preferentially. Also, if the user is traveling, voices related to the destination can be collected preferentially. Furthermore, if the user is participating in a specific event, voices related to the event can be collected preferentially. In this way, highly relevant voices can be collected preferentially by taking geographical location information into consideration.

[0062] The voice analysis unit can also adjust the level of detail in the analysis results according to the user's level of expertise. For example, if the user has expertise, it can provide detailed analysis results. On the other hand, if the user is a beginner, it can provide concise and easy-to-understand analysis results. Furthermore, it can customize the way in which the analysis results are displayed according to the user's level of expertise. This allows the user to provide appropriate information by adjusting the level of detail in the analysis results according to the user's level of expertise.

[0063] The text display unit may also have a function for automatically adjusting the display brightness according to the user's current ambient light. For example, if the user is in a bright environment, the display brightness can be automatically increased. Also, if the user is in a dark environment, the display brightness can be automatically decreased. Furthermore, the display brightness can be automatically adjusted to an optimum value based on the user's current ambient light. This improves visibility by automatically adjusting the brightness according to the ambient light.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The audio collection unit collects audio. For example, the audio data is collected by using a microphone to pick up the voice of the person on the other end of a conversation. The audio collection unit may also have a filtering function to remove ambient noise. For example, a filter can be used to remove noise such as wind noise and traffic sounds. Step 2: The voice analysis unit analyzes the collected voice data. For example, it uses voice recognition technology to analyze spoken words and converts the content into text. For example, it converts the voice data of "Hello" into the text "Hello." Step 3: The text conversion unit converts the analyzed voice data into text. For example, highly accurate text conversion can be achieved using voice recognition technology. Step 4: The text display unit displays the converted text on the eyeglasses. For example, by displaying the text in the user's field of vision, a hearing-impaired person can check the content of the conversation in real time.

[0066] (Example 2) A system according to an embodiment of the present invention supports timely communication for persons with hearing impairments. This system collects speech, analyzes it using AI, converts it into text, and displays the text on a glasses-type device. For example, a microphone collects spoken words, and AI analyzes the speech data. The analyzed speech data is then converted into text and displayed on a glasses-type device. This allows persons with hearing impairments to visually confirm the content of a conversation in real time. This allows the system to instantly understand the content of a conversation. This allows persons with hearing impairments to visually confirm the content of a conversation in real time. For example, in a meeting or everyday conversation, the system converts the other person's spoken words into text in real time and displays it on a glasses-type device, allowing persons with hearing impairments to instantly understand the content of the conversation. Furthermore, because the analysis of speech data and the conversion to text are performed by AI, high accuracy is possible. Furthermore, the glasses-type device is lightweight, easy to carry, and easy to use in everyday life. For example, it can be used in a variety of situations, such as on the go or at work. This broadens the scope of communication for persons with hearing impairments and promotes their social participation.

[0067] A communication support system according to an embodiment includes a voice collection unit, a voice analysis unit, a text conversion unit, and a text display unit. The voice collection unit collects voice. For example, the voice collection unit uses a microphone to pick up the voice of a person in a conversation and collects the voice data. The voice collection unit may also include a filtering function to remove ambient noise. For example, the voice collection unit may use a filter to remove noise such as wind or traffic noise. The voice analysis unit analyzes the collected voice data. The voice analysis unit analyzes spoken words using, for example, voice recognition technology and converts the content into text. For example, the voice analysis unit converts voice data such as "hello" into text such as "hello." The text conversion unit converts the analyzed voice data into text. The text conversion unit can perform highly accurate text conversion using, for example, voice recognition technology. The text display unit displays the converted text on the eyeglass device. For example, the text display unit displays text within the user's field of vision, allowing a hearing-impaired person to check the content of a conversation in real time. As a result, the communication support system according to the embodiment allows the hearing impaired person to visually check the content of the conversation in real time.

