system

Abstract

The system according to the embodiment aims to eliminate inconveniences in daily life caused by hearing impairments or hearing loss. According to an embodiment, the system includes a collection unit, an analysis unit, and a transmission unit. The collection unit collects ambient sound. The analysis unit analyzes the sound collected by the collection unit. The transmission unit transmits the sound analyzed by the analysis unit to the inner ear using bone conduction technology.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to fully alleviate the inconveniences of daily life caused by hearing impairments or hearing loss, and there is room for improvement.

[0005] The system according to the embodiment aims to eliminate inconveniences in daily life caused by hearing impairments or hearing loss. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a transmission unit. The collection unit collects ambient sound. The analysis unit analyzes the sound collected by the collection unit. The transmission unit transmits the sound analyzed by the analysis unit to the inner ear using bone conduction technology. [Effects of the Invention]

[0007] The system according to the embodiment can eliminate inconveniences in daily life caused by hearing impairments or hearing loss. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​bone conduction chip system according to an embodiment of the present invention eliminates the inconveniences of hearing impairments and offers a new tomorrow. This system collects ambient sounds, analyzes them using AI, and transmits them directly to the inner ear using bone conduction technology, allowing the hearing-impaired and hard-of-hearing individuals to hear the sounds around them. It also provides multilingual automatic translation and information gathering functions to general users. For example, when a user hears a conversation in a foreign language, the system analyzes the audio, automatically translates it using AI, and transmits it to the inner ear using bone conduction technology. This allows the user to listen to the translated audio in real time. Furthermore, it can also be used as an information gathering device. For example, if a user wants to hear information such as the news or weather forecast, the system collects the information from the Internet, analyzes it using AI, and transmits it to the inner ear using bone conduction technology. This allows the user to obtain the necessary information in real time. Thus, the AI ​​bone conduction chip system is a groundbreaking system that provides a new tomorrow for the hearing-impaired and hard-of-hearing individuals, and can also be used as a multilingual automatic translation and information gathering device for general users. This means that the AI ​​bone conduction chip system will not only enable the deaf and hard of hearing to hear the sounds around them, but will also provide general users with multilingual automatic translation and information gathering functions.

[0029] The AI ​​bone conduction chip system according to the embodiment includes a collection unit, an analysis unit, and a transmission unit. The collection unit collects surrounding sounds. The collection unit can collect surrounding sounds using, for example, a microphone. The collection unit can collect sounds such as conversation sounds, environmental sounds, and warning sounds. The collection unit can also collect sounds from a specific direction using, for example, a directional microphone. The collection unit can also collect sounds from all directions using, for example, an omnidirectional microphone. The analysis unit analyzes the sounds collected by the collection unit. The analysis unit removes noise using, for example, digital signal processing and analyzes the sounds using a speech recognition algorithm. The analysis unit can use speech recognition algorithms such as a hidden Markov model (HMM) or a deep neural network (DNN). The analysis unit can extract speech features and recognize the content of the speech. The analysis unit can also analyze the frequency components of the speech and grasp the characteristics of the speech. The transmission unit transmits the speech analyzed by the analysis unit to the inner ear using bone conduction technology. The transmission unit can transmit sound to the inner ear using, for example, a bone conduction transducer. The transmission unit can transmit sound to the inner ear as vibrations by, for example, attaching a bone conduction transducer to the skull. The transmission unit can also transmit sound to the inner ear as vibrations by, for example, attaching a bone conduction transducer behind the ear. As a result, the AI ​​bone conduction chip system according to the embodiment collects and analyzes surrounding sounds and transmits them to the inner ear using bone conduction technology, allowing people with hearing impairments or hard of hearing to hear surrounding sounds. Some or all of the above-described 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 transmit sound to the inner ear using an AI model that receives sound analyzed by the analysis unit as input and outputs the sound as vibrations.

[0030] The analysis unit can remove noise using digital signal processing and analyze the speech using a speech recognition algorithm. Digital signal processing includes, for example, filtering and noise reduction techniques. The analysis unit can remove noise in a specific frequency band using filtering techniques. For example, the analysis unit can use a low-pass filter to remove low-frequency noise. The analysis unit can also use a high-pass filter to remove high-frequency noise. The analysis unit can also reduce noise from the speech signal using noise reduction techniques. For example, the analysis unit can reduce noise using spectral subtraction techniques. Speech recognition algorithms include, for example, hidden Markov models (HMMs) and deep neural networks (DNNs). The analysis unit can model temporal changes in speech using HMMs to recognize speech content. The analysis unit can also extract speech features using DNNs to perform highly accurate speech recognition. For example, the analysis unit can use DNNs to learn the spectral features of speech and recognize speech content. As a result, the accuracy of speech analysis is improved by removing noise and using a speech recognition algorithm. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may use the voice collected by the collection unit as input and analyze the voice using an AI model that performs noise removal and voice recognition.

[0031] The transmission unit can transmit sound to the inner ear using a bone conduction transducer. Bone conduction transducers include, for example, types attached to the skull and types attached behind the ear. The transmission unit can transmit sound to the inner ear using, for example, a bone conduction transducer attached to the skull. For example, the transmission unit can recognize sound by having a transducer attached to the skull transmit an audio signal as vibration, and the vibration is transmitted to the inner ear. The transmission unit can also transmit sound to the inner ear using a bone conduction transducer attached behind the ear. For example, the transmission unit can recognize sound by having a transducer attached behind the ear transmit an audio signal as vibration, and the vibration is transmitted to the inner ear. In this way, sound can be transmitted directly to the inner ear using a bone conduction transducer. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the transmission unit can transmit sound to the inner ear using an AI model that receives the sound analyzed by the analysis unit as input and outputs the sound as vibration.

[0032] The analysis unit can analyze foreign language speech and automatically translate it using AI. Examples of foreign language speech include English, Chinese, and Spanish. The analysis unit can analyze English speech and automatically translate it using AI. For example, the analysis unit can collect English speech, convert it into text using a speech recognition algorithm, and automatically translate the text using AI. The analysis unit can also analyze Chinese speech and automatically translate it using AI. For example, the analysis unit can collect Chinese speech, convert it into text using a speech recognition algorithm, and automatically translate the text using AI. The analysis unit can also analyze Spanish speech and automatically translate it using AI. For example, the analysis unit can collect Spanish speech, convert it into text using a speech recognition algorithm, and automatically translate the text using AI. Automatic translation using AI includes technologies such as neural machine translation (NMT) and statistical machine translation (SMT). The analysis unit can perform high-precision translation using NMT. ​​For example, the analysis unit can translate foreign language text using a neural network and output the translation result. The analysis unit can also perform translation using SMT. For example, the analysis unit can translate foreign language text using statistical methods and output the translation results. This enables multilingual support by analyzing foreign language speech and automatically translating it. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input foreign language speech and analyze the speech using an AI model that performs speech recognition and translation.

