System

Abstract

To provide a system capable of helping elderly people with hearing loss to communicate smoothly.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a display unit, and a transmission unit. The collection unit collects voice data. The analysis unit analyzes the collected voice data and converts the same into a text. The display unit displays the data converted into text. The transmission unit transmits the analyzed voice data to the inner ear via bone conduction.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, elderly people with hearing loss have difficulty communicating smoothly, and the high cost of hearing aids limits their use.

[0005] The system according to the embodiment aims to support elderly people with hearing loss so that they can have smooth conversations. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a display unit, and a transmission unit. The collection unit collects voice data. The analysis unit analyzes the collected voice data and converts it into text. The display unit displays the converted text data on the lens. The transmission unit transmits the analyzed voice data to the inner ear via bone conduction. Effect of the Invention

[0007] The system according to the embodiment can assist elderly people with hearing loss to have smooth conversations. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The communication assist glasses system according to the embodiment of the present invention is a system that supports conversations of elderly people with hearing loss. This system is equipped with a voice recognition microphone function and AI in the bridge part, and has the function of converting the other person's voice into text and displaying it on the lens. In addition, the temple part that goes over the ear has a bone conduction function, so even elderly people who have difficulty hearing conversations can easily check the other person's words in text. This makes it possible to accommodate people who cannot afford expensive hearing aids or for whom hearing aids do not fit. Furthermore, these glasses also contribute to brain stimulation for the prevention of dementia, and are useful for suppressing the progression of hearing loss in the elderly. For example, a voice recognition microphone installed in the bridge part picks up the other person's voice. This voice data is analyzed by AI and converted into text. For example, if the other person says "Hello," the voice is picked up by the microphone and the AI ​​converts it into text as "Hello." This text is displayed on the lens, so the user can check the other person's words in text. Next, the bone conduction function installed in the temple part transmits the voice to the inner ear through the bone. This allows the user to hear the voice without blocking their ears. For example, the voice of the other person is converted to text as "Hello" and the voice is transmitted to the user through bone conduction. This allows the user to check the other person's words in both text and audio. These glasses can also accommodate people who cannot afford expensive hearing aids or whose hearing aids do not fit. For example, these glasses are an easy and effective solution for people who cannot afford expensive hearing aids or who feel uncomfortable because hearing aids do not fit their ears. They also contribute to brain stimulation to prevent dementia. For example, checking the other person's words in text activates the brain, which helps prevent dementia. Furthermore, these glasses are also useful for preventing the progression of hearing loss in the elderly. For example, checking the other person's words in text increases the opportunities for conversation and increases auditory stimulation, which is expected to have the effect of suppressing the progression of hearing loss. As a result, the communication assist glasses system allows elderly people with hearing loss to check conversations in both text and audio.

[0029] The communication assist glasses system according to the embodiment includes a collection unit, an analysis unit, a display unit, and a transmission unit. The collection unit collects voice data. The collection unit collects voice data using, for example, a voice recognition microphone. The voice recognition microphone is a microphone with a directional microphone or a noise canceling function, and can eliminate ambient noise and collect clear voice data. For example, the collection unit collects voice from a specific direction using a directional microphone. The collection unit can also eliminate ambient noise using a noise canceling function to improve the collection accuracy of the voice data. Furthermore, the collection unit can collect voice data using multiple microphones and identify the position of the sound source. The analysis unit analyzes the voice data collected by the collection unit and converts it into text. The analysis unit converts the voice data into text using, for example, a voice recognition technology. The voice recognition technology is a technology that uses AI to analyze voice data and convert it into text data. For example, the analysis unit converts the voice data into text in real time using the voice recognition technology. The analysis unit can also adjust the accuracy of the text conversion depending on the content of the voice data. For example, the analysis unit can convert important conversation contents into text in detail and simplify unimportant contents. The display unit displays the data converted into text by the analysis unit on the lens. The display unit displays the text data using, for example, smart glasses or an augmented reality (AR) lens. Smart glasses and AR lenses are devices that display text data by overlaying it on the user's field of vision, and the user can check the text data without moving his or her line of sight. For example, the display unit displays the text data in real time, allowing the user to check it immediately. The display unit can also adjust the font size and color of the text data to improve visibility. The transmission unit transmits the voice data analyzed by the analysis unit to the inner ear through bone conduction. The transmission unit transmits the voice data to the inner ear using, for example, a bone conduction headset or a bone conduction earphone. Bone conduction technology is a technology that transmits voice to the inner ear through bone, and allows the user to hear voice without blocking their ears. For example, the transmission unit transmits the voice data to the inner ear using a bone conduction headset in real time.The transmission unit can also adjust the strength of bone conduction according to the content of the voice data. For example, the transmission unit can emphasize and transmit important conversation content, and transmit unimportant content at normal strength. As a result, the communication assist glasses system according to the embodiment allows hearing-impaired elderly people to check the conversation in both text and voice. For example, the collection unit collects voice data using a voice recognition microphone, and the analysis unit converts the voice data into text using voice recognition technology. The display unit displays the text data on smart glasses or AR lenses, and the transmission unit transmits the voice data to the inner ear using bone conduction technology. As a result, the user can check the other person's speech in both text and voice. Furthermore, a part or all of the above-mentioned processing in the collection unit, analysis unit, display unit, and transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the voice data collected using the voice recognition microphone to the generation AI, and cause the generation AI to execute analysis of the voice data. The analysis unit converts the voice data into text using the generation AI, and the display unit displays the text data using the generation AI. The transmission unit can transmit the voice data to the inner ear using the generation AI.

[0030] The collection unit collects voice data. The collection unit collects voice data using, for example, a voice recognition microphone. The voice recognition microphone is a microphone equipped with a directional microphone and a noise canceling function, and can eliminate surrounding noise and collect clear voice data. Specifically, a directional microphone intensively collects voice from a specific direction, so that the voice of the person in the conversation can be clearly captured. The noise canceling function improves the quality of the voice data by analyzing the surrounding noise in real time and removing unnecessary sounds. Furthermore, the collection unit can identify the position of the sound source by using multiple microphones. For example, by using a stereo microphone, it is possible to calculate the direction and distance of the sound and focus on a specific sound source. This allows the collection unit to collect highly accurate voice data even in a complex environment. In addition, the collection unit can adjust the collection frequency and sensitivity of the voice data, and can flexibly respond according to a specific situation or environment. For example, by using a high sensitivity mode in a quiet room and strengthening noise canceling in a noisy outdoor environment, it is possible to always collect optimal voice data. This allows the collection unit to provide clear and accurate voice data no matter what environment the user is in.

[0031] The analysis unit analyzes the voice data collected by the collection unit and converts it into text. The analysis unit converts the voice data into text using, for example, voice recognition technology. Voice recognition technology is a technology that uses AI to analyze voice data and convert it into text data. Specifically, voice recognition AI breaks down voice data into phonemes and words and combines them to generate sentences. For example, the analysis unit converts voice data into text in real time using voice recognition technology. This allows the user to instantly check the content of the conversation. The analysis unit can also adjust the accuracy of the text conversion depending on the content of the voice data. For example, important conversation content can be converted into text in detail and unimportant content can be simplified. Furthermore, the analysis unit can use a custom dictionary to recognize specific technical terms and proper nouns, and can perform voice recognition specialized for a specific industry or field. This allows the analysis unit to handle not only general conversations but also specialized conversations and technical discussions. Furthermore, the analysis unit can continuously improve the accuracy of the text conversion by learning past conversation data and adapting to the user's speaking style and accent. This allows the analysis unit to always provide highly accurate text data and support the user's communication.

[0032] The display unit displays the data converted into text by the analysis unit on the lens. The display unit displays the text data using, for example, smart glasses or an augmented reality (AR) lens. Smart glasses and AR lenses are devices that display text data by overlaying it on the user's field of vision, and the user can check the text data without moving his or her line of sight. Specifically, the display unit displays the text data in real time, allowing the user to check it immediately. For example, since the other person's remarks are displayed as text during a conversation, even a user who is hard of hearing or has a hearing impairment can understand the content of the conversation. The display unit can also adjust the font size and color of the text data to improve visibility. For example, by increasing the contrast of characters in a bright environment and adjusting the brightness of characters in a dark environment, it is possible to always provide optimal visibility. Furthermore, the display unit can customize the display content according to the user's preferences. For example, it is possible to provide a function to highlight specific keywords or phrases and a function to notify important information by a pop-up. This allows the display unit to support the user in quickly and reliably obtaining the information he or she needs.