[0068] The sound collection unit has a filtering function for removing ambient noise. Ambient noise includes, for example, wind noise, human speech, and traffic noise. The sound collection unit can use, for example, a noise canceling filter for removing wind noise. The sound collection unit can also use spectral subtraction technology for removing human speech. Furthermore, the sound collection unit can use a bandpass filter for removing traffic noise. This removes ambient noise, thereby improving the accuracy of sound collection.

[0069] The text display unit has a function for adjusting the font size, color, or display position of the displayed text. Font sizes include, for example, minimum size, maximum size, and recommended size. The text display unit can adjust the font size according to, for example, the user's eyesight. The text display unit can also use, for example, RGB values, hue, and saturation as the color adjustment range. Furthermore, the text display unit can also use, for example, the top, bottom, left, right, and center position of the screen as the display position adjustment range. This improves the user's visual comfort.

[0070] The speech analysis unit has a function of learning specific speech patterns. The specific speech patterns include, for example, specific words, phrases, and tones. The speech analysis unit can use deep learning technology, for example, to learn specific words. The speech analysis unit can also use an HMM (Hidden Markov Model) to learn specific phrases. Furthermore, the speech analysis unit can also use an acoustic model to learn specific tones. In this way, by learning specific speech patterns, analysis accuracy is improved.

[0071] The text display unit is designed to improve the battery life and durability of the eyeglass-type device. Battery life includes, for example, usage time, charging time, and battery capacity. The text display unit can extend battery life by using, for example, low-power display technology. Durability includes, for example, impact resistance, waterproofness, and heat resistance. For example, the text display unit can use tempered glass to improve impact resistance. Furthermore, the text display unit can be coated with a waterproof coating to improve waterproofness. This improves the battery life and durability of the eyeglass-type device.

[0072] The voice collection unit estimates the user's emotion and adjusts the timing of voice collection based on the estimated user emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the voice collection unit delays the timing of voice collection to give the user time to relax. Furthermore, if the user is relaxed, the voice collection unit can also advance the timing of voice collection to promote smooth conversation. Furthermore, if the user is in a hurry, the voice collection unit can immediately timing voice collection to support quick communication. This allows the timing of voice collection to be optimized according to the user's emotion.

[0073] The voice collection unit has a function of emphasizing and collecting a specific frequency band. The specific frequency band includes, for example, low frequency, high frequency, and mid frequency. The voice collection unit can, for example, emphasize and collect the frequency band of human voices to suppress background noise. The voice collection unit can also emphasize and collect a specific voice pattern (for example, a keyword in a conversation). Furthermore, the voice collection unit can also emphasize and collect a specific frequency band set by the user. Thus, by emphasizing a specific frequency band, the accuracy of voice collection is improved.

[0074] The voice collection unit has a function of dynamically adjusting the collection range. The collection range includes, for example, a collection distance and a collection angle. The voice collection unit can automatically adjust the collection range according to, for example, the user's position. The voice collection unit can also widen or narrow the collection range according to the situation of the conversation. Furthermore, the voice collection unit can manually adjust the collection range based on a user's instruction. This dynamically adjusts the collection range, thereby improving the efficiency of voice collection.

[0075] The voice collection unit evaluates the quality of the collected voice data in real time and selects the most appropriate collection method. The quality of the voice data includes, for example, the noise level and the clarity of the voice. The voice collection unit can, for example, evaluate the noise level of the collected voice data in real time and select a method with the least noise. The voice collection unit can also evaluate the clarity of the collected voice data and select a method for collecting the clearest voice. Furthermore, the voice collection unit can evaluate the signal strength of the collected voice data and select the optimal collection method. In this way, the optimal collection method can be selected by evaluating the quality of the voice data in real time.

[0076] The voice collection unit estimates the user's emotions and determines the priority of the voices to be collected based on the estimated user emotions. The emotion estimation is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the voice collection unit prioritizes collecting important voices when the user is nervous. The voice collection unit can also collect all voices equally when the user is relaxed. Furthermore, the voice collection unit can also prioritize collecting important voices in a short time when the user is in a hurry. In this way, important voices can be collected preferentially by determining the priority of voices based on the user's emotions.