[0033] The transmission unit can transmit the translated audio to the inner ear using bone conduction technology. Translated audio includes, for example, English, Chinese, Spanish, etc. The transmission unit can transmit, for example, audio translated into English to the inner ear using bone conduction technology. For example, the transmission unit can transmit an audio signal translated into English to the inner ear as vibrations. The transmission unit can also transmit audio translated into Chinese to the inner ear using bone conduction technology. For example, the transmission unit can transmit an audio signal translated into Chinese to the inner ear as vibrations. The transmission unit can also transmit audio translated into Spanish to the inner ear using bone conduction technology. For example, the transmission unit can transmit an audio signal translated into Spanish to the inner ear as vibrations. By transmitting the translated audio to the inner ear, the user can hear the translated audio in real time. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the transmission unit can transmit audio to the inner ear using an AI model that receives audio translated by the analysis unit as input and outputs audio as vibrations.

[0034] The analysis unit may use specific means for collecting and analyzing information from the Internet. Examples of methods for collecting information from the Internet include web scraping and the use of APIs. The analysis unit may use web scraping to collect information on the Internet. For example, the analysis unit may collect information such as news articles and weather forecasts from specific websites. The analysis unit may also use APIs to collect information on the Internet. For example, the analysis unit may use a news API or a weather forecast API to collect the latest news and weather forecast information. The collected information is analyzed by the analysis unit. The analysis unit may use natural language processing technology to analyze the collected information. For example, the analysis unit may analyze the content of collected news articles and extract important information. The analysis unit may also analyze collected weather forecast data to provide important information to users. This allows necessary information to be obtained in real time by collecting and analyzing information from the Internet. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can take information collected from the Internet as input and analyze the information using an AI model that analyzes the information.

[0035] The transmission unit can transmit the analyzed information to the inner ear using bone conduction technology. The analyzed information includes, for example, news articles and weather forecasts. The transmission unit can transmit, for example, the content of the analyzed news article to the inner ear using bone conduction technology. For example, the transmission unit can convert the content of the news article into vibrations as an audio signal and transmit the vibrations to the inner ear. The transmission unit can also transmit analyzed weather forecast information to the inner ear using bone conduction technology. For example, the transmission unit can convert weather forecast information into vibrations as an audio signal and transmit the vibrations to the inner ear. In this way, by transmitting the analyzed information to the inner ear, information can be obtained in real time. Some 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 transmit information to the inner ear using an AI model that receives the information analyzed by the analysis unit as input and outputs the information as an audio signal.

[0036] The collection unit can analyze surrounding environmental sounds and select an appropriate collection method. The collection unit can, for example, use a microphone to analyze the surrounding environmental sounds. For example, the collection unit can collect surrounding environmental sounds and analyze the environmental sounds using a sound analysis algorithm. The collection unit can analyze environmental sounds such as traffic sounds, natural sounds, and noise. The collection unit can, for example, analyze traffic sounds and enhance noise canceling functionality. For example, when there is a lot of traffic noise, the collection unit can enhance the noise canceling functionality and collect clear audio. The collection unit can also analyze natural sounds and use a high-sensitivity microphone. For example, when the surroundings are quiet, the collection unit can collect even faint audio using a high-sensitivity microphone. Furthermore, the collection unit can dynamically adjust the collection method when the surrounding sounds fluctuate. For example, when the surrounding sounds fluctuate, the collection unit can dynamically adjust the collection method using AI to collect optimal audio. This allows clear audio to be collected by selecting the optimal collection method depending on the surrounding environmental sounds. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can collect audio using an AI model that inputs ambient environmental sound data and selects the optimal collection method.

[0037] The collection unit can emphasize specific frequency bands when collecting audio. The collection unit can use filtering technology to emphasize specific frequency bands. For example, the collection unit can use a bandpass filter to emphasize frequency bands where human voices are prevalent. For example, the collection unit can emphasize a frequency band from 1 kHz to 4 kHz to clearly collect conversations. The collection unit can also filter specific frequency bands to emphasize specific musical instrument sounds. For example, the collection unit can emphasize a frequency band from 100 Hz to 1 kHz to emphasize the sound of a guitar. Furthermore, the collection unit can emphasize specific frequency bands of environmental sounds. For example, the collection unit can emphasize a wide frequency band from 20 Hz to 20 kHz to realistically collect natural sounds. By emphasizing and collecting specific frequency bands, audio tailored to the purpose can be clearly collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input audio data and collect audio using an AI model that emphasizes specific frequency bands.

[0038] When collecting audio, the collection unit can prioritize collecting highly relevant audio by taking into account the user's geographical location information. The collection unit can, for example, acquire the user's geographical location information using GPS data. For example, the collection unit can acquire GPS data from the user's smartphone or wearable device to identify the user's current location. The collection unit can also acquire the user's geographical location information using a location information service. For example, the collection unit can acquire the user's current location through a location information service and collect audio based on the location information. The collection unit prioritizes collecting highly relevant audio based on the user's geographical location information. For example, if the user is in a tourist destination, the collection unit can prioritize collecting tourist information audio. If the user is in a business area, the collection unit can prioritize collecting business-related audio. Furthermore, if the user is at home, the collection unit can prioritize collecting audio from within the home. This allows for more useful information to be obtained by collecting highly relevant audio based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect audio using an AI model that takes the user's geographic location information as input and prioritizes collecting highly relevant audio.