[0033] The transmission unit transmits the voice data analyzed by the analysis unit to the inner ear through bone conduction. The transmission unit transmits the voice data to the inner ear using, for example, a bone conduction headset or a bone conduction earphone. Bone conduction technology is a technology that transmits voice to the inner ear through bone, and allows the user to hear the voice without blocking the ear. Specifically, a bone conduction headset transmits the voice directly to the inner ear by transmitting vibrations to the bones of the head and jaw. This allows the user to hear clear voice without blocking external sounds. For example, the transmission unit transmits the voice data to the inner ear in real time using a bone conduction headset. The transmission unit can also adjust the strength of bone conduction according to the content of the voice data. For example, important conversation content can be emphasized and transmitted, and unimportant content can be transmitted at normal strength. Furthermore, the transmission unit can customize the frequency and volume of the voice according to the user's hearing characteristics. This allows the user to enjoy conversation in an audio environment that is optimal for the user. Furthermore, the transmission unit can transmit multiple voice data simultaneously, allowing the user to check multiple conversations and voice information simultaneously. This allows the transmission unit to efficiently obtain various voice information and smoothly communicate.

[0034] The collection unit can collect voice data using a voice recognition microphone. The collection unit collects voice data using, for example, a voice recognition microphone. The voice recognition microphone is a microphone equipped with a directional microphone or a noise canceling function, and can eliminate surrounding noise and collect clear voice data. For example, the collection unit collects voice from a specific direction using a directional microphone. The collection unit can also eliminate surrounding noise using a noise canceling function to improve the collection accuracy of the voice data. Furthermore, the collection unit can also collect voice data using multiple microphones and identify the position of the sound source. As a result, the use of the voice recognition microphone improves the collection accuracy of the voice data. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the voice data collected using the voice recognition microphone to the generation AI and cause the generation AI to execute analysis of the voice data.

[0035] The analysis unit can analyze the collected voice data and convert it into text. The analysis unit converts the voice data into text using, for example, voice recognition technology. Voice recognition technology is a technology that uses AI to analyze voice data and convert it into text data. For example, the analysis unit converts the voice data into text in real time using voice recognition technology. The analysis unit can also adjust the accuracy of the text conversion depending on the content of the voice data. For example, the analysis unit can convert important conversation content into text in detail and simplify unimportant content. This allows the voice data to be converted into text, allowing visual confirmation. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected voice data to a generation AI and have the generation AI convert the voice data into text.

[0036] The display unit can display the text data on the lens. The display unit displays the text data using, for example, smart glasses or an augmented reality (AR) lens. Smart glasses and AR lenses are devices that display text data by overlaying it on the user's field of vision, and the user can check the text data without moving his or her line of sight. For example, the display unit displays the text data in real time, allowing the user to check it immediately. The display unit can also adjust the font size and color of the text data to improve visibility. This allows the text data to be visually checked by displaying it on the lens. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the text data to a generation AI and cause the generation AI to execute a process of displaying the data on the lens.

[0037] The transmission unit can transmit sound to the inner ear using a bone conduction function. The transmission unit transmits sound data to the inner ear using, for example, a bone conduction headset or a bone conduction earphone. Bone conduction technology is a technology that transmits sound to the inner ear through bone, and allows the user to hear the sound without blocking the ear. For example, the transmission unit transmits sound data to the inner ear in real time using a bone conduction headset. The transmission unit can also adjust the strength of bone conduction according to the content of the sound data. For example, the transmission unit can transmit important conversation content with emphasis and transmit unimportant content at normal strength. As a result, the user can hear the sound without blocking the ear by using the bone conduction function. A part or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input sound data to the generation AI and cause the generation AI to execute a process of transmitting the sound data to the inner ear through bone conduction.

[0038] The display unit can display the text data in real time. The display unit, for example, displays the text data in real time so that the user can check it immediately. Real-time display means that there is little delay time and the user can check the text data without interrupting the flow of conversation. For example, the display unit updates the text data in real time so that the user can always check the latest information. The display unit can also adjust the font size and color of the text data to improve visibility. As a result, the text data can be displayed in real time so that it can be checked immediately. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input the text data into the generation AI and cause the generation AI to execute a process of displaying the data in real time.

[0039] The transmission unit can transmit the voice to the inner ear in real time. The transmission unit, for example, transmits voice data to the inner ear in real time. Real-time transmission means that there is little delay time and the user can check the voice data without interrupting the flow of conversation. For example, the transmission unit updates the voice data in real time, allowing the user to always check the latest information. The transmission unit can also adjust the strength of bone conduction according to the content of the voice data. This allows the voice to be transmitted in real time, allowing it to be checked immediately. Some or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can input voice data to the generation AI and have the generation AI execute a process of transmitting the voice data to the inner ear in real time.

[0040] The collection unit can analyze the surrounding environmental sound and perform noise canceling to improve the collection accuracy of the voice data. The collection unit, for example, analyzes the surrounding environmental sound and performs noise canceling to improve the collection accuracy of the voice data. Noise canceling is performed using a technology such as active noise canceling or passive noise canceling. For example, the collection unit analyzes the surrounding noise in real time and applies noise canceling to collect clear voice data. The collection unit can also detect a sudden loud sound (for example, a car horn) and filter the voice data at that moment. Furthermore, the collection unit can learn the pattern of the surrounding environmental sound and automatically adjust the optimal noise canceling setting. This makes it possible to collect clear voice data by performing noise canceling. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the surrounding environmental sound data to the generation AI and cause the generation AI to apply noise canceling.

[0041] The collection unit can refer to the user's past conversation history and preferentially collect voice data based on a specific keyword. The collection unit, for example, refers to the user's past conversation history and preferentially collects voice data based on a specific keyword. The conversation history is stored, for example, in a database or cloud storage. For example, the collection unit preferentially collects keywords frequently used by the user so as not to miss important conversation content. The collection unit can also preferentially collect voice data related to a specific topic from the past conversation history. Furthermore, the collection unit can strengthen the collection of voice data and perform detailed analysis when the user utters a specific keyword. This makes it possible to refer to the past conversation history so as not to miss important conversation content. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's past conversation history data to the generation AI and cause the generation AI to perform preferential collection of voice data based on a specific keyword.

[0042] The collection unit can preferentially collect highly relevant voice data in consideration of the geographical location information of the user when collecting voice data. For example, the collection unit preferentially collects highly relevant voice data in consideration of the geographical location information of the user when collecting voice data. The geographical location information is acquired, for example, using GPS data or a location information service. For example, when the user is in a specific location, the collection unit preferentially collects voice data related to the location. In addition, when the user is moving, the collection unit can also preferentially collect voice data related to the destination. Furthermore, when the user is participating in a specific event, the collection unit can also preferentially collect voice data related to the event. In this way, by considering the geographical location information, highly relevant voice data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to the generation AI and cause the generation AI to perform preferential collection of highly relevant voice data.

[0043] The collection unit can analyze the social media activity of the user and collect related voice data when collecting the voice data. For example, the collection unit analyzes the social media activity of the user and collects related voice data when collecting the voice data. The social media activity is performed, for example, by analyzing the content of posts and the number of likes. For example, the collection unit collects voice data based on keywords frequently used by the user on social media. The collection unit can also analyze the content of posts on the user's social media and collect related voice data preferentially. Furthermore, the collection unit can also collect related voice data with reference to the content of posts by the user's social media followers and friends. In this way, the related voice data can be collected preferentially by analyzing the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the social media activity data of the user to the generation AI and cause the generation AI to collect related voice data.