[0077] The voice collection unit prioritizes collection of highly relevant voices based on the user's geographical location information. Geographical location information includes, for example, GPS data, location information services, and the like. For example, when the user is in a specific location, the voice collection unit can prioritize collection of voices related to that location. Furthermore, when the user is moving, the voice collection unit can also prioritize collection of voices related to the user's destination. Furthermore, when the user is participating in a specific event, the voice collection unit can also prioritize collection of voices related to the event. In this way, by taking geographical location information into consideration, highly relevant voices can be collected preferentially.

[0078] The audio collection unit analyzes the user's social media activity and collects related audio. Social media activity includes, for example, the content of posts, the number of likes, the number of comments, etc. The audio collection unit can collect, for example, audio related to places where the user has checked in on social media. The audio collection unit can also analyze the content of the user's posts on social media and collect related audio. Furthermore, the audio collection unit can also collect related audio by referring to the activities of the user's friends on social media. In this way, related audio can be collected by analyzing social media activity.

[0079] The voice collection unit customizes the collection method by reflecting the user's past feedback. Past feedback includes, for example, user reviews, survey results, and the like. The voice collection unit can adjust the collection method, for example, based on feedback provided by the user in the past. The voice collection unit can also preferentially collect specific voice patterns from the user's past feedback. Furthermore, the voice collection unit can analyze the user's past feedback and propose an optimal collection method. In this way, the collection method can be optimized by reflecting the past feedback.

[0080] The voice analysis unit estimates the user's emotions and adjusts the voice analysis algorithm based on the estimated user emotions. Emotion estimation is achieved, for example, by using an emotion estimation function with an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the voice analysis unit simplifies the analysis algorithm to perform a quick analysis. Furthermore, if the user is relaxed, the voice analysis unit can apply an algorithm that performs a detailed analysis. Furthermore, if the user is in a hurry, the voice analysis unit can apply an algorithm that prioritizes analysis of the most important information. This improves analysis accuracy by adjusting the voice analysis algorithm based on the user's emotions.

[0081] The voice analysis unit learns specific voice patterns in real time to improve analysis accuracy. Learning in real time includes, for example, online learning and sequential learning. The voice analysis unit can, for example, learn specific keywords in a conversation in real time to improve analysis accuracy. The voice analysis unit can also learn the user's pronunciation habits in real time to improve analysis accuracy. Furthermore, the voice analysis unit can also learn background sounds and noise in real time to improve analysis accuracy. As a result, analysis accuracy is improved by learning specific voice patterns in real time.

[0082] The speech analysis unit has a multilingual analysis function to support different languages ​​and dialects. The multilingual analysis function includes, for example, a language model and a translation algorithm. The speech analysis unit can, for example, analyze speech data in different languages ​​and convert it into text. The speech analysis unit can also analyze differences in dialects and accents and convert it into accurate text. Furthermore, if a user speaks multiple languages, the speech analysis unit can perform analysis corresponding to each language. Thus, the multilingual analysis function makes it possible to support different languages ​​and dialects.

[0083] The voice analysis unit applies advanced filtering technology to remove background sounds and noise. Examples of advanced filtering technology include noise canceling and spectral subtraction. The voice analysis unit can, for example, remove background sounds during conversation in real time to analyze clear voice data. The voice analysis unit can also extract and analyze important sounds even in noisy environments. Furthermore, the voice analysis unit can filter noise in specific frequency bands to improve analysis accuracy. As a result, analysis accuracy is improved by applying advanced filtering technology.

[0084] The voice analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. Emotion estimation is achieved, for example, by using an emotion estimation function with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the voice analysis unit provides a simple, highly visible display method. If the user is relaxed, the voice analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the voice analysis unit can also provide a display method that focuses on the main points. This improves visibility by adjusting the display method of the analysis results based on the user's emotions.