[0039] When collecting audio, the collection unit can analyze the user's social media activity and collect related audio. For example, the collection unit can collect the user's post content, number of likes, number of followers, etc. to analyze the social media activity. For example, if the user posts music-related content, the collection unit can prioritize collecting music-related audio. Furthermore, if the user posts news-related content, the collection unit can prioritize collecting news-related audio. Furthermore, if the user posts sports-related content, the collection unit can prioritize collecting sports-related audio. This allows for the provision of more personalized information by collecting related audio based on the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can collect audio using an AI model that uses the user's social media data as input and collects related audio.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the audio. For example, the analysis unit can consider the urgency, relevance, frequency, etc. of the content to evaluate the importance of the audio. For example, the analysis unit can perform a detailed analysis of important conversational audio to provide accurate information. Furthermore, the analysis unit can perform a simplified analysis of background sound to provide the minimum necessary information. Furthermore, the analysis unit can perform an appropriate analysis of notification sounds to provide information quickly. Thus, by adjusting the level of detail of the analysis according to the importance of the audio, necessary information can be appropriately provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can provide analysis results using an AI model that receives audio data and adjusts the level of detail of the analysis based on the importance of the audio.

[0041] The analysis unit can apply different analysis algorithms depending on the category of audio during analysis. For example, the analysis unit can set categories such as news, music, and conversation to classify audio. For example, the analysis unit can apply a speech recognition algorithm to conversational audio to perform accurate transcription. The analysis unit can also apply a music analysis algorithm to musical audio to provide song information. Furthermore, the analysis unit can apply an environmental sound analysis algorithm to environmental sounds to identify the sound source. In this way, by applying different analysis algorithms depending on the category of audio, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can provide analysis results using an AI model that receives audio data and applies different analysis algorithms depending on the category of audio.

[0042] During analysis, the analysis unit can determine the analysis priority based on when the audio was collected. The analysis unit can, for example, consider the collection date and time, collection frequency, etc. to evaluate when the audio was collected. For example, the analysis unit can prioritize analyzing the most recent audio and provide information in real time. The analysis unit can also prioritize analyzing important audio from the past and provide necessary information. Furthermore, the analysis unit can prioritize analyzing audio collected periodically and provide continuous information. In this way, by determining the analysis priority based on when the audio was collected, necessary information can be provided in real time. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that receives audio data as input and determines the analysis priority based on when the audio was collected.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the audio. The analysis unit can, for example, consider the degree of content similarity or related keywords to evaluate the relevance of the audio. For example, the analysis unit can prioritize analyzing important conversational audio and provide information quickly. The analysis unit can also prioritize analyzing necessary information, leaving background audio for later analysis. Furthermore, the analysis unit can appropriately analyze notification audio and provide information quickly. As a result, important information can be provided quickly by adjusting the order of analysis based on the relevance of the audio. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that receives audio data as input and adjusts the order of analysis based on the relevance of the audio.

[0044] When transmitting information, the transmission unit can analyze the user's past hearing history and select an appropriate transmission method. The transmission unit can, for example, collect hearing test results and past audio data to obtain the user's past hearing history. For example, the transmission unit can analyze the user's hearing test results and identify the user's preferred audio format. The transmission unit can also analyze past audio data and identify the user's preferred audio format. The transmission unit can also identify audio formats avoided by the user. For example, the transmission unit can eliminate audio formats avoided by the user in the past and select optimal volume and tone. This allows information to be transmitted in a more appropriate manner by selecting an optimal transmission method based on the user's past hearing history. Some or all of the above-described processing in the transmission unit can be performed, for example, using AI, or can be performed without using AI. For example, the transmission unit can transmit information using an AI model that inputs the user's past hearing history data and selects an appropriate transmission method.

[0045] The transmission unit can customize the means of transmission based on the user's current living situation when transmitting information. The transmission unit can, for example, take into account the user's daily activity patterns and living environment to evaluate the user's current living situation. For example, the transmission unit can prioritize voice guidance when the user is driving. Furthermore, the transmission unit can notify the user by vibration when the user is in a meeting. Furthermore, the transmission unit can provide detailed information when the user is relaxed. This allows information to be transmitted in a more appropriate manner by customizing the means of transmission according to the user's living situation. Some or all of the above-described 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 transmit information using an AI model that uses the user's living situation data as input and customizes the means of transmission.

[0046] The transmission unit can select the optimal transmission method by taking into account the user's geographical location information when transmitting information. The transmission unit can, for example, acquire the user's geographical location information using GPS data. For example, the transmission unit can acquire GPS data from the user's smartphone or wearable device to identify the user's current location. The transmission unit can also acquire the user's geographical location information using a location information service. For example, the transmission unit can acquire the user's current location through a location information service and select the optimal transmission method based on the location information. The transmission unit selects the optimal transmission method based on the user's geographical location information. For example, if the user is in a tourist destination, the transmission unit can prioritize transmitting tourist information. Furthermore, if the user is in a business area, the transmission unit can prioritize transmitting business-related information. Furthermore, if the user is at home, the transmission unit can prioritize transmitting information about the home. As a result, by selecting the optimal transmission method based on the user's geographical location information, more appropriate information can be provided. Some or all of the above-described 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 transmit information using an AI model that inputs the user's geographical location information and selects the optimal transmission method.

[0047] The transmission unit can analyze the user's social media activity and suggest a means of transmission when transmitting the information. For example, the transmission unit can collect the user's post content, the number of likes, the number of followers, etc., to analyze the social media activity. For example, if the user posts music-related information, the transmission unit can prioritize transmitting music-related information. Furthermore, if the user posts news-related information, the transmission unit can prioritize transmitting news-related information. Furthermore, if the user posts sports-related information, the transmission unit can prioritize transmitting sports-related information. This allows for more personalized information to be provided by suggesting a means of transmission based on the user's social media activity. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the transmission unit can transmit information using an AI model that inputs the user's social media data and suggests a means of transmission.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The collection unit can detect the user's activity status and select an appropriate audio collection mode. For example, if the user is exercising, the collection unit can select an audio collection mode suitable for exercise and emphasize ambient sounds. If the user is reading, the collection unit can select an audio collection mode suitable for a quiet environment and reduce noise. If the user is in a meeting, the collection unit can select an audio collection mode suitable for the meeting and emphasize conversation sounds. Thus, by selecting an appropriate audio collection mode according to the user's activity status, clearer audio can be collected.

[0050] The analysis unit can determine the priority of analysis based on the time when the audio was collected. For example, it can prioritize analysis of the most recent audio and provide information in real time. It can also prioritize analysis of important audio from the past and provide necessary information. It can also prioritize analysis of audio collected periodically and provide continuous information. Thus, by determining the priority of analysis based on the time when the audio was collected, it is possible to provide necessary information in real time.