[0044] The analysis unit can adjust the level of detail of the text based on the importance of the voice data during analysis. The analysis unit, for example, adjusts the level of detail of the text based on the importance of the voice data during analysis. The importance of the voice data is evaluated based on, for example, the contents of the conversation and the importance of the speaker. For example, the analysis unit converts important conversation contents into text in detail and simplifies unimportant contents. The analysis unit can also convert parts that are particularly emphasized in the conversation into text in detail. Furthermore, the analysis unit can also convert parts related to topics in which the user is particularly interested into text in detail. As a result, important information can be converted into text in detail by adjusting the level of detail of the text based on the importance of the voice data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the importance of the voice data to the generation AI and cause the generation AI to adjust the level of detail of the text.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the voice data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the voice data during analysis. The categories of voice data are classified into, for example, business conversation and daily conversation. For example, the analysis unit applies an algorithm that appropriately analyzes technical terms when the content of the conversation is business-related. In addition, the analysis unit can also apply an algorithm that emphasizes natural expressions when the content of the conversation is daily conversation. Furthermore, the analysis unit can also apply an algorithm that accurately analyzes medical terms when the content of the conversation is medical-related. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of the voice data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the voice data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of text conversion based on the collection time of the voice data during analysis. The analysis unit, for example, determines the priority of text conversion based on the collection time of the voice data during analysis. The collection time of the voice data is evaluated based on, for example, a time stamp or a collection date and time. For example, the analysis unit preferentially converts the latest voice data into text. The analysis unit can also preferentially convert voice data collected in a specific time period into text. Furthermore, the analysis unit can also preferentially convert voice data in a time period specified by the user into text. As a result, by determining the priority of text conversion based on the collection time of the voice data, the latest information can be preferentially converted into text. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the voice data to the generation AI and cause the generation AI to execute the determination of the priority of text conversion.

[0047] The analysis unit can adjust the order of text conversion based on the relevance of the voice data during analysis. The analysis unit, for example, adjusts the order of text conversion based on the relevance of the voice data during analysis. The relevance of the voice data is evaluated based on, for example, the similarity of the contents and the relationship between the speakers. For example, the analysis unit preferentially converts important conversation contents into text. The analysis unit can also preferentially convert highly relevant parts into text along the flow of the conversation. Furthermore, the analysis unit can also preferentially convert parts related to topics in which the user is interested into text. As a result, important information can be preferentially converted into text by adjusting the order of text conversion based on the relevance of the voice data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the voice data to the generation AI and cause the generation AI to adjust the order of text conversion.

[0048] The display unit can select the optimal display method by referring to the user's visual acuity information at the time of display. The display unit, for example, selects the optimal display method by referring to the user's visual acuity information at the time of display. The visual acuity information is acquired, for example, based on a visual acuity test result or a user's self-report. For example, the display unit adjusts the font size according to the user's visual acuity. The display unit can also adjust the contrast according to the user's visual acuity. Furthermore, the display unit can adjust the brightness of the display according to the user's visual acuity. In this way, the optimal display method can be provided by referring to the user's visual acuity information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's visual acuity information to the generation AI and cause the generation AI to select the optimal display method.

[0049] The display unit can apply different display formats according to the contents of the text when displayed. For example, the display unit applies different display formats according to the contents of the text when displayed. The contents of the text are classified based on, for example, importance or category. For example, the display unit displays important information in bold. The display unit can also perform color coding according to the flow of the conversation. Furthermore, the display unit can also highlight specific keywords. This makes it possible to provide an optimal display format according to the contents of the text. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the contents of the text to a generation AI and cause the generation AI to apply different display formats.

[0050] The display unit can select the optimal display method in consideration of the device information of the user at the time of display. For example, the display unit selects the optimal display method in consideration of the device information of the user at the time of display. The device information is acquired, for example, based on the type of device and the screen size. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the display unit can also provide a simple and highly visible display method. This makes it possible to provide the optimal display method by considering the device information of the user. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the device information of the user to the generation AI and cause the generation AI to select the optimal display method.

[0051] The display unit can select the optimal display method by referring to the user's past display history at the time of display. For example, the display unit selects the optimal display method by referring to the user's past display history at the time of display. The display history is stored in, for example, a database or cloud storage. For example, the display unit preferentially provides a display format that the user has used in the past. The display unit can also suggest optimal font sizes and colors from the user's past display history. Furthermore, the display unit can analyze the user's past display history and suggest optimal display timing. In this way, the optimal display method can be provided by referring to the user's past display history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's past display history data to the generation AI and cause the generation AI to select the optimal display method.

[0052] The transmission unit can select the optimal transmission method by referring to the user's hearing information at the time of transmission. For example, the transmission unit selects the optimal transmission method by referring to the user's hearing information at the time of transmission. The hearing information is acquired, for example, based on a hearing test result or a user's self-report. For example, the transmission unit adjusts the strength of bone conduction according to the user's hearing. The transmission unit can also adjust the frequency of the sound according to the user's hearing. Furthermore, the transmission unit can also adjust the volume of the sound according to the user's hearing. This makes it possible to optimally transmit sound by referring to the user's hearing information. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can input the user's hearing information to the generation AI and cause the generation AI to select the optimal transmission method.

[0053] The transmission unit can apply different transmission formats depending on the contents of the voice data at the time of transmission. For example, the transmission unit applies different transmission formats depending on the contents of the voice data at the time of transmission. The contents of the voice data are classified based on, for example, importance or category. For example, the transmission unit emphasizes and transmits important information. The transmission unit can also perform natural transmission along the flow of the conversation. Furthermore, the transmission unit can also emphasize and transmit specific keywords. This makes it possible to provide an optimal transmission format depending on the contents of the voice data. Part or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the contents of the voice data to the generation AI and cause the generation AI to apply different transmission formats.

[0054] The transmission unit can select the optimal transmission method in consideration of the geographical location information of the user at the time of transmission. For example, the transmission unit selects the optimal transmission method in consideration of the geographical location information of the user at the time of transmission. The geographical location information is acquired, for example, using GPS data or a location information service. For example, when the user is in a specific location, the transmission unit preferentially transmits voice data related to the location. In addition, when the user is moving, the transmission unit can also preferentially transmit voice data related to the destination. Furthermore, when the user is participating in a specific event, the transmission unit can also preferentially transmit voice data related to the event. This makes it possible to optimally transmit voice by considering the geographical location information of the user. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select the optimal transmission method.

[0055] The transmission unit can select the optimal transmission method by referring to the user's past transmission history at the time of transmission. For example, the transmission unit selects the optimal transmission method by referring to the user's past transmission history at the time of transmission. The transmission history is stored in, for example, a database or cloud storage. For example, the transmission unit preferentially provides a transmission format that the user has used in the past. The transmission unit can also suggest the optimal transmission method from the user's past transmission history. Furthermore, the transmission unit can analyze the user's past transmission history and suggest the optimal transmission timing. This makes it possible to perform optimal voice transmission by referring to the user's past transmission history. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI or may be performed without using AI. For example, the transmission unit can input the user's past transmission history data to the generation AI and cause the generation AI to select the optimal transmission method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The communication assist glasses system may further include a translation unit. The translation unit translates the voice data collected by the collection unit into another language in real time. For example, when the collection unit collects Japanese voice data, the translation unit translates the voice data into English, and the analysis unit converts the translated voice data into text. This facilitates communication between users who speak different languages. The translation unit may also automatically select a translation language based on the user's language settings. Furthermore, the translation unit may also include a dictionary function for appropriately translating technical terms and slang. This allows the user to smoothly converse with a person who speaks a different language.

[0058] The collection unit can learn the user's tone of voice and speaking patterns, and collect voice data that is individually optimized. For example, if the user speaks quickly, the collection unit collects voice data that matches that speed. Also, if the user speaks in a low voice, the collection unit can collect voice data that matches that range. Furthermore, the collection unit can learn the tone of voice used when the user expresses a particular emotion, and collect voice data according to that emotion. This makes it possible to collect voice data that is optimized for the individual speaking style of the user.

[0059] When analyzing the voice data, the analysis unit can improve the analysis accuracy by referring to the user's past conversation history. For example, the analysis unit preferentially analyzes phrases and words that the user has frequently used in the past. The analysis unit can also learn the user's speaking patterns from the past conversation history and improve the analysis accuracy based on the patterns. Furthermore, the analysis unit can also preferentially analyze voice data related to a specific topic based on the past conversation history. This makes it possible to improve the analysis accuracy by utilizing the user's past conversation history.

[0060] The display unit can automatically adjust the display method of the text data based on the user's visual acuity information. For example, the display unit increases the font size when the user's visual acuity is deteriorating. The display unit can also adjust the contrast according to the user's visual acuity to improve visibility. Furthermore, the display unit can adjust the display position of the text data based on the user's visual acuity information. This makes it possible to provide a display method optimized for the user's visual acuity.