[0085] The voice analysis unit improves the accuracy of analysis by referring to the user's past conversation history. Past conversation history includes, for example, recorded data, text logs, etc. The voice analysis unit can, for example, prioritize the analysis of specific keywords or phrases based on the user's past conversation history. The voice analysis unit can also learn pronunciation habits from the user's past conversation history to improve the accuracy of analysis. Furthermore, the voice analysis unit can analyze the user's past conversation history and apply an optimal analysis algorithm. In this way, the analysis accuracy is improved by referring to the past conversation history.

[0086] The voice analysis unit adjusts the level of detail of the analysis results according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and advanced. For example, if the user has expertise, the voice analysis unit can provide detailed analysis results. Furthermore, if the user is a beginner, the voice analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the voice analysis unit can customize the display method of the analysis results according to the user's level of expertise. In this way, appropriate information can be provided by adjusting the level of detail of the analysis results according to the user's level of expertise.

[0087] The audio analysis unit optimizes the analysis algorithm based on the user's current environmental information. The current environmental information includes, for example, temperature, humidity, and noise level. For example, if the user is in a quiet environment, the audio analysis unit can apply an algorithm that performs detailed analysis. Also, if the user is in a noisy environment, the audio analysis unit can apply an algorithm that removes noise. Furthermore, the audio analysis unit can select the optimal analysis algorithm based on the user's current environmental information. This allows the analysis algorithm to be optimized by taking the current environmental information into consideration.

[0088] The text conversion unit estimates the user's emotion and adjusts the expression method of the text conversion based on the estimated user emotion. Emotion estimation is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the text conversion unit performs simple, highly visible text conversion. Furthermore, if the user is relaxed, the text conversion unit can also perform text conversion that includes detailed information. Furthermore, if the user is in a hurry, the text conversion unit can also perform text conversion that focuses on the main points. This improves visibility by adjusting the expression method of the text conversion based on the user's emotion.

[0089] The text conversion unit has a function of highlighting specific keywords and phrases. Specific keywords and phrases include, for example, importance and frequency. The text conversion unit can, for example, highlight important keywords in a conversation. The text conversion unit can also highlight specific phrases set by the user. Furthermore, the text conversion unit can automatically highlight important information during text conversion. This allows important information to be emphasized by highlighting specific keywords and phrases.

[0090] The text conversion unit has a function for automatically translating into different languages. The automatic translation includes, for example, a translation algorithm, supported languages, and the like. The text conversion unit can, for example, translate the voice of a conversation into different languages ​​in real time and convert it into text. In addition, if a user speaks multiple languages, the text conversion unit can also perform text conversion corresponding to each language. Furthermore, the text conversion unit can analyze voice data in different languages ​​and convert it into accurate text. This enables multilingual support by automatically translating into different languages.

[0091] The text conversion unit improves conversion accuracy by referring to the user's past text conversion results. Past text conversion results include, for example, conversion logs and user feedback. The text conversion unit can, for example, preferentially convert specific keywords or phrases based on the user's past text conversion results. The text conversion unit can also learn the user's pronunciation habits from the user's past text conversion results and improve conversion accuracy. Furthermore, the text conversion unit can analyze the user's past text conversion results and apply the optimal conversion algorithm. In this way, conversion accuracy is improved by referring to the past text conversion results.

[0092] The text conversion unit estimates the user's emotion and adjusts the length of the text based on the estimated user emotion. The emotion estimation is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the text conversion unit may provide short, to-the-point text when the user is nervous. The text conversion unit may also provide longer text with detailed information when the user is relaxed. Furthermore, the text conversion unit may provide short, quickly understandable text when the user is in a hurry. This improves legibility by adjusting the length of the text based on the user's emotion.

[0093] The text conversion unit determines the priority of text based on the time of submission by the user. The submission time includes, for example, the submission date and time, the submission deadline, etc. For example, if the user is in a hurry, the text conversion unit can convert text submitted earlier with priority. Furthermore, if the user is relaxed, the text conversion unit can convert all text equally regardless of the time of submission. Furthermore, the text conversion unit can convert the most important text with priority based on the time of submission by the user. In this way, by determining the priority of text based on the time of submission, important text can be converted with priority.