[0051] The collection unit can prioritize collection of highly relevant voices in consideration of the user's geographical location information. For example, if the user is in a tourist spot, it can prioritize collection of tourist information voices. Also, if the user is in a business area, it can prioritize collection of business-related voices. Furthermore, if the user is at home, it can prioritize collection of voices from within the home. In this way, by collecting highly relevant voices based on the user's geographical location information, more useful information can be obtained.

[0052] The analysis unit can apply different analysis algorithms depending on the category of audio. For example, a speech recognition algorithm can be applied to conversational audio to provide accurate transcription. A music analysis algorithm can be applied to musical audio to provide song information. Furthermore, an environmental sound analysis algorithm can be applied to environmental sounds to identify the sound source. This allows for more accurate analysis results to be provided by applying different analysis algorithms depending on the category of audio.

[0053] The transmission unit can analyze the user's past hearing history to select an appropriate transmission method. For example, it can analyze the user's hearing test results to identify the user's preferred audio format. It can also analyze past audio data to identify the audio format the user prefers to listen to. It can also identify audio formats the user avoids and select optimal volume and tone. This allows information to be transmitted in a more appropriate manner by selecting the optimal transmission method based on the user's past hearing history.

[0054] The communication unit can customize the means of communication based on the user's current living situation. For example, if the user is driving, voice guidance can be prioritized. If the user is in a meeting, a vibration notification can be used. Furthermore, if the user is relaxed, detailed information can be provided. In this way, by customizing the means of communication according to the user's living situation, information can be communicated in a more appropriate manner.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The collection unit collects ambient sounds. For example, the collection unit can collect sounds such as conversation sounds, environmental sounds, and warning sounds using a microphone. Furthermore, the collection unit can collect sounds from a specific direction using a directional microphone, or can collect sounds from all directions using an omnidirectional microphone. Step 2: The analysis unit analyzes the audio collected by the collection unit. The analysis unit removes noise using digital signal processing and analyzes the audio using a speech recognition algorithm. For example, speech recognition algorithms such as HMM (Hidden Markov Model) and DNN (Deep Neural Network) can be used. The analysis unit can extract audio features and recognize the audio content. It can also analyze the frequency components of the audio to understand the characteristics of the audio. Step 3: The transmission unit transmits the sound analyzed by the analysis unit to the inner ear using bone conduction technology. The transmission unit can transmit sound to the inner ear using a bone conduction transducer. For example, a bone conduction transducer can be attached to the skull or behind the ear to transmit sound to the inner ear as vibrations. This allows the hearing impaired and hard of hearing to hear the sounds around them.

[0057] (Example 2) The AI ​​bone conduction chip system according to an embodiment of the present invention eliminates the inconveniences of hearing impairments and offers a new tomorrow. This system collects ambient sounds, analyzes them using AI, and transmits them directly to the inner ear using bone conduction technology, allowing the hearing-impaired and hard-of-hearing individuals to hear the sounds around them. It also provides multilingual automatic translation and information gathering functions to general users. For example, when a user hears a conversation in a foreign language, the system analyzes the audio, automatically translates it using AI, and transmits it to the inner ear using bone conduction technology. This allows the user to listen to the translated audio in real time. Furthermore, it can also be used as an information gathering device. For example, if a user wants to hear information such as the news or weather forecast, the system collects the information from the Internet, analyzes it using AI, and transmits it to the inner ear using bone conduction technology. This allows the user to obtain the necessary information in real time. Thus, the AI ​​bone conduction chip system is a groundbreaking system that provides a new tomorrow for the hearing-impaired and hard-of-hearing individuals, and can also be used as a multilingual automatic translation and information gathering device for general users. This means that the AI ​​bone conduction chip system will not only enable the deaf and hard of hearing to hear the sounds around them, but will also provide general users with multilingual automatic translation and information gathering functions.

[0058] The AI ​​bone conduction chip system according to the embodiment includes a collection unit, an analysis unit, and a transmission unit. The collection unit collects surrounding sounds. The collection unit can collect surrounding sounds using, for example, a microphone. The collection unit can collect sounds such as conversation sounds, environmental sounds, and warning sounds. The collection unit can also collect sounds from a specific direction using, for example, a directional microphone. The collection unit can also collect sounds from all directions using, for example, an omnidirectional microphone. The analysis unit analyzes the sounds collected by the collection unit. The analysis unit removes noise using, for example, digital signal processing and analyzes the sounds using a speech recognition algorithm. The analysis unit can use speech recognition algorithms such as a hidden Markov model (HMM) or a deep neural network (DNN). The analysis unit can extract speech features and recognize the content of the speech. The analysis unit can also analyze the frequency components of the speech and grasp the characteristics of the speech. The transmission unit transmits the speech analyzed by the analysis unit to the inner ear using bone conduction technology. The transmission unit can transmit sound to the inner ear using, for example, a bone conduction transducer. The transmission unit can transmit sound to the inner ear as vibrations by, for example, attaching a bone conduction transducer to the skull. The transmission unit can also transmit sound to the inner ear as vibrations by, for example, attaching a bone conduction transducer behind the ear. As a result, the AI ​​bone conduction chip system according to the embodiment collects and analyzes surrounding sounds and transmits them to the inner ear using bone conduction technology, allowing people with hearing impairments or hard of hearing to hear surrounding sounds. Some or all of the above-described 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 transmit sound to the inner ear using an AI model that receives sound analyzed by the analysis unit as input and outputs the sound as vibrations.

[0059] The analysis unit can remove noise using digital signal processing and analyze the speech using a speech recognition algorithm. Digital signal processing includes, for example, filtering and noise reduction techniques. The analysis unit can remove noise in a specific frequency band using filtering techniques. For example, the analysis unit can use a low-pass filter to remove low-frequency noise. The analysis unit can also use a high-pass filter to remove high-frequency noise. The analysis unit can also reduce noise from the speech signal using noise reduction techniques. For example, the analysis unit can reduce noise using spectral subtraction techniques. Speech recognition algorithms include, for example, hidden Markov models (HMMs) and deep neural networks (DNNs). The analysis unit can model temporal changes in speech using HMMs to recognize speech content. The analysis unit can also extract speech features using DNNs to perform highly accurate speech recognition. For example, the analysis unit can use DNNs to learn the spectral features of speech and recognize speech content. As a result, the accuracy of speech analysis is improved by removing noise and using a speech recognition algorithm. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may use the voice collected by the collection unit as input and analyze the voice using an AI model that performs noise removal and voice recognition.