[0061] The transmission unit can automatically adjust the bone conduction strength according to the content of the audio data. For example, when the content of the conversation is important, the bone conduction strength is increased for transmission. Also, when the content of the conversation is everyday, the bone conduction strength can be set to normal. Furthermore, when the content of the audio data is urgent, the bone conduction strength can be maximized for transmission. This allows audio to be transmitted at the optimal bone conduction strength according to the content of the audio data.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects voice data. The collection unit collects voice data using, for example, a voice recognition microphone. The voice recognition microphone is a microphone equipped with a directional microphone and a noise canceling function, and can eliminate ambient noise and collect clear voice data. For example, the collection unit uses a directional microphone to collect voice from a specific direction. The collection unit can also eliminate ambient noise using a noise canceling function to improve the accuracy of collecting voice data. Furthermore, the collection unit can collect voice data using multiple microphones and identify the position of the sound source. Step 2: The analysis unit analyzes the voice data collected by the collection unit and converts it into text. The analysis unit converts the voice data into text, for example, using voice recognition technology. Voice recognition technology is a technology that uses AI to analyze voice data and convert it into text data. For example, the analysis unit converts the voice data into text in real time using voice recognition technology. The analysis unit can also adjust the accuracy of the conversion into text depending on the content of the voice data. For example, the analysis unit can convert important conversation content into text in detail and simplify unimportant content. Step 3: The display unit displays the data converted to text by the analysis unit on the lens. The display unit displays the text data using, for example, smart glasses or an augmented reality (AR) lens. Smart glasses and AR lenses are devices that display text data by overlaying it on the user's field of vision, allowing the user to check the text data without moving their line of sight. For example, the display unit displays the text data in real time, allowing the user to check it immediately. The display unit can also adjust the font size and color of the text data to improve visibility. Step 4: The transmission unit transmits the voice data analyzed by the analysis unit to the inner ear through bone conduction. The transmission unit transmits the voice data to the inner ear using, for example, a bone conduction headset or bone conduction earphones. Bone conduction technology is a technology that transmits voice to the inner ear through bones, allowing you to hear the voice without blocking your ears. For example, the transmission unit transmits the voice data to the inner ear in real time using a bone conduction headset. The transmission unit can also adjust the strength of bone conduction depending on the content of the voice data. For example, the transmission unit can transmit important conversation content with emphasis and transmit unimportant content at normal strength.

[0064] (Example 2) The communication assist glasses system according to the embodiment of the present invention is a system that supports conversations of elderly people with hearing loss. This system is equipped with a voice recognition microphone function and AI in the bridge part, and has the function of converting the other person's voice into text and displaying it on the lens. In addition, the temple part that goes over the ear has a bone conduction function, so even elderly people who have difficulty hearing conversations can easily check the other person's words in text. This makes it possible to accommodate people who cannot afford expensive hearing aids or for whom hearing aids do not fit. Furthermore, these glasses also contribute to brain stimulation for the prevention of dementia, and are useful for suppressing the progression of hearing loss in the elderly. For example, a voice recognition microphone installed in the bridge part picks up the other person's voice. This voice data is analyzed by AI and converted into text. For example, if the other person says "Hello," the voice is picked up by the microphone and the AI ​​converts it into text as "Hello." This text is displayed on the lens, so the user can check the other person's words in text. Next, the bone conduction function installed in the temple part transmits the voice to the inner ear through the bone. This allows the user to hear the voice without blocking their ears. For example, the voice of the other person is converted to text as "Hello" and the voice is transmitted to the user through bone conduction. This allows the user to check the other person's words in both text and audio. These glasses can also accommodate people who cannot afford expensive hearing aids or whose hearing aids do not fit. For example, these glasses are an easy and effective solution for people who cannot afford expensive hearing aids or who feel uncomfortable because hearing aids do not fit their ears. They also contribute to brain stimulation to prevent dementia. For example, checking the other person's words in text activates the brain, which helps prevent dementia. Furthermore, these glasses are also useful for preventing the progression of hearing loss in the elderly. For example, checking the other person's words in text increases the opportunities for conversation and increases auditory stimulation, which is expected to have the effect of suppressing the progression of hearing loss. As a result, the communication assist glasses system allows elderly people with hearing loss to check conversations in both text and audio.

[0065] The communication assist glasses system according to the embodiment includes a collection unit, an analysis unit, a display unit, and a transmission unit. The collection unit collects voice data. The collection unit collects voice data using, for example, a voice recognition microphone. The voice recognition microphone is a microphone with a directional microphone or a noise canceling function, and can eliminate ambient noise and collect clear voice data. For example, the collection unit collects voice from a specific direction using a directional microphone. The collection unit can also eliminate ambient noise using a noise canceling function to improve the collection accuracy of the voice data. Furthermore, the collection unit can collect voice data using multiple microphones and identify the position of the sound source. The analysis unit analyzes the voice data collected by the collection unit and converts it into text. The analysis unit converts the voice data into text using, for example, a voice recognition technology. The voice recognition technology is a technology that uses AI to analyze voice data and convert it into text data. For example, the analysis unit converts the voice data into text in real time using the voice recognition technology. The analysis unit can also adjust the accuracy of the text conversion depending on the content of the voice data. For example, the analysis unit can convert important conversation contents into text in detail and simplify unimportant contents. The display unit displays the data converted into text by the analysis unit on the lens. The display unit displays the text data using, for example, smart glasses or an augmented reality (AR) lens. Smart glasses and AR lenses are devices that display text data by overlaying it on the user's field of vision, and the user can check the text data without moving his or her line of sight. For example, the display unit displays the text data in real time, allowing the user to check it immediately. The display unit can also adjust the font size and color of the text data to improve visibility. The transmission unit transmits the voice data analyzed by the analysis unit to the inner ear through bone conduction. The transmission unit transmits the voice data to the inner ear using, for example, a bone conduction headset or a bone conduction earphone. Bone conduction technology is a technology that transmits voice to the inner ear through bone, and allows the user to hear voice without blocking their ears. For example, the transmission unit transmits the voice data to the inner ear using a bone conduction headset in real time.The transmission unit can also adjust the strength of bone conduction according to the content of the voice data. For example, the transmission unit can emphasize and transmit important conversation content, and transmit unimportant content at normal strength. As a result, the communication assist glasses system according to the embodiment allows hearing-impaired elderly people to check the conversation in both text and voice. For example, the collection unit collects voice data using a voice recognition microphone, and the analysis unit converts the voice data into text using voice recognition technology. The display unit displays the text data on smart glasses or AR lenses, and the transmission unit transmits the voice data to the inner ear using bone conduction technology. As a result, the user can check the other person's speech in both text and voice. Furthermore, a part or all of the above-mentioned processing in the collection unit, analysis unit, display unit, and transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the voice data collected using the voice recognition microphone to the generation AI, and cause the generation AI to execute analysis of the voice data. The analysis unit converts the voice data into text using the generation AI, and the display unit displays the text data using the generation AI. The transmission unit can transmit the voice data to the inner ear using the generation AI.

[0066] The collection unit collects voice data. The collection unit collects voice data using, for example, a voice recognition microphone. The voice recognition microphone is a microphone equipped with a directional microphone and a noise canceling function, and can eliminate surrounding noise and collect clear voice data. Specifically, a directional microphone intensively collects voice from a specific direction, so that the voice of the person in the conversation can be clearly captured. The noise canceling function improves the quality of the voice data by analyzing the surrounding noise in real time and removing unnecessary sounds. Furthermore, the collection unit can identify the position of the sound source by using multiple microphones. For example, by using a stereo microphone, it is possible to calculate the direction and distance of the sound and focus on a specific sound source. This allows the collection unit to collect highly accurate voice data even in a complex environment. In addition, the collection unit can adjust the collection frequency and sensitivity of the voice data, and can flexibly respond according to a specific situation or environment. For example, by using a high sensitivity mode in a quiet room and strengthening noise canceling in a noisy outdoor environment, it is possible to always collect optimal voice data. This allows the collection unit to provide clear and accurate voice data no matter what environment the user is in.

