System

Abstract

To provide a system capable of providing external sound after converting the same into the best sound information for a user.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a conversion unit, and a providing unit. The collection unit collects sound information. The analysis unit analyzes sound information collected by the collection unit. The conversion unit converts the sound information analyzed by the analysis unit. The providing unit provides the sound information converted by the conversion unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional techniques have not adequately converted external sounds into optimal sound information for the user, and there is room for improvement.

[0005] The system according to the embodiment aims to convert external sounds into optimal sound information for the user and provide the converted sound information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a conversion unit, and a provision unit. The collection unit collects sound information. The analysis unit analyzes the sound information collected by the collection unit. The conversion unit converts the sound information analyzed by the analysis unit. The provision unit provides the sound information converted by the conversion unit. Effect of the Invention

[0007] The system according to the embodiment can convert external sounds into optimal sound information for the user and provide the converted sound information. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The sound information providing system according to the embodiment of the present invention is a system that combines a generation AI, a device such as an earphone for external sound capture, and 5G communication to provide optimal sound information for a user. In this system, an earphone for external sound capture collects surrounding sounds, and the collected sounds are transmitted to the generation AI through 5G communication. The generation AI analyzes the collected sounds and converts them into optimal sound information for the user. For example, in an educational setting, the voice of a teacher that is difficult to hear in terms of tone of voice, etc., is converted into a voice that is easy to hear. In addition, for the hearing impaired, important sound information such as a siren is converted into other sensory information such as visual information and delivered to them. This mechanism can solve the problems of sound in the world. For example, in an educational setting, the teacher's voice becomes easier to hear, improving learning effects. In addition, for the hearing impaired, important sound information can be received through other sensory information such as visual information, improving safety. As a result, the sound information providing system can provide optimal sound information for the user.

[0029] The sound information providing system according to the embodiment includes a collection unit, an analysis unit, a conversion unit, and a provision unit. The collection unit collects sound information. The collection unit can collect sound information using, for example, earphones that capture external sound. The earphones that capture external sound can collect surrounding sounds with high accuracy. For example, in an educational setting, the teacher's voice is collected. Next, the collected sound information is transmitted to the analysis unit through 5G communication. Since 5G communication is high speed and has low latency, sound information can be transmitted in real time. For example, when the teacher's voice is collected, it is immediately transmitted to the analysis unit. The analysis unit analyzes the collected sound information. The analysis unit can analyze the sound information using, for example, voice recognition technology or frequency analysis technology. For example, if the teacher's voice is difficult to hear, the analysis unit analyzes the voice and generates information for converting it into a voice that is easy to hear. The conversion unit converts the sound information analyzed by the analysis unit. The conversion unit can convert the sound information using, for example, a generation AI. The generation AI can adjust the tone and frequency of the voice. For example, if a teacher's voice is difficult to hear, the generation AI analyzes the voice and converts it into an easier-to-hear voice. In addition, important sound information such as a siren can be converted into visual information for the hearing impaired. For example, the sound of a siren can be notified by flashing lights or vibrations. The providing unit provides the sound information converted by the converting unit. The providing unit can provide the sound information through, for example, a speaker. It can also be transmitted to a digital device. This allows the sound information providing system to provide optimal sound information for the user. For example, in educational settings, the teacher's voice becomes easier to hear, improving learning outcomes. In addition, important sound information can be received by other sensory information such as vision for the hearing impaired, improving safety.

[0030] The collection unit collects sound information. The collection unit can collect sound information using, for example, earphones that capture external sound. The earphones that capture external sound can collect surrounding sounds with high accuracy. Specifically, the earphones that capture external sound have multiple microphones built in, and these microphones capture surrounding sounds three-dimensionally. This allows the direction and distance of the sound to be accurately grasped. For example, in an educational setting, the voice of a teacher is collected. Since the teacher's voice is often affected by noise and reverberation in the classroom, high-precision collection is required. The earphones that capture external sound can remove these noises and collect the teacher's voice clearly. Next, the collected sound information is transmitted to the analysis unit through 5G communication. Since 5G communication is high-speed and low-latency, sound information can be transmitted in real time. For example, when the teacher's voice is collected, it is immediately transmitted to the analysis unit. This minimizes the delay in sound information and enables real-time analysis. Furthermore, the collection unit can adjust the collection range and sensitivity of the sound information. For example, it is possible to emphasize sounds in a specific frequency band or preferentially collect sounds from a specific direction. This allows the collection unit to flexibly respond to various environments and situations.