[0060] The transmission unit can transmit sound to the inner ear using a bone conduction transducer. Bone conduction transducers include, for example, types attached to the skull and types attached behind the ear. The transmission unit can transmit sound to the inner ear using, for example, a bone conduction transducer attached to the skull. For example, the transmission unit can recognize sound by having a transducer attached to the skull transmit an audio signal as vibration, and the vibration is transmitted to the inner ear. The transmission unit can also transmit sound to the inner ear using a bone conduction transducer attached behind the ear. For example, the transmission unit can recognize sound by having a transducer attached behind the ear transmit an audio signal as vibration, and the vibration is transmitted to the inner ear. In this way, sound can be transmitted directly to the inner ear using a bone conduction transducer. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the transmission unit can transmit sound to the inner ear using an AI model that receives the sound analyzed by the analysis unit as input and outputs the sound as vibration.

[0061] The analysis unit can analyze foreign language speech and automatically translate it using AI. Examples of foreign language speech include English, Chinese, and Spanish. The analysis unit can analyze English speech and automatically translate it using AI. For example, the analysis unit can collect English speech, convert it into text using a speech recognition algorithm, and automatically translate the text using AI. The analysis unit can also analyze Chinese speech and automatically translate it using AI. For example, the analysis unit can collect Chinese speech, convert it into text using a speech recognition algorithm, and automatically translate the text using AI. The analysis unit can also analyze Spanish speech and automatically translate it using AI. For example, the analysis unit can collect Spanish speech, convert it into text using a speech recognition algorithm, and automatically translate the text using AI. Automatic translation using AI includes technologies such as neural machine translation (NMT) and statistical machine translation (SMT). The analysis unit can perform high-precision translation using NMT. ​​For example, the analysis unit can translate foreign language text using a neural network and output the translation result. The analysis unit can also perform translation using SMT. For example, the analysis unit can translate foreign language text using statistical methods and output the translation results. This enables multilingual support by analyzing foreign language speech and automatically translating it. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input foreign language speech and analyze the speech using an AI model that performs speech recognition and translation.

[0062] The transmission unit can transmit the translated audio to the inner ear using bone conduction technology. Translated audio includes, for example, English, Chinese, Spanish, etc. The transmission unit can transmit, for example, audio translated into English to the inner ear using bone conduction technology. For example, the transmission unit can transmit an audio signal translated into English to the inner ear as vibrations. The transmission unit can also transmit audio translated into Chinese to the inner ear using bone conduction technology. For example, the transmission unit can transmit an audio signal translated into Chinese to the inner ear as vibrations. The transmission unit can also transmit audio translated into Spanish to the inner ear using bone conduction technology. For example, the transmission unit can transmit an audio signal translated into Spanish to the inner ear as vibrations. By transmitting the translated audio to the inner ear, the user can hear the translated audio in real time. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the transmission unit can transmit audio to the inner ear using an AI model that receives audio translated by the analysis unit as input and outputs audio as vibrations.

[0063] The analysis unit may use specific means for collecting and analyzing information from the Internet. Examples of methods for collecting information from the Internet include web scraping and the use of APIs. The analysis unit may use web scraping to collect information on the Internet. For example, the analysis unit may collect information such as news articles and weather forecasts from specific websites. The analysis unit may also use APIs to collect information on the Internet. For example, the analysis unit may use a news API or a weather forecast API to collect the latest news and weather forecast information. The collected information is analyzed by the analysis unit. The analysis unit may use natural language processing technology to analyze the collected information. For example, the analysis unit may analyze the content of collected news articles and extract important information. The analysis unit may also analyze collected weather forecast data to provide important information to users. This allows necessary information to be obtained in real time by collecting and analyzing information from the Internet. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can take information collected from the Internet as input and analyze the information using an AI model that analyzes the information.

[0064] The transmission unit can transmit the analyzed information to the inner ear using bone conduction technology. The analyzed information includes, for example, news articles and weather forecasts. The transmission unit can transmit, for example, the content of the analyzed news article to the inner ear using bone conduction technology. For example, the transmission unit can convert the content of the news article into vibrations as an audio signal and transmit the vibrations to the inner ear. The transmission unit can also transmit analyzed weather forecast information to the inner ear using bone conduction technology. For example, the transmission unit can convert weather forecast information into vibrations as an audio signal and transmit the vibrations to the inner ear. In this way, by transmitting the analyzed information to the inner ear, information can be obtained in real time. Some 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 transmit information to the inner ear using an AI model that receives the information analyzed by the analysis unit as input and outputs the information as an audio signal.

[0065] The collection unit can estimate the user's emotion and adjust the timing of collecting audio based on the estimated user's emotion. The collection unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the collection unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The collection unit can also estimate the user's emotion using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice and estimate the user's emotion using a voice analysis algorithm. The collection unit can also estimate the user's emotion using biometric technology. For example, the collection unit can measure the user's heart rate and electrodermal activity with a sensor and estimate the user's emotion using a biometric algorithm. The collection unit adjusts the timing of collecting audio based on the estimated user's emotion. For example, if the user is nervous, the collection unit can delay the timing of collecting audio and wait until the user is relaxed. Furthermore, if the user is relaxed, the collection unit can advance the timing of collecting audio and collect audio immediately. Furthermore, when the user is in a hurry, the collection unit can optimize the timing of voice collection and collect voice quickly. This allows the voice to be collected at a more appropriate time by adjusting the timing of voice collection according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect voice using an AI model that inputs user emotion data and adjusts the timing of voice collection.

[0066] The collection unit can analyze surrounding environmental sounds and select an appropriate collection method. The collection unit can, for example, use a microphone to analyze the surrounding environmental sounds. For example, the collection unit can collect surrounding environmental sounds and analyze the environmental sounds using a sound analysis algorithm. The collection unit can analyze environmental sounds such as traffic sounds, natural sounds, and noise. The collection unit can, for example, analyze traffic sounds and enhance noise canceling functionality. For example, when there is a lot of traffic noise, the collection unit can enhance the noise canceling functionality and collect clear audio. The collection unit can also analyze natural sounds and use a high-sensitivity microphone. For example, when the surroundings are quiet, the collection unit can collect even faint audio using a high-sensitivity microphone. Furthermore, the collection unit can dynamically adjust the collection method when the surrounding sounds fluctuate. For example, when the surrounding sounds fluctuate, the collection unit can dynamically adjust the collection method using AI to collect optimal audio. This allows clear audio to be collected by selecting the optimal collection method depending on the surrounding environmental sounds. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can collect audio using an AI model that inputs ambient environmental sound data and selects the optimal collection method.