[0094] The text conversion unit adjusts the order of text based on the user's relevance. Relevance includes, for example, the degree of content match and relevance score. The text conversion unit can, for example, preferentially convert text that the user considers important. The text conversion unit can also preferentially convert highly relevant text based on the user's past behavior history. Furthermore, the text conversion unit can also preferentially convert the most relevant text based on the user's current situation. In this way, important information can be preferentially displayed by adjusting the order of text based on relevance.

[0095] The text conversion unit adjusts the use of technical terms in the text according to the user's level of expertise. Technical terms include, for example, technical terms, industry jargon, etc. For example, if the user has specialized knowledge, the text conversion unit can provide text that uses a lot of technical terms. In addition, if the user is a beginner, the text conversion unit can provide concise and easy-to-understand text. Furthermore, the text conversion unit can adjust the use of technical terms in the text according to the user's level of expertise. As a result, appropriate information can be provided by adjusting the use of technical terms according to the user's level of expertise.

[0096] The text display unit estimates the user's emotion and adjusts the font size and color of the displayed text based on the estimated user emotion. Emotion estimation is achieved, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the text display unit may use a font with a subdued color tone to reduce visual stress. Alternatively, if the user is relaxed, the text display unit may use a font with a bright color tone to provide visual enjoyment. Furthermore, if the user is in a hurry, the text display unit may use a font size with high visibility to support quick understanding. This improves visibility by adjusting the font size and color based on the user's emotion.

[0097] The text display unit customizes the display method based on the user's visual acuity information. The visual acuity information includes, for example, the results of a visual acuity test, a diagnosis by an ophthalmologist, etc. For example, if the user has poor visual acuity, the text display unit can display text in a large font size. Alternatively, if the user has good visual acuity, the text display unit can display text in a standard font size. Furthermore, the text display unit can select the optimal font size and display method based on the user's visual acuity information. This allows the optimal display method to be provided by taking the visual acuity information into consideration.

[0098] The text display unit has a function of automatically adjusting the display brightness according to the user's current ambient light. The ambient light includes, for example, a light sensor, an illuminance meter, etc. For example, the text display unit can automatically increase the display brightness when the user is in a bright environment. The text display unit can also automatically decrease the display brightness when the user is in a dark environment. Furthermore, the text display unit can automatically adjust the display brightness to an optimum level based on the user's current ambient light. This improves visibility by automatically adjusting the brightness according to the ambient light.

[0099] The text display unit selects the optimal display method by referring to the user's past display settings. The past display settings include, for example, a user profile and a setting log. The text display unit can, for example, select the optimal font size and color based on the user's past display settings. The text display unit can also preferentially apply a specific display method from the user's past display settings. Furthermore, the text display unit can analyze the user's past display settings and suggest the optimal display method. In this way, the optimal display method can be provided by referring to the past display settings.

[0100] The text display unit estimates the user's emotion and adjusts the position of the displayed text based on the estimated user emotion. Emotion estimation is achieved, for example, by using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the text display unit displays the text in a highly visible position. Furthermore, if the user is relaxed, the text display unit can also display the text in a visually comfortable position. Furthermore, if the user is in a hurry, the text display unit can display the text in a position where it can be quickly confirmed. In this way, visibility is improved by adjusting the position of the text based on the user's emotion.

[0101] The text display unit selects the most appropriate display method based on the user's device information. The device information includes, for example, the device type, specifications, OS version, etc. For example, if the user is using a smartphone, the text display unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the text display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the text display unit can provide a simple and highly visible display method. This allows the optimal display method to be provided by taking device information into consideration.

[0102] The text display unit supports multiple languages ​​in display content according to the user's language setting. The language setting includes, for example, the user's preferred language and the device's language setting. The text display unit can automatically set the display content based on, for example, the user's device's language setting. The text display unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the text display unit can provide the display content in that language. This improves user convenience by supporting multiple languages ​​according to the language setting.