[0067] The analysis unit analyzes the voice data collected by the collection unit and converts it into text. The analysis unit converts the voice data into text using, for example, voice recognition technology. Voice recognition technology is a technology that uses AI to analyze voice data and convert it into text data. Specifically, voice recognition AI breaks down voice data into phonemes and words and combines them to generate sentences. For example, the analysis unit converts voice data into text in real time using voice recognition technology. This allows the user to instantly check the content of the conversation. The analysis unit can also adjust the accuracy of the text conversion depending on the content of the voice data. For example, important conversation content can be converted into text in detail and unimportant content can be simplified. Furthermore, the analysis unit can use a custom dictionary to recognize specific technical terms and proper nouns, and can perform voice recognition specialized for a specific industry or field. This allows the analysis unit to handle not only general conversations but also specialized conversations and technical discussions. Furthermore, the analysis unit can continuously improve the accuracy of the text conversion by learning past conversation data and adapting to the user's speaking style and accent. This allows the analysis unit to always provide highly accurate text data and support the user's communication.

[0068] The display unit displays the data converted into text by the analysis unit on the lens. The display unit displays the text data using, for example, smart glasses or an augmented reality (AR) lens. Smart glasses and AR lenses are devices that display text data by overlaying it on the user's field of vision, and the user can check the text data without moving his or her line of sight. Specifically, the display unit displays the text data in real time, allowing the user to check it immediately. For example, since the other person's remarks are displayed as text during a conversation, even a user who is hard of hearing or has a hearing impairment can understand the content of the conversation. The display unit can also adjust the font size and color of the text data to improve visibility. For example, by increasing the contrast of characters in a bright environment and adjusting the brightness of characters in a dark environment, it is possible to always provide optimal visibility. Furthermore, the display unit can customize the display content according to the user's preferences. For example, it is possible to provide a function to highlight specific keywords or phrases and a function to notify important information by a pop-up. This allows the display unit to support the user in quickly and reliably obtaining the information he or she needs.

[0069] The transmission unit transmits the voice data analyzed by the analysis unit to the inner ear through bone conduction. The transmission unit transmits the voice data to the inner ear using, for example, a bone conduction headset or a bone conduction earphone. Bone conduction technology is a technology that transmits voice to the inner ear through bone, and allows the user to hear the voice without blocking the ear. Specifically, a bone conduction headset transmits the voice directly to the inner ear by transmitting vibrations to the bones of the head and jaw. This allows the user to hear clear voice without blocking external sounds. For example, the transmission unit transmits the voice data to the inner ear in real time using a bone conduction headset. The transmission unit can also adjust the strength of bone conduction according to the content of the voice data. For example, important conversation content can be emphasized and transmitted, and unimportant content can be transmitted at normal strength. Furthermore, the transmission unit can customize the frequency and volume of the voice according to the user's hearing characteristics. This allows the user to enjoy conversation in an audio environment that is optimal for the user. Furthermore, the transmission unit can transmit multiple voice data simultaneously, allowing the user to check multiple conversations and voice information simultaneously. This allows the transmission unit to efficiently obtain various voice information and smoothly communicate.

[0070] The collection unit can collect voice data using a voice recognition microphone. The collection unit collects voice data using, for example, a voice recognition microphone. The voice recognition microphone is a microphone equipped with a directional microphone or a noise canceling function, and can eliminate surrounding noise and collect clear voice data. For example, the collection unit collects voice from a specific direction using a directional microphone. The collection unit can also eliminate surrounding noise using a noise canceling function to improve the collection accuracy of the voice data. Furthermore, the collection unit can also collect voice data using multiple microphones and identify the position of the sound source. As a result, the use of the voice recognition microphone improves the collection accuracy of the voice data. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the voice data collected using the voice recognition microphone to the generation AI and cause the generation AI to execute analysis of the voice data.

[0071] The analysis unit can analyze the collected voice data and convert it into text. The analysis unit converts the voice data into text using, for example, voice recognition technology. Voice recognition technology is a technology that uses AI to analyze voice data and convert it into text data. For example, the analysis unit converts the voice data into text in real time using voice recognition technology. The analysis unit can also adjust the accuracy of the text conversion depending on the content of the voice data. For example, the analysis unit can convert important conversation content into text in detail and simplify unimportant content. This allows the voice data to be converted into text, allowing visual confirmation. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected voice data to a generation AI and have the generation AI convert the voice data into text.

[0072] The display unit can display the text data on the lens. The display unit displays the text data using, for example, smart glasses or an augmented reality (AR) lens. Smart glasses and AR lenses are devices that display text data by overlaying it on the user's field of vision, and the user can check the text data without moving his or her line of sight. For example, the display unit displays the text data in real time, allowing the user to check it immediately. The display unit can also adjust the font size and color of the text data to improve visibility. This allows the text data to be visually checked by displaying it on the lens. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the text data to a generation AI and cause the generation AI to execute a process of displaying the data on the lens.

[0073] The transmission unit can transmit sound to the inner ear using a bone conduction function. The transmission unit transmits sound data to the inner ear using, for example, a bone conduction headset or a bone conduction earphone. Bone conduction technology is a technology that transmits sound to the inner ear through bone, and allows the user to hear the sound without blocking the ear. For example, the transmission unit transmits sound data to the inner ear in real time using a bone conduction headset. The transmission unit can also adjust the strength of bone conduction according to the content of the sound data. For example, the transmission unit can transmit important conversation content with emphasis and transmit unimportant content at normal strength. As a result, the user can hear the sound without blocking the ear by using the bone conduction function. A part or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input sound data to the generation AI and cause the generation AI to execute a process of transmitting the sound data to the inner ear through bone conduction.

[0074] The display unit can display the text data in real time. The display unit, for example, displays the text data in real time so that the user can check it immediately. Real-time display means that there is little delay time and the user can check the text data without interrupting the flow of conversation. For example, the display unit updates the text data in real time so that the user can always check the latest information. The display unit can also adjust the font size and color of the text data to improve visibility. As a result, the text data can be displayed in real time so that it can be checked immediately. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input the text data into the generation AI and cause the generation AI to execute a process of displaying the data in real time.

[0075] The transmission unit can transmit the voice to the inner ear in real time. The transmission unit, for example, transmits voice data to the inner ear in real time. Real-time transmission means that there is little delay time and the user can check the voice data without interrupting the flow of conversation. For example, the transmission unit updates the voice data in real time, allowing the user to always check the latest information. The transmission unit can also adjust the strength of bone conduction according to the content of the voice data. This allows the voice to be transmitted in real time, allowing it to be checked immediately. Some or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can input voice data to the generation AI and have the generation AI execute a process of transmitting the voice data to the inner ear in real time.

[0076] The collection unit can estimate the emotion and adjust the timing of collecting voice data based on the estimated emotion. The collection unit, for example, estimates the emotion of the user and adjusts the timing of collecting voice data based on the estimated emotion. The emotion estimation is performed using, for example, a technique such as facial expression recognition or voice tone analysis. For example, when the user is nervous, the collection unit slightly delays the collection of voice data to give the user time to relax. In addition, when the user is relaxed, the collection unit can immediately start collecting voice data to promote natural conversation. Furthermore, when the user is in a hurry, the collection unit can quickly collect voice data to support efficient conversation. This promotes more natural conversation by adjusting the timing of collecting voice data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the timing of collecting voice data.

[0077] The collection unit can analyze the surrounding environmental sound and perform noise canceling to improve the collection accuracy of the voice data. The collection unit, for example, analyzes the surrounding environmental sound and performs noise canceling to improve the collection accuracy of the voice data. Noise canceling is performed using a technology such as active noise canceling or passive noise canceling. For example, the collection unit analyzes the surrounding noise in real time and applies noise canceling to collect clear voice data. The collection unit can also detect a sudden loud sound (for example, a car horn) and filter the voice data at that moment. Furthermore, the collection unit can learn the pattern of the surrounding environmental sound and automatically adjust the optimal noise canceling setting. This makes it possible to collect clear voice data by performing noise canceling. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the surrounding environmental sound data to the generation AI and cause the generation AI to apply noise canceling.