[0067] The collection unit can emphasize specific frequency bands when collecting audio. The collection unit can use filtering technology to emphasize specific frequency bands. For example, the collection unit can use a bandpass filter to emphasize frequency bands where human voices are prevalent. For example, the collection unit can emphasize a frequency band from 1 kHz to 4 kHz to clearly collect conversations. The collection unit can also filter specific frequency bands to emphasize specific musical instrument sounds. For example, the collection unit can emphasize a frequency band from 100 Hz to 1 kHz to emphasize the sound of a guitar. Furthermore, the collection unit can emphasize specific frequency bands of environmental sounds. For example, the collection unit can emphasize a wide frequency band from 20 Hz to 20 kHz to realistically collect natural sounds. By emphasizing and collecting specific frequency bands, audio tailored to the purpose can be clearly collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input audio data and collect audio using an AI model that emphasizes specific frequency bands.

[0068] The collection unit can estimate the user's emotion and determine the priority of sounds to be collected based on the estimated user's emotion. The collection unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the collection unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The collection unit can also estimate the user's emotion using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice and estimate the user's emotion using a voice analysis algorithm. The collection unit can also estimate the user's emotion using biometric technology. For example, the collection unit can measure the user's heart rate and electrodermal activity with a sensor and estimate the user's emotion using a biometric algorithm. Based on the estimated user's emotion, the collection unit determines the priority of sounds to be collected. For example, if the user is feeling stressed, the collection unit can prioritize collecting relaxing sounds. If the user is concentrating, the collection unit can prioritize collecting important notification sounds. If the user is having fun, the collection unit can prioritize collecting entertainment-related sounds. This allows for more appropriate audio collection by determining audio priorities according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect audio using an AI model that receives user emotion data as input and determines audio priorities.

[0069] When collecting audio, the collection unit can prioritize collecting highly relevant audio by taking into account the user's geographical location information. The collection unit can, for example, acquire the user's geographical location information using GPS data. For example, the collection unit can acquire GPS data from the user's smartphone or wearable device to identify the user's current location. The collection unit can also acquire the user's geographical location information using a location information service. For example, the collection unit can acquire the user's current location through a location information service and collect audio based on the location information. The collection unit prioritizes collecting highly relevant audio based on the user's geographical location information. For example, if the user is in a tourist destination, the collection unit can prioritize collecting tourist information audio. If the user is in a business area, the collection unit can prioritize collecting business-related audio. Furthermore, if the user is at home, the collection unit can prioritize collecting audio from within the home. This allows for more useful information to be obtained by collecting highly relevant audio based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect audio using an AI model that takes the user's geographic location information as input and prioritizes collecting highly relevant audio.

[0070] When collecting audio, the collection unit can analyze the user's social media activity and collect related audio. For example, the collection unit can collect the user's post content, number of likes, number of followers, etc. to analyze the social media activity. For example, if the user posts music-related content, the collection unit can prioritize collecting music-related audio. Furthermore, if the user posts news-related content, the collection unit can prioritize collecting news-related audio. Furthermore, if the user posts sports-related content, the collection unit can prioritize collecting sports-related audio. This allows for the provision of more personalized information by collecting related audio based on the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can collect audio using an AI model that uses the user's social media data as input and collects related audio.

[0071] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the analysis unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice and estimate the user's emotion using a voice analysis algorithm. The analysis unit can also estimate the user's emotion using biometric technology. For example, the analysis unit can measure the user's heart rate and electrodermal activity with a sensor and estimate the user's emotion using a biometric algorithm. Based on the estimated user's emotion, the analysis unit can adjust the presentation method of the analysis. For example, if the user is relaxed, the analysis unit can provide the analysis result in a calm tone. If the user is in a hurry, the analysis unit can provide the analysis result concisely and quickly. If the user is excited, the analysis unit can provide the analysis result in a visually stimulating format. This allows the analysis result to be provided more appropriately by adjusting the analysis presentation method according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may provide the analysis result using an AI model that inputs the user's emotional data and adjusts the analysis presentation method.

[0072] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the audio. For example, the analysis unit can consider the urgency, relevance, frequency, etc. of the content to evaluate the importance of the audio. For example, the analysis unit can perform a detailed analysis of important conversational audio to provide accurate information. Furthermore, the analysis unit can perform a simplified analysis of background sound to provide the minimum necessary information. Furthermore, the analysis unit can perform an appropriate analysis of notification sounds to provide information quickly. Thus, by adjusting the level of detail of the analysis according to the importance of the audio, necessary information can be appropriately provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can provide analysis results using an AI model that receives audio data and adjusts the level of detail of the analysis based on the importance of the audio.

[0073] The analysis unit can apply different analysis algorithms depending on the category of audio during analysis. For example, the analysis unit can set categories such as news, music, and conversation to classify audio. For example, the analysis unit can apply a speech recognition algorithm to conversational audio to perform accurate transcription. The analysis unit can also apply a music analysis algorithm to musical audio to provide song information. Furthermore, the analysis unit can apply an environmental sound analysis algorithm to environmental sounds to identify the sound source. In this way, by applying different analysis algorithms depending on the category of audio, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can provide analysis results using an AI model that receives audio data and applies different analysis algorithms depending on the category of audio.

[0074] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the analysis unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice and estimate the user's emotion using a voice analysis algorithm. The analysis unit can also estimate the user's emotion using biometric technology. For example, the analysis unit can measure the user's heart rate and electrodermal activity with a sensor and estimate the user's emotion using a biometric algorithm. The analysis unit adjusts the length of the analysis based on the estimated user's emotion. For example, the analysis unit can provide a short and to-the-point analysis result if the user is in a hurry. The analysis unit can provide a detailed analysis result if the user is relaxed. The analysis unit can provide a visually stimulating analysis result if the user is excited. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may provide analysis results using an AI model that inputs user emotion data and adjusts the length of the analysis.