[0103] The text display unit customizes the display method by reflecting user feedback. The feedback includes, for example, user reviews, survey results, etc. The text display unit can adjust the display method based on, for example, the feedback provided by the user. The text display unit can also preferentially apply a specific display method based on the user feedback. Furthermore, the text display unit can analyze the user feedback and suggest the optimal display method. In this way, the optimal display method can be provided by reflecting the feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned voice collection unit, voice analysis unit, text conversion unit, and text display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice collection unit collects voice using the microphone 38B of the smart device 14, and the collected voice data is analyzed by the specific processing unit 290 of the data processing device 12. The voice analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected voice data. The text conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the analyzed voice data into text. The text display unit is realized by the display 40A of the smart device 14 and displays the converted text. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned voice collection unit, voice analysis unit, text conversion unit, and text display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice collection unit collects voice using the microphone 238 of the smart glasses 214, and the collected voice data is analyzed by the specific processing unit 290 of the data processing device 12. The voice analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected voice data. The text conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the analyzed voice data into text. The text display unit is realized by the display of the smart glasses 214 and displays the converted text. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned voice collection unit, voice analysis unit, text conversion unit, and text display 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 collection unit collects voice using the microphone 238 of the headset type terminal 314, and the collected voice data is analyzed by the specific processing unit 290 of the data processing device 12. The voice analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected voice data. The text conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the analyzed voice data into text. The text display unit is realized by the display 343 of the headset type terminal 314 and displays the converted text. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice collection unit, voice analysis unit, text conversion unit, and text display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice collection unit collects voice using the microphone 238 of the robot 414, and the collected voice data is analyzed by the specific processing unit 290 of the data processing device 12. The voice analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected voice data. The text conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the analyzed voice data into text. The text display unit is realized by the display of the robot 414 and displays the converted text.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The voice collection unit can also monitor the user's heart rate and adjust the timing of voice collection based on heart rate fluctuations. For example, if the user's heart rate suddenly rises, the timing of voice collection can be delayed to give the user time to calm down. If the heart rate is stable, the timing of voice collection can be advanced to promote smooth conversation. Furthermore, if the heart rate drops, the timing of voice collection can be immediately performed to support prompt communication. This makes it possible to optimize the timing of voice collection according to the user's physiological state.

[0106] The voice analysis unit can also improve analysis accuracy by referring to the user's past conversation history. For example, it can prioritize analysis of specific keywords or phrases based on the user's past conversation history. It can also learn pronunciation habits from the user's past conversation history to improve analysis accuracy. It can also analyze the user's past conversation history and apply the optimal analysis algorithm. In this way, analysis accuracy can be improved by referring to the past conversation history.

[0107] The text display unit can also customize the display method based on the user's visual acuity information. For example, if the user has poor visual acuity, it can display text in a large font size. On the other hand, if the user has good visual acuity, it can display text in a standard font size. Furthermore, it can select the optimal font size and display method based on the user's visual acuity information. This allows the optimal display method to be provided by taking visual acuity information into consideration.

[0108] The voice collection unit can also prioritize collection of highly relevant voices based on the user's geographical location information. For example, if the user is in a specific location, voices related to that location can be collected preferentially. Also, if the user is traveling, voices related to the destination can be collected preferentially. Furthermore, if the user is participating in a specific event, voices related to the event can be collected preferentially. In this way, highly relevant voices can be collected preferentially by taking geographical location information into consideration.

[0109] The voice analysis unit can also adjust the level of detail in the analysis results according to the user's level of expertise. For example, if the user has expertise, it can provide detailed analysis results. On the other hand, if the user is a beginner, it can provide concise and easy-to-understand analysis results. Furthermore, it can customize the way in which the analysis results are displayed according to the user's level of expertise. This allows the user to provide appropriate information by adjusting the level of detail in the analysis results according to the user's level of expertise.