[0078] The collection unit can refer to the user's past conversation history and preferentially collect voice data based on a specific keyword. The collection unit, for example, refers to the user's past conversation history and preferentially collects voice data based on a specific keyword. The conversation history is stored, for example, in a database or cloud storage. For example, the collection unit preferentially collects keywords frequently used by the user so as not to miss important conversation content. The collection unit can also preferentially collect voice data related to a specific topic from the past conversation history. Furthermore, the collection unit can strengthen the collection of voice data and perform detailed analysis when the user utters a specific keyword. This makes it possible to refer to the past conversation history so as not to miss important conversation content. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's past conversation history data to the generation AI and cause the generation AI to perform preferential collection of voice data based on a specific keyword.

[0079] The collection unit can estimate emotions and determine the priority of the voice data to be collected based on the estimated emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the voice data to be collected based on the estimated emotions. The estimation of emotions is performed using, for example, techniques such as facial expression recognition and voice tone analysis. For example, when the user is excited, the collection unit preferentially collects voice data related to the heightened emotions. In addition, when the user is calm, the collection unit can also preferentially collect normal conversation contents. Furthermore, when the user is stressed, the collection unit can also preferentially collect stress-related voice data. In this way, important conversation contents can be preferentially collected by determining the priority of the voice data according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and have the generation AI determine the priority of the voice data.

[0080] The collection unit can preferentially collect highly relevant voice data in consideration of the geographical location information of the user when collecting voice data. For example, the collection unit preferentially collects highly relevant voice data in consideration of the geographical location information of the user when collecting voice data. The geographical location information is acquired, for example, using GPS data or a location information service. For example, when the user is in a specific location, the collection unit preferentially collects voice data related to the location. In addition, when the user is moving, the collection unit can also preferentially collect voice data related to the destination. Furthermore, when the user is participating in a specific event, the collection unit can also preferentially collect voice data related to the event. In this way, by considering the geographical location information, highly relevant voice data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to the generation AI and cause the generation AI to perform preferential collection of highly relevant voice data.

[0081] The collection unit can analyze the social media activity of the user and collect related voice data when collecting the voice data. For example, the collection unit analyzes the social media activity of the user and collects related voice data when collecting the voice data. The social media activity is performed, for example, by analyzing the content of posts and the number of likes. For example, the collection unit collects voice data based on keywords frequently used by the user on social media. The collection unit can also analyze the content of posts on the user's social media and collect related voice data preferentially. Furthermore, the collection unit can also collect related voice data with reference to the content of posts by the user's social media followers and friends. In this way, the related voice data can be collected preferentially by analyzing the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the social media activity data of the user to the generation AI and cause the generation AI to collect related voice data.

[0082] The analysis unit can estimate the emotion and adjust the expression method of the text based on the estimated emotion. The analysis unit, for example, estimates the emotion of the user and adjusts the expression method of the text based on the estimated emotion. The emotion estimation is performed using, for example, a technology such as facial expression recognition or voice tone analysis. For example, when the user is relaxed, the analysis unit converts the user into a text using a soft expression. In addition, when the user is nervous, the analysis unit can also convert the user into a text using a concise and clear expression. Furthermore, when the user is excited, the analysis unit can also convert the user into a text using an expression that emphasizes the emotion. This allows for more appropriate expression by adjusting the expression method of the text according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the text is expressed.

[0083] The analysis unit can adjust the level of detail of the text based on the importance of the voice data during analysis. The analysis unit, for example, adjusts the level of detail of the text based on the importance of the voice data during analysis. The importance of the voice data is evaluated based on, for example, the contents of the conversation and the importance of the speaker. For example, the analysis unit converts important conversation contents into text in detail and simplifies unimportant contents. The analysis unit can also convert parts that are particularly emphasized in the conversation into text in detail. Furthermore, the analysis unit can also convert parts related to topics in which the user is particularly interested into text in detail. As a result, important information can be converted into text in detail by adjusting the level of detail of the text based on the importance of the voice data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the importance of the voice data to the generation AI and cause the generation AI to adjust the level of detail of the text.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the voice data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the voice data during analysis. The categories of voice data are classified into, for example, business conversation and daily conversation. For example, the analysis unit applies an algorithm that appropriately analyzes technical terms when the content of the conversation is business-related. In addition, the analysis unit can also apply an algorithm that emphasizes natural expressions when the content of the conversation is daily conversation. Furthermore, the analysis unit can also apply an algorithm that accurately analyzes medical terms when the content of the conversation is medical-related. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of the voice data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the voice data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0085] The analysis unit can estimate the emotion and adjust the length of the text based on the estimated emotion. The analysis unit, for example, estimates the emotion of the user and adjusts the length of the text based on the estimated emotion. The emotion estimation is performed using, for example, a technology such as facial expression recognition or voice tone analysis. For example, when the user is in a hurry, the analysis unit performs short and to the point text. In addition, when the user is relaxed, the analysis unit can also perform longer text including detailed explanation. Furthermore, when the user is excited, the analysis unit can also perform longer text with emphasis on the emotion. This allows more appropriate text to be produced by adjusting the length of the text according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the length of the text to be converted.

[0086] The analysis unit can determine the priority of text conversion based on the collection time of the voice data during analysis. The analysis unit, for example, determines the priority of text conversion based on the collection time of the voice data during analysis. The collection time of the voice data is evaluated based on, for example, a time stamp or a collection date and time. For example, the analysis unit preferentially converts the latest voice data into text. The analysis unit can also preferentially convert voice data collected in a specific time period into text. Furthermore, the analysis unit can also preferentially convert voice data in a time period specified by the user into text. As a result, by determining the priority of text conversion based on the collection time of the voice data, the latest information can be preferentially converted into text. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the voice data to the generation AI and cause the generation AI to execute the determination of the priority of text conversion.

[0087] The analysis unit can adjust the order of text conversion based on the relevance of the voice data during analysis. The analysis unit, for example, adjusts the order of text conversion based on the relevance of the voice data during analysis. The relevance of the voice data is evaluated based on, for example, the similarity of the contents and the relationship between the speakers. For example, the analysis unit preferentially converts important conversation contents into text. The analysis unit can also preferentially convert highly relevant parts into text along the flow of the conversation. Furthermore, the analysis unit can also preferentially convert parts related to topics in which the user is interested into text. As a result, important information can be preferentially converted into text by adjusting the order of text conversion based on the relevance of the voice data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the voice data to the generation AI and cause the generation AI to adjust the order of text conversion.

[0088] The display unit can estimate the emotion and adjust the font size and color of the display based on the estimated emotion. The display unit, for example, estimates the emotion of the user and adjusts the font size and color of the display based on the estimated emotion. The emotion estimation is performed using, for example, a technology such as facial expression recognition or voice tone analysis. For example, when the user is nervous, the display unit uses a font with a calm color. Also, when the user is relaxed, the display unit can use a font with a bright color. Furthermore, when the user is excited, the display unit can use a font size with high visibility. Thereby, the visibility is improved by adjusting the font size and color of the display according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the font size and color of the display.

[0089] The display unit can select the optimal display method by referring to the user's visual acuity information at the time of display. The display unit, for example, selects the optimal display method by referring to the user's visual acuity information at the time of display. The visual acuity information is acquired, for example, based on a visual acuity test result or a user's self-report. For example, the display unit adjusts the font size according to the user's visual acuity. The display unit can also adjust the contrast according to the user's visual acuity. Furthermore, the display unit can adjust the brightness of the display according to the user's visual acuity. In this way, the optimal display method can be provided by referring to the user's visual acuity information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's visual acuity information to the generation AI and cause the generation AI to select the optimal display method.

[0090] The display unit can apply different display formats according to the contents of the text when displayed. For example, the display unit applies different display formats according to the contents of the text when displayed. The contents of the text are classified based on, for example, importance or category. For example, the display unit displays important information in bold. The display unit can also perform color coding according to the flow of the conversation. Furthermore, the display unit can also highlight specific keywords. This makes it possible to provide an optimal display format according to the contents of the text. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the contents of the text to a generation AI and cause the generation AI to apply different display formats.

[0091] The display unit can estimate the emotion and adjust the timing of display based on the estimated emotion. The display unit, for example, estimates the emotion of the user and adjusts the timing of display based on the estimated emotion. The emotion estimation is performed using, for example, a technique such as facial expression recognition or voice tone analysis. For example, when the user is nervous, the display unit delays the timing of display to give the user time to relax. In addition, when the user is relaxed, the display unit can also immediately perform the timing of display. Furthermore, when the user is in a hurry, the display unit can quickly perform the timing of display. This allows for more appropriate display by adjusting the timing of display according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of display.