[0075] During analysis, the analysis unit can determine the analysis priority based on when the audio was collected. The analysis unit can, for example, consider the collection date and time, collection frequency, etc. to evaluate when the audio was collected. For example, the analysis unit can prioritize analyzing the most recent audio and provide information in real time. The analysis unit can also prioritize analyzing important audio from the past and provide necessary information. Furthermore, the analysis unit can prioritize analyzing audio collected periodically and provide continuous information. In this way, by determining the analysis priority based on when the audio was collected, necessary information can be provided in real time. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that receives audio data as input and determines the analysis priority based on when the audio was collected.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the audio. The analysis unit can, for example, consider the degree of content similarity or related keywords to evaluate the relevance of the audio. For example, the analysis unit can prioritize analyzing important conversational audio and provide information quickly. The analysis unit can also prioritize analyzing necessary information, leaving background audio for later analysis. Furthermore, the analysis unit can appropriately analyze notification audio and provide information quickly. As a result, important information can be provided quickly by adjusting the order of analysis based on the relevance of the audio. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that receives audio data as input and adjusts the order of analysis based on the relevance of the audio.

[0077] The communication unit can estimate the user's emotion and adjust the communication method based on the estimated user's emotion. The communication unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the communication unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The communication unit can also estimate the user's emotion using voice analysis technology. For example, the communication unit can analyze the tone and speed of the user's voice and estimate the user's emotion using a voice analysis algorithm. The communication unit can also estimate the user's emotion using biometric technology. For example, the communication unit can measure the user's heart rate and electrodermal activity with a sensor and estimate the user's emotion using a biometric algorithm. The communication unit adjusts the communication method based on the estimated user's emotion. For example, the communication unit can communicate information in a calm voice if the user is nervous. The communication unit can provide detailed information if the user is relaxed. The communication unit can also communicate information concisely and quickly if the user is in a hurry. This allows the method of transmission to be adjusted according to the user's emotions, thereby enabling information to be transmitted in a more appropriate manner. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit may transmit information using an AI model that receives user emotion data as input and adjusts the method of transmission.

[0078] When transmitting information, the transmission unit can analyze the user's past hearing history and select an appropriate transmission method. The transmission unit can, for example, collect hearing test results and past audio data to obtain the user's past hearing history. For example, the transmission unit can analyze the user's hearing test results and identify the user's preferred audio format. The transmission unit can also analyze past audio data and identify the user's preferred audio format. The transmission unit can also identify audio formats avoided by the user. For example, the transmission unit can eliminate audio formats avoided by the user in the past and select optimal volume and tone. This allows information to be transmitted in a more appropriate manner by selecting an optimal transmission method based on the user's past hearing history. Some or all of the above-described processing in the transmission unit can be performed, for example, using AI, or can be performed without using AI. For example, the transmission unit can transmit information using an AI model that inputs the user's past hearing history data and selects an appropriate transmission method.

[0079] The transmission unit can customize the means of transmission based on the user's current living situation when transmitting information. The transmission unit can, for example, take into account the user's daily activity patterns and living environment to evaluate the user's current living situation. For example, the transmission unit can prioritize voice guidance when the user is driving. Furthermore, the transmission unit can notify the user by vibration when the user is in a meeting. Furthermore, the transmission unit can provide detailed information when the user is relaxed. This allows information to be transmitted in a more appropriate manner by customizing the means of transmission according to the user's living situation. Some or all of the above-described 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 transmit information using an AI model that uses the user's living situation data as input and customizes the means of transmission.

[0080] The transmission unit can estimate the user's emotion and determine a transmission priority based on the estimated user's emotion. The transmission unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the transmission unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The transmission unit can also estimate the user's emotion using voice analysis technology. For example, the transmission unit can analyze the tone and speed of the user's voice and estimate the user's emotion using a voice analysis algorithm. The transmission unit can also estimate the user's emotion using biometric technology. For example, the transmission unit can measure the user's heart rate and electrodermal activity with a sensor and estimate the user's emotion using a biometric algorithm. The transmission unit determines a transmission priority based on the estimated user's emotion. For example, if the user is feeling stressed, the transmission unit can prioritize transmitting relaxing information. If the user is concentrating, the transmission unit can prioritize transmitting important notifications. If the user is having fun, the transmission unit can prioritize transmitting entertainment-related information. This allows for the transmission of more appropriate information with priority by determining transmission priorities according to the user's emotions. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit may transmit information using an AI model that receives user emotion data as input and determines transmission priorities.

[0081] The transmission unit can select the optimal transmission method by taking into account the user's geographical location information when transmitting information. The transmission unit can, for example, acquire the user's geographical location information using GPS data. For example, the transmission unit can acquire GPS data from the user's smartphone or wearable device to identify the user's current location. The transmission unit can also acquire the user's geographical location information using a location information service. For example, the transmission unit can acquire the user's current location through a location information service and select the optimal transmission method based on the location information. The transmission unit selects the optimal transmission method based on the user's geographical location information. For example, if the user is in a tourist destination, the transmission unit can prioritize transmitting tourist information. Furthermore, if the user is in a business area, the transmission unit can prioritize transmitting business-related information. Furthermore, if the user is at home, the transmission unit can prioritize transmitting information about the home. As a result, by selecting the optimal transmission method based on the user's geographical location information, more appropriate information can be provided. Some or all of the above-described 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 transmit information using an AI model that inputs the user's geographical location information and selects the optimal transmission method.

[0082] The transmission unit can analyze the user's social media activity and suggest a means of transmission when transmitting the information. For example, the transmission unit can collect the user's post content, the number of likes, the number of followers, etc., to analyze the social media activity. For example, if the user posts music-related information, the transmission unit can prioritize transmitting music-related information. Furthermore, if the user posts news-related information, the transmission unit can prioritize transmitting news-related information. Furthermore, if the user posts sports-related information, the transmission unit can prioritize transmitting sports-related information. This allows for more personalized information to be provided by suggesting a means of transmission based on the user's social media activity. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the transmission unit can transmit information using an AI model that inputs the user's social media data and suggests a means of transmission. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and transmission unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect ambient sounds using the microphone 38B of the smart device 14. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected sounds by digital signal processing. The transmission unit can be realized, for example, by the control unit 46A of the smart device 14, and can transmit the analyzed sounds to the inner ear using a bone conduction transducer. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and transmission unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect ambient sound using the microphone 238 of the smart glasses 214. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected sound by digital signal processing. The transmission unit can be realized, for example, by the control unit 46A of the smart glasses 214, and can transmit the analyzed sound to the inner ear using a bone conduction transducer. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis 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 can collect ambient sound using the microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected sound by digital signal processing. The transmission unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and can transmit the analyzed sound to the inner ear using a bone conduction transducer. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis 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 can collect surrounding sounds using the microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected sounds by digital signal processing. The transmission unit is realized, for example, by the control unit 46A of the robot 414, and can transmit the analyzed sounds to the inner ear using a bone conduction transducer.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The analysis unit can estimate the user's emotions and adjust audio filtering based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can emphasize relaxing audio and reduce noise. If the user is concentrating, the analysis unit can emphasize important audio and suppress background noise. If the user is having fun, the analysis unit can emphasize entertainment-related audio and filter other audio. This allows the system to provide more appropriate audio by adjusting audio filtering according to the user's emotions.