[0110] The voice collection unit can also estimate the user's emotions and determine the priority of voices to be collected based on the estimated user's emotions. For example, if the user is nervous, important voices can be collected with priority. Also, if the user is relaxed, all voices can be collected equally. Furthermore, if the user is in a hurry, important voices can be collected with priority in a short time. In this way, by determining the priority of voices based on the user's emotions, important voices can be collected with priority.

[0111] The voice analysis unit can also estimate the user's emotions and adjust the voice analysis algorithm based on the estimated user emotions. For example, if the user is nervous, the analysis algorithm can be simplified to perform a quick analysis. Alternatively, if the user is relaxed, an algorithm can be applied that performs a detailed analysis. Furthermore, if the user is in a hurry, an algorithm can be applied that prioritizes analysis of the most important information. In this way, adjusting the voice analysis algorithm based on the user's emotions improves analysis accuracy.

[0112] The text conversion unit can also estimate the user's emotions and adjust the expression method of the text conversion based on the estimated user's emotions. For example, if the user is nervous, simple text conversion with high visibility can be performed. If the user is relaxed, text conversion including detailed information can be performed. Furthermore, if the user is in a hurry, text conversion that focuses on the main points can be performed. In this way, visibility can be improved by adjusting the expression method of the text conversion based on the user's emotions.

[0113] The text display unit can also estimate the user's emotions and adjust the font size and color of the displayed text based on the estimated user's emotions. For example, if the user is nervous, a font with a calm color tone can be used to reduce visual stress. If the user is relaxed, a font with a bright color tone can be used to provide visual enjoyment. Furthermore, if the user is in a hurry, a font size with high visibility can be used to support quick understanding. Thus, adjusting the font size and color based on the user's emotions improves visibility.

[0114] The text display unit may also have a function for automatically adjusting the display brightness according to the user's current ambient light. For example, if the user is in a bright environment, the display brightness can be automatically increased. Also, if the user is in a dark environment, the display brightness can be automatically decreased. Furthermore, the display brightness can be automatically adjusted to an optimum value based on the user's current ambient light. This improves visibility by automatically adjusting the brightness according to the ambient light.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The audio collection unit collects audio. For example, the audio data is collected by using a microphone to pick up the voice of the person on the other end of a conversation. The audio collection unit may also have a filtering function to remove ambient noise. For example, a filter can be used to remove noise such as wind noise and traffic sounds. Step 2: The voice analysis unit analyzes the collected voice data. For example, it uses voice recognition technology to analyze spoken words and converts the content into text. For example, it converts the voice data of "Hello" into the text "Hello." Step 3: The text conversion unit converts the analyzed voice data into text. For example, highly accurate text conversion can be achieved using voice recognition technology. Step 4: The text display unit displays the converted text on the eyeglasses. For example, by displaying the text in the user's field of vision, a hearing-impaired person can check the content of the conversation in real time.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 collection unit that collects voice; a voice analysis unit that analyzes the voice collected by the voice collection unit; a text conversion unit that converts the voice analyzed by the voice analysis unit into text; a text display unit that displays the text converted by the text conversion unit on the eyeglass-type device. A system characterized by:

2. The sound collection unit Equipped with filtering function to remove ambient noise 2. The system of claim 1.

3. The text display unit Ability to adjust font size, color, or display position of displayed text 2. The system of claim 1.

4. The voice analysis unit Has the ability to learn specific voice patterns 2. The system of claim 1.

5. The text display unit Designed to improve battery life and durability of eyeglasses 2. The system of claim 1.

6. The sound collection unit The user's emotion is estimated, and the timing of voice collection is adjusted based on the estimated user's emotion.

2. The system of claim 1.

7. The sound collection unit Equipped with a function to emphasize and collect specific frequency bands 2. The system of claim 1.

8. The sound collection unit Has the ability to dynamically adjust the collection range 2. The system of claim 1.

Images