[0092] The display unit can select the optimal display method in consideration of the device information of the user at the time of display. For example, the display unit selects the optimal display method in consideration of the device information of the user at the time of display. The device information is acquired, for example, based on the type of device and the screen size. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the display unit can also provide a simple and highly visible display method. This makes it possible to provide the optimal display method by considering the device information of the user. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the device information of the user to the generation AI and cause the generation AI to select the optimal display method.

[0093] The display unit can select the optimal display method by referring to the user's past display history at the time of display. For example, the display unit selects the optimal display method by referring to the user's past display history at the time of display. The display history is stored in, for example, a database or cloud storage. For example, the display unit preferentially provides a display format that the user has used in the past. The display unit can also suggest optimal font sizes and colors from the user's past display history. Furthermore, the display unit can analyze the user's past display history and suggest optimal display timing. In this way, the optimal display method can be provided by referring to the user's past display history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's past display history data to the generation AI and cause the generation AI to select the optimal display method.

[0094] The transmission unit can estimate the emotion and adjust the intensity of bone conduction based on the estimated emotion. The transmission unit, for example, estimates the emotion of the user and adjusts the intensity of bone conduction based on the estimated emotion. The emotion is estimated using, for example, a technology such as facial expression recognition or voice tone analysis. For example, when the user is tense, the transmission unit weakens the intensity of bone conduction to relax the user. In addition, when the user is relaxed, the transmission unit can also set the intensity of bone conduction to normal. Furthermore, when the user is excited, the transmission unit can also strengthen the intensity of bone conduction to emphasize the emotion. This enables more appropriate voice transmission by adjusting the intensity of bone conduction according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the strength of bone conduction.

[0095] The transmission unit can select the optimal transmission method by referring to the user's hearing information at the time of transmission. For example, the transmission unit selects the optimal transmission method by referring to the user's hearing information at the time of transmission. The hearing information is acquired, for example, based on a hearing test result or a user's self-report. For example, the transmission unit adjusts the strength of bone conduction according to the user's hearing. The transmission unit can also adjust the frequency of the sound according to the user's hearing. Furthermore, the transmission unit can also adjust the volume of the sound according to the user's hearing. This makes it possible to optimally transmit sound by referring to the user's hearing information. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can input the user's hearing information to the generation AI and cause the generation AI to select the optimal transmission method.

[0096] The transmission unit can apply different transmission formats depending on the contents of the voice data at the time of transmission. For example, the transmission unit applies different transmission formats depending on the contents of the voice data at the time of transmission. The contents of the voice data are classified based on, for example, importance or category. For example, the transmission unit emphasizes and transmits important information. The transmission unit can also perform natural transmission along the flow of the conversation. Furthermore, the transmission unit can also emphasize and transmit specific keywords. This makes it possible to provide an optimal transmission format depending on the contents of the voice data. Part or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the contents of the voice data to the generation AI and cause the generation AI to apply different transmission formats.

[0097] The transmission unit can estimate the emotion and adjust the timing of the transmission based on the estimated emotion. The transmission unit, for example, estimates the emotion of the user and adjusts the timing of the transmission based on the estimated emotion. The emotion estimation is performed using, for example, a technology such as facial expression recognition or voice tone analysis. For example, when the user is nervous, the transmission unit delays the timing of the transmission to give the user time to relax. In addition, when the user is relaxed, the transmission unit can also immediately perform the timing of the transmission. Furthermore, when the user is in a hurry, the transmission unit can also quickly perform the timing of the transmission. This enables more appropriate voice transmission by adjusting the timing of the transmission according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of the transmission.

[0098] The transmission unit can select the optimal transmission method in consideration of the geographical location information of the user at the time of transmission. For example, the transmission unit selects the optimal transmission method in consideration of the geographical location information of the user at the time of transmission. The geographical location information is acquired, for example, using GPS data or a location information service. For example, when the user is in a specific location, the transmission unit preferentially transmits voice data related to the location. In addition, when the user is moving, the transmission unit can also preferentially transmit voice data related to the destination. Furthermore, when the user is participating in a specific event, the transmission unit can also preferentially transmit voice data related to the event. This makes it possible to optimally transmit voice by considering the geographical location information of the user. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select the optimal transmission method.

[0099] The transmission unit can select the optimal transmission method by referring to the user's past transmission history at the time of transmission. For example, the transmission unit selects the optimal transmission method by referring to the user's past transmission history at the time of transmission. The transmission history is stored in, for example, a database or cloud storage. For example, the transmission unit preferentially provides a transmission format that the user has used in the past. The transmission unit can also suggest the optimal transmission method from the user's past transmission history. Furthermore, the transmission unit can analyze the user's past transmission history and suggest the optimal transmission timing. This makes it possible to perform optimal voice transmission by referring to the user's past transmission history. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI or may be performed without using AI. For example, the transmission unit can input the user's past transmission history data to the generation AI and cause the generation AI to select the optimal transmission method.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The communication assist glasses system may further include a translation unit. The translation unit translates the voice data collected by the collection unit into another language in real time. For example, when the collection unit collects Japanese voice data, the translation unit translates the voice data into English, and the analysis unit converts the translated voice data into text. This facilitates communication between users who speak different languages. The translation unit may also automatically select a translation language based on the user's language settings. Furthermore, the translation unit may also include a dictionary function for appropriately translating technical terms and slang. This allows the user to smoothly converse with a person who speaks a different language.

[0102] The collection unit can learn the user's tone of voice and speaking patterns, and collect voice data that is individually optimized. For example, if the user speaks quickly, the collection unit collects voice data that matches that speed. Also, if the user speaks in a low voice, the collection unit can collect voice data that matches that range. Furthermore, the collection unit can learn the tone of voice used when the user expresses a particular emotion, and collect voice data according to that emotion. This makes it possible to collect voice data that is optimized for the individual speaking style of the user.

[0103] When analyzing the voice data, the analysis unit can improve the analysis accuracy by referring to the user's past conversation history. For example, the analysis unit preferentially analyzes phrases and words that the user has frequently used in the past. The analysis unit can also learn the user's speaking patterns from the past conversation history and improve the analysis accuracy based on the patterns. Furthermore, the analysis unit can also preferentially analyze voice data related to a specific topic based on the past conversation history. This makes it possible to improve the analysis accuracy by utilizing the user's past conversation history.

[0104] The display unit can automatically adjust the display method of the text data based on the user's visual acuity information. For example, the display unit increases the font size when the user's visual acuity is deteriorating. The display unit can also adjust the contrast according to the user's visual acuity to improve visibility. Furthermore, the display unit can adjust the display position of the text data based on the user's visual acuity information. This makes it possible to provide a display method optimized for the user's visual acuity.

[0105] The transmission unit can automatically adjust the bone conduction strength according to the content of the audio data. For example, when the content of the conversation is important, the bone conduction strength is increased for transmission. Also, when the content of the conversation is everyday, the bone conduction strength can be set to normal. Furthermore, when the content of the audio data is urgent, the bone conduction strength can be maximized for transmission. This allows audio to be transmitted at the optimal bone conduction strength according to the content of the audio data.

[0106] The collection unit can estimate the user's emotions and adjust the method of collecting voice data based on the estimated emotions. For example, if the user is nervous, the collection unit can slightly delay the collection of voice data to give the user time to relax. Also, if the user is relaxed, the collection of voice data can be started immediately. Furthermore, if the user is in a hurry, the collection of voice data can be performed quickly to support efficient conversation. This makes it possible to collect optimal voice data according to the user's emotions.

[0107] The analysis unit can estimate the user's emotions and adjust the way the text is expressed based on the estimated emotions. For example, if the user is relaxed, the text can be expressed in a gentle manner. If the user is nervous, the text can be expressed in a concise and clear manner. Furthermore, if the user is excited, the text can be expressed in an expression that emphasizes the emotion. This makes it possible to optimally convert the text according to the user's emotions.

[0108] The display unit can estimate the user's emotion and adjust the font size and color of the display based on the estimated emotion. For example, if the user is nervous, a font with a calm color tone can be used. If the user is relaxed, a font with a bright color tone can be used. Furthermore, if the user is excited, a font size with high visibility can be used. This makes it possible to provide an optimal display according to the user's emotion.