[0031] The analysis unit analyzes the collected sound information. The analysis unit can analyze the sound information using, for example, voice recognition technology or frequency analysis technology. Specifically, the collected voice data is converted into text data using voice recognition technology. This allows the content of the voice to be analyzed as text information. In addition, the frequency analysis technology can be used to analyze the frequency components of the voice and extract specific voice patterns and features. For example, if the teacher's voice is difficult to hear, the analysis unit analyzes the voice and generates information for converting it into a voice that is easy to hear. Specifically, the frequency components of the voice are analyzed and filtering information for removing noise and reverberation is generated. In addition, information for adjusting the tone and pitch of the voice can also be generated. Furthermore, the analysis unit can classify the collected sound information and detect specific sound events or situations. For example, it can detect siren sounds or alarm sounds in an emergency and provide information for responding quickly. This allows the analysis unit to analyze the collected sound information from multiple angles and provide information that is useful to the user.

[0032] The conversion unit converts the sound information analyzed by the analysis unit. The conversion unit can convert the sound information using, for example, a generation AI. The generation AI can adjust the tone and frequency of the voice. Specifically, the generation AI learns the characteristics of the voice and builds a model to convert it into a voice that is easy to hear. For example, if the teacher's voice is difficult to hear, the generation AI analyzes the voice and converts it into a voice that is easy to hear. Specifically, the generation AI removes noise from the voice and converts it into a clear voice. The generation AI can also adjust the tone and pitch of the voice and convert it into a voice that is easy to hear. Furthermore, the generation AI can adjust the speed and rhythm of the voice. This enables voice conversion according to the needs of the user. In addition, important sound information such as a siren can be converted into visual information for the hearing impaired. For example, the sound of a siren can be notified by flashing lights or vibrations. Specifically, the generation AI analyzes the voice data and detects a specific sound event. Then, it generates visual information or a vibration pattern corresponding to the sound event. This allows the hearing impaired to receive important sound information visually and tactilely. This allows the conversion unit to provide sound information in an optimal form for the user.

[0033] The providing unit provides the sound information converted by the converting unit. The providing unit can provide the sound information through, for example, a speaker. Specifically, the providing unit transmits the converted voice data to a speaker and reproduces clear voice. Transmission to a digital device is also possible. For example, the voice data is transmitted to a smartphone or tablet so that the user can listen to the voice individually. This allows the sound information providing system to provide optimal sound information for the user. For example, in an educational setting, the teacher's voice becomes easier to hear, improving learning effectiveness. Specifically, clear voice can be delivered to everyone through a speaker in the classroom. In addition, by using an individual device, it is also possible to provide individual voice guidance to specific students. Furthermore, for hearing-impaired people, important sound information can be received through other sensory information such as vision, improving safety. Specifically, visual information is displayed on a smartphone or tablet to notify important voice events. In addition, information can be conveyed tactilely using a vibration device. This allows the providing unit to provide sound information to users in a variety of ways, enabling flexible response according to the needs of the user.

[0034] The conversion unit can adjust the tone or frequency of the voice. The conversion unit can, for example, adjust the tone of the voice. For example, the pitch or timbre of the voice can be adjusted. The conversion unit can also adjust the frequency. For example, the low frequency or high frequency can be adjusted. By adjusting the tone or frequency of the voice, the voice can be converted into a voice that is easy to hear. Some or all of the above-mentioned processing in the conversion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs voice data to a generation AI, and the generation AI can adjust the tone or frequency of the voice.

[0035] The conversion unit can convert sound information into visual information. The conversion unit can, for example, convert sound information into text. For example, the conversion unit can convert voice data into text data. The conversion unit can also convert sound information into images. For example, the conversion unit can convert voice data into image data. Furthermore, the conversion unit can also convert sound information into video. For example, the conversion unit can convert voice data into video data. As a result, important information can be provided to the hearing impaired by converting sound information into visual information. A part or all of the above-mentioned processing in the conversion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input voice data into a generation AI, and the generation AI can convert the voice data into text data or image data.

[0036] The conversion unit can convert sound information into haptic information. The conversion unit can convert sound information into a vibration pattern, for example. For example, voice data can be converted into a vibration pattern. The conversion unit can also convert sound information into haptic feedback, for example, voice data can be converted into haptic feedback. As a result, important information can be provided to the hearing impaired by converting sound information into haptic information. A part or all of the above-mentioned processing in the conversion unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the conversion unit can input voice data into a generation AI, and the generation AI can convert the voice data into a vibration pattern or haptic feedback.

[0037] The providing unit can provide the converted sound information to the user. The providing unit can provide the sound information through, for example, a speaker. For example, the converted sound information can be played on the speaker. The providing unit can also transmit to a digital device. For example, the converted sound information can be transmitted to a smartphone or a tablet. As a result, the converted sound information can be provided to the user, so that the user can receive optimal sound information. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the converted sound information to the generation AI, and the generation AI can determine how to provide the sound information to the user.

[0038] The collection unit can collect sound information using earphones that capture external sounds. The collection unit can collect sound information using, for example, earphones that capture external sounds. The earphones that capture external sounds can collect surrounding sounds with high accuracy. For example, in an educational setting, the voice of a teacher is collected. The earphones that capture external sounds may have a noise canceling function, and important sound information can be collected by removing surrounding noise. As a result, sound information can be collected with high accuracy by using the earphones that capture external sounds. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input sound information collected by the earphones that capture external sounds to the generation AI, and the generation AI can optimize the method of collecting sound information.

[0039] The collection unit can analyze the surrounding environmental sound in real time and collect important sound information preferentially. The collection unit can, for example, analyze the surrounding environmental sound in real time and collect the siren sound of an emergency vehicle preferentially. The collection unit can also collect conversation sounds preferentially. Furthermore, the collection unit can also collect alarm sounds preferentially. Thereby, important sound information can be collected preferentially by analyzing the surrounding environmental sound in real time. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the surrounding environmental sound data to the generation AI, and the generation AI can collect important sound information preferentially.

[0040] The collection unit can collect sound information by emphasizing a specific frequency band. For example, the collection unit can collect sound information by emphasizing a low frequency band. For example, the heavy bass can be emphasized. The collection unit can also collect sound information by emphasizing a mid-frequency band. For example, conversation sounds can be made clear. Furthermore, the collection unit can also collect sound information by emphasizing a high frequency band. For example, warning sounds can be emphasized. As a result, important sound information can be collected by emphasizing a specific frequency band. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input sound information data to the generation AI, and the generation AI can collect the sound information by emphasizing a specific frequency band.

[0041] When collecting sound information, the collection unit can preferentially collect highly relevant sound information in consideration of the geographical location information of the user. For example, when the user is at school, the collection unit can preferentially collect the voice of the teacher. In addition, when the user is at a hospital, the collection unit can also preferentially collect the alarm sound of a medical device. Furthermore, when the user is at a park, the collection unit can also preferentially collect natural sounds. In this way, by considering the geographical location information of the user, highly relevant sound information can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the geographical location information of the user to the generation AI, and the generation AI can preferentially collect highly relevant sound information.

[0042] The collection unit can analyze the user's social media activities and collect related sound information when collecting sound information. For example, when the user posts about music on social media, the collection unit can preferentially collect music-related sound information. In addition, when the user posts about an event on social media, the collection unit can also preferentially collect event-related sound information. Furthermore, when the user posts about news on social media, the collection unit can also preferentially collect news-related sound information. In this way, the user's social media activities can be analyzed to collect related sound information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's social media data to the generation AI, and the generation AI can collect related sound information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the sound information during the analysis. For example, the analysis unit can perform a detailed analysis on important sound information (e.g., siren sound). The analysis unit can also perform a standard analysis on general sound information (e.g., environmental sound). The analysis unit can also perform a simplified analysis on unnecessary sound information (e.g., noise). This allows important sound information to be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the sound information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input sound information data to the generation AI, which can then adjust the level of detail of the analysis based on the importance of the sound information.

[0044] The analysis unit can apply different analysis algorithms depending on the category of sound information during analysis. For example, the analysis unit can apply a voice recognition algorithm to voice information. The analysis unit can also apply an environmental sound analysis algorithm to environmental sounds. Furthermore, the analysis unit can apply a warning sound analysis algorithm to warning sounds. This allows more appropriate analysis to be performed by applying different analysis algorithms depending on the category of sound information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs sound information data to a generation AI, and the generation AI can apply different analysis algorithms depending on the category of sound information.

[0045] The analysis unit can determine the priority of the analysis based on the time when the sound information was collected during the analysis. The analysis unit can, for example, preferentially analyze the latest sound information. The analysis unit can also analyze the current sound information while referring to past sound information. Furthermore, the analysis unit can also preferentially analyze sound information collected in a specific time period. As a result, the analysis priority can be determined based on the time when the sound information was collected, so that the latest sound information can be preferentially analyzed. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input sound information data to the generation AI, and the generation AI can determine the priority of the analysis based on the time when the sound information was collected.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the sound information during analysis. The analysis unit can, for example, prioritize analysis of highly relevant sound information. The analysis unit can also prioritize analysis of less relevant sound information. Furthermore, the analysis unit can evaluate the relevance of the sound information in real time and dynamically adjust the order of analysis. As a result, by adjusting the order of analysis based on the relevance of the sound information, highly relevant sound information can be analyzed with priority. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input sound information data to a generation AI, and the generation AI can adjust the order of analysis based on the relevance of the sound information.

[0047] The conversion unit can adjust the level of detail of the conversion based on the importance of the sound information during conversion. For example, the conversion unit can perform detailed conversion on important sound information (e.g., siren sound). The conversion unit can also perform standard conversion on general sound information (e.g., environmental sound). The conversion unit can also perform simplified conversion on unnecessary sound information (e.g., noise). This allows important sound information to be converted in detail by adjusting the level of detail of the conversion based on the importance of the sound information. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input sound information data to the generation AI, and the generation AI can adjust the level of detail of the conversion based on the importance of the sound information.

[0048] The conversion unit can apply different conversion algorithms depending on the category of sound information at the time of conversion. For example, the conversion unit can apply a voice conversion algorithm to voice information. The conversion unit can also apply an environmental sound conversion algorithm to environmental sound. Furthermore, the conversion unit can apply a warning sound conversion algorithm to warning sounds. This allows more appropriate conversion to be performed by applying different conversion algorithms depending on the category of sound information. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs sound information data to a generation AI, and the generation AI can apply different conversion algorithms depending on the category of sound information.

[0049] The conversion unit can determine the priority of conversion based on the time when the sound information was collected during conversion. The conversion unit can, for example, preferentially convert the latest sound information. The conversion unit can also convert the current sound information while referring to past sound information. Furthermore, the conversion unit can also preferentially convert sound information collected in a specific time period. As a result, the conversion priority can be determined based on the time when the sound information was collected, so that the latest sound information can be preferentially converted. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input sound information data to the generation AI, and the generation AI can determine the priority of conversion based on the time when the sound information was collected.

[0050] The conversion unit can adjust the order of conversion based on the relevance of the sound information at the time of conversion. The conversion unit can, for example, preferentially convert highly relevant sound information. The conversion unit can also convert less relevant sound information later. Furthermore, the conversion unit can evaluate the relevance of the sound information in real time and dynamically adjust the order of conversion. As a result, by adjusting the order of conversion based on the relevance of the sound information, highly relevant sound information can be preferentially converted. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input sound information data to a generation AI, and the generation AI can adjust the order of conversion based on the relevance of the sound information.

[0051] The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit can preferentially select a providing method that the user has used in the past. The providing unit can also adjust the optimal volume and tone from the user's past usage history and provide it. Furthermore, the providing unit can also select the optimal providing method based on the time period and situation in which the user used it in the past. In this way, the optimal providing method can be selected by referring to the user's past usage history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past usage history data to the generation AI, and the generation AI can select the optimal providing method.

[0052] The providing unit can customize the means of provision based on the user's current living situation at the time of provision. For example, when the user is exercising, the providing unit can convert audio information into vibration or visual information and provide it. In addition, the providing unit can provide the sound at a low volume when the user is in a quiet environment. Furthermore, the providing unit can provide the sound at a high volume when the user is in a noisy environment. In this way, more appropriate sound information can be provided by customizing the means of provision based on the user's current living situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's current living situation data into the generation AI, and the generation AI can customize the means of provision.

[0053] The providing unit can select the optimal providing method in consideration of the geographical location information of the user at the time of providing. For example, when the user is at school, the providing unit can provide the teacher's voice in a clear manner. In addition, when the user is at a hospital, the providing unit can also provide the alarm sound of a medical device in an emphasized manner. Furthermore, when the user is in a park, the providing unit can also provide the natural sound in an emphasized manner. In this way, the optimal providing method can be selected by considering the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the geographical location information of the user to the generation AI, and the generation AI can select the optimal providing method.

[0054] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. For example, when the user posts about music on social media, the providing unit can provide music-related sound information. In addition, when the user posts about an event on social media, the providing unit can also provide event-related sound information. Furthermore, when the user posts about news on social media, the providing unit can also provide news-related sound information. In this way, by analyzing the user's social media activity, it is possible to provide related sound information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's social media data to the generation AI, which can then suggest a means of provision.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing method that the user has used with preference in the past can be preferentially selected. Also, the optimal volume and tone can be adjusted and provided based on the user's past usage history. Furthermore, the optimal providing method can be selected based on the time period and situation used by the user in the past. In this way, the optimal providing method can be selected by referring to the user's past usage history.

[0057] When collecting sound information, the collection unit can prioritize collection of highly relevant sound information by taking into consideration the geographical location information of the user. For example, when the user is at school, the voice of the teacher can be prioritized for collection. Also, when the user is at a hospital, the alarm sound of a medical device can be prioritized for collection. Furthermore, when the user is at a park, natural sounds can be prioritized for collection. In this way, by taking into consideration the geographical location information of the user, highly relevant sound information can be prioritized for collection.