[0085] The collection unit can detect the user's activity status and select an appropriate audio collection mode. For example, if the user is exercising, the collection unit can select an audio collection mode suitable for exercise and emphasize ambient sounds. If the user is reading, the collection unit can select an audio collection mode suitable for a quiet environment and reduce noise. If the user is in a meeting, the collection unit can select an audio collection mode suitable for the meeting and emphasize conversation sounds. Thus, by selecting an appropriate audio collection mode according to the user's activity status, clearer audio can be collected.

[0086] The analysis unit can estimate the user's emotions and adjust the accuracy of the speech translation based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can increase translation accuracy and reduce mistranslations. Also, if the user is relaxed, the analysis unit can prioritize translation speed and provide translation results quickly. Furthermore, if the user is excited, the analysis unit can visually emphasize the translation results and provide them in a format that is easy for the user to understand. This allows for more appropriate translation results to be provided by adjusting the translation accuracy according to the user's emotions.

[0087] The communication unit can estimate the user's emotions and customize the voice communication method based on the estimated user's emotions. For example, if the user is feeling stressed, the communication unit can communicate information in a calm voice. If the user is relaxed, the communication unit can provide detailed information. If the user is in a hurry, the communication unit can communicate information concisely and quickly. Thus, by customizing the voice communication method according to the user's emotions, information can be communicated in a more appropriate manner.

[0088] The collection unit can estimate the user's emotions and determine the priority of sounds to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting relaxing sounds. Also, if the user is concentrating, the collection unit can prioritize collecting important notification sounds. Furthermore, if the user is having fun, the collection unit can prioritize collecting entertainment-related sounds. In this way, by prioritizing sounds according to the user's emotions, more appropriate sounds can be collected.

[0089] The analysis unit can determine the priority of analysis based on the time when the audio was collected. For example, it can prioritize analysis of the most recent audio and provide information in real time. It can also prioritize analysis of important audio from the past and provide necessary information. It can also prioritize analysis of audio collected periodically and provide continuous information. Thus, by determining the priority of analysis based on the time when the audio was collected, it is possible to provide necessary information in real time.

[0090] The collection unit can prioritize collection of highly relevant voices in consideration of the user's geographical location information. For example, if the user is in a tourist spot, it can prioritize collection of tourist information voices. Also, if the user is in a business area, it can prioritize collection of business-related voices. Furthermore, if the user is at home, it can prioritize collection of voices from within the home. In this way, by collecting highly relevant voices based on the user's geographical location information, more useful information can be obtained.

[0091] The analysis unit can apply different analysis algorithms depending on the category of audio. For example, a speech recognition algorithm can be applied to conversational audio to provide accurate transcription. A music analysis algorithm can be applied to musical audio to provide song information. Furthermore, an environmental sound analysis algorithm can be applied to environmental sounds to identify the sound source. This allows for more accurate analysis results to be provided by applying different analysis algorithms depending on the category of audio.

[0092] The transmission unit can analyze the user's past hearing history to select an appropriate transmission method. For example, it can analyze the user's hearing test results to identify the user's preferred audio format. It can also analyze past audio data to identify the audio format the user prefers to listen to. It can also identify audio formats the user avoids and select optimal volume and tone. This allows information to be transmitted in a more appropriate manner by selecting the optimal transmission method based on the user's past hearing history.

[0093] The communication unit can customize the means of communication based on the user's current living situation. For example, if the user is driving, voice guidance can be prioritized. If the user is in a meeting, a vibration notification can be used. Furthermore, if the user is relaxed, detailed information can be provided. In this way, by customizing the means of communication according to the user's living situation, information can be communicated in a more appropriate manner.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The collection unit collects ambient sounds. For example, the collection unit can collect sounds such as conversation sounds, environmental sounds, and warning sounds using a microphone. Furthermore, the collection unit can collect sounds from a specific direction using a directional microphone, or can collect sounds from all directions using an omnidirectional microphone. Step 2: The analysis unit analyzes the audio collected by the collection unit. The analysis unit removes noise using digital signal processing and analyzes the audio using a speech recognition algorithm. For example, speech recognition algorithms such as HMM (Hidden Markov Model) and DNN (Deep Neural Network) can be used. The analysis unit can extract audio features and recognize the audio content. It can also analyze the frequency components of the audio to understand the characteristics of the audio. Step 3: The transmission unit transmits the sound analyzed by the analysis unit to the inner ear using bone conduction technology. The transmission unit can transmit sound to the inner ear using a bone conduction transducer. For example, a bone conduction transducer can be attached to the skull or behind the ear to transmit sound to the inner ear as vibrations. This allows the hearing impaired and hard of hearing to hear the sounds around them.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] 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.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] 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.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] 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.

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

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

[0143] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] 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.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0159] 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.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0167] [Explanation of symbols]

[0168] 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 surrounding sounds; an analysis unit that analyzes the voice collected by the collection unit; a transmitting unit that transmits the sound analyzed by the analyzing unit to the inner ear using bone conduction technology; Equipped with A system characterized by:

2. The analysis unit Digital signal processing is used to remove noise, and speech is analyzed using a speech recognition algorithm.

2. The system of claim 1.

3. The transmission unit is A bone conduction transducer is used to transmit sound to the inner ear 2. The system of claim 1.

4. The analysis unit Analyze foreign language speech and automatically translate it using AI 2. The system of claim 1.

5. The transmission unit is Translated audio is transmitted to the inner ear using bone conduction technology 2. The system of claim 1.

6. The analysis unit Use specific means to collect and analyze information from the Internet 2. The system of claim 1.

7. The transmission unit is The analyzed information is transmitted to the inner ear using bone conduction technology.

2. The system of claim 1.

8. The collecting unit Estimate the user's emotions and adjust the timing of voice collection based on the estimated user emotions.

2. The system of claim 1.

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