[0109] The transmission unit can estimate the user's emotion and adjust the bone conduction intensity based on the estimated emotion. For example, when the user is nervous, the bone conduction intensity is reduced to relax the user. Also, when the user is relaxed, the bone conduction intensity can be set to normal. Furthermore, when the user is excited, the bone conduction intensity can be increased to emphasize the emotion. This allows audio to be transmitted at an optimal bone conduction intensity according to the user's emotion.

[0110] The transmission unit can estimate the user's emotion and adjust the timing of transmission based on the estimated emotion. For example, if the user is nervous, the timing of transmission can be delayed to give the user time to relax. Also, if the user is relaxed, the timing of transmission can be immediate. Furthermore, if the user is in a hurry, the timing of transmission can be quick. This makes it possible to transmit voice at the optimal timing according to the user's emotion.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The collection unit collects voice data. The collection unit collects voice data using, for example, a voice recognition microphone. The voice recognition microphone is a microphone equipped with a directional microphone and a noise canceling function, and can eliminate ambient noise and collect clear voice data. For example, the collection unit uses a directional microphone to collect voice from a specific direction. The collection unit can also eliminate ambient noise using a noise canceling function to improve the accuracy of collecting voice data. Furthermore, the collection unit can collect voice data using multiple microphones and identify the position of the sound source. Step 2: The analysis unit analyzes the voice data collected by the collection unit and converts it into text. The analysis unit converts the voice data into text, for example, using voice recognition technology. Voice recognition technology is a technology that uses AI to analyze voice data and convert it into text data. For example, the analysis unit converts the voice data into text in real time using voice recognition technology. The analysis unit can also adjust the accuracy of the conversion into text depending on the content of the voice data. For example, the analysis unit can convert important conversation content into text in detail and simplify unimportant content. Step 3: The display unit displays the data converted to text by the analysis unit on the lens. The display unit displays the text data using, for example, smart glasses or an augmented reality (AR) lens. Smart glasses and AR lenses are devices that display text data by overlaying it on the user's field of vision, allowing the user to check the text data without moving their line of sight. For example, the display unit displays the text data in real time, allowing the user to check it immediately. The display unit can also adjust the font size and color of the text data to improve visibility. Step 4: The transmission unit transmits the voice data analyzed by the analysis unit to the inner ear through bone conduction. The transmission unit transmits the voice data to the inner ear using, for example, a bone conduction headset or bone conduction earphones. Bone conduction technology is a technology that transmits voice to the inner ear through bones, allowing you to hear the voice without blocking your ears. For example, the transmission unit transmits the voice data to the inner ear in real time using a bone conduction headset. The transmission unit can also adjust the strength of bone conduction depending on the content of the voice data. For example, the transmission unit can transmit important conversation content with emphasis and transmit unimportant content at normal strength.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned collection unit, analysis unit, display unit, and transmission unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects voice data using a voice recognition microphone of the smart device 14. The analysis unit analyzes the voice data by the specific processing unit 290 of the data processing device 12 and converts it into text. The display unit displays the text data using the display 40A of the smart device 14. The transmission unit transmits the voice data to the inner ear using the bone conduction function of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0124] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned collection unit, analysis unit, display unit, and transmission unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects voice data using a voice recognition microphone of the smart glasses 214. The analysis unit analyzes the voice data by the specific processing unit 290 of the data processing device 12 and converts it into text. The display unit displays the text data using the display of the smart glasses 214. The transmission unit transmits the voice data to the inner ear using the bone conduction function of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

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

[0136] 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.

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

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

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

[0140] 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.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned collection unit, analysis unit, display unit, and transmission unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects voice data using a voice recognition microphone of the headset type terminal 314. The analysis unit analyzes the voice data by the specific processing unit 290 of the data processing device 12 and converts it into text. The display unit displays the text data using the display 343 of the headset type terminal 314. The transmission unit transmits the voice data to the inner ear using the bone conduction function of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0152] 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.

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

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

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

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] Each of the multiple elements including the above-mentioned collection unit, analysis unit, display unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects voice data using a voice recognition microphone of the robot 414. The analysis unit analyzes the voice data by the specific processing unit 290 of the data processing device 12 and converts it into text. The display unit displays the text data using the display of the robot 414. The transmission unit transmits the voice data to the inner ear using the bone conduction function of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0173] 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.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0176] 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.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A collection unit that collects voice data; An analysis unit that analyzes the collected voice data and converts it into text; A display unit that displays the text data on the lens; A transmission unit that transmits the analyzed voice data to the inner ear through bone conduction. A system characterized by: (Appendix 2) The collecting unit includes: Collect voice data using a voice recognition microphone 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze collected voice data and convert it into text 2. The system of claim 1, (Appendix 4) The display unit is Displaying text data in a lens 2. The system of claim 1, (Appendix 5) The transmission unit is Uses bone conduction to transmit sound to the inner ear 2. The system of claim 1, (Appendix 6) The display unit is Displaying textual data in real time 2. The system of claim 1, (Appendix 7) The transmission unit is Transmitting sound to the inner ear in real time 2. The system of claim 1, (Appendix 8) The collecting unit includes: Estimate emotions and adjust the timing of voice data collection based on the estimated emotions. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyzes surrounding environmental sounds and performs noise cancellation to improve the accuracy of voice data collection 2. The system of claim 1, (Appendix 10) The collecting unit includes: Referencing the user's past conversation history and collecting voice data based on specific keywords 2. The system of claim 1, (Appendix 11) The collecting unit includes: Estimate emotions and prioritize the voice data to be collected based on the estimated emotions. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting voice data, the system prioritizes collection of relevant voice data by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting voice data, analyze your social media activity and collect related voice data. 2. The system of claim 1, (Appendix 14) The analysis unit is Estimate emotions and adjust the way the text is rendered based on the estimated emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the level of detail in the text conversion is adjusted based on the importance of the audio data. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of audio data. 2. The system of claim 1, (Appendix 17) The analysis unit is Estimate emotions and adjust the length of transcription based on the estimated emotions 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize transcription based on when the audio data was collected 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, adjust the order of transcription based on the relevance of the audio data 2. The system of claim 1, (Appendix 20) The display unit is Estimate emotions and adjust font size and color based on the emotion 2. The system of claim 1, (Appendix 21) The display unit is When displaying, the optimal display method is selected by referring to the user's visual acuity information. 2. The system of claim 1, (Appendix 22) The display unit is Apply different display formats depending on the text content when displayed 2. The system of claim 1, (Appendix 23) The display unit is Estimate emotions and adjust the timing of display based on the estimated emotions 2. The system of claim 1, (Appendix 24) The display unit is When displaying, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 25) The display unit is When displaying, the optimal display method is selected by referring to the user's past display history. 2. The system of claim 1, (Appendix 26) The transmission unit is Estimate emotions and adjust bone conduction strength based on the estimated emotions 2. The system of claim 1, (Appendix 27) The transmission unit is When transmitting, the optimal transmission method is selected by referring to the user's hearing information. 2. The system of claim 1, (Appendix 28) The transmission unit is When transmitting, different transmission formats are applied depending on the content of the audio data. 2. The system of claim 1, (Appendix 29) The transmission unit is Estimate emotions and adjust the timing of communication based on the estimated emotions 2. The system of claim 1, (Appendix 30) The transmission unit is When transmitting information, the optimal method of transmission is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 31) The transmission unit is When communicating, select the optimal communication method by referring to the user's past communication history. 2. The system of claim 1, [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects voice data; An analysis unit that analyzes the collected voice data and converts it into text; A display unit that displays the text data on the lens; A transmission unit that transmits the analyzed voice data to the inner ear through bone conduction. A system characterized by:

2. The collecting unit includes: Collect voice data using a voice recognition microphone 2. The system of claim 1 .

3. The display unit is Displaying textual data in real time 2. The system of claim 1 .

4. The transmission unit is Transmitting sound to the inner ear in real time 2. The system of claim 1 .

5. The collecting unit includes: Estimate emotions and adjust the timing of voice data collection based on the estimated emotions.

2. The system of claim 1 .

6. The analysis unit is During analysis, the level of detail in the text conversion is adjusted based on the importance of the audio data.

2. The system of claim 1 .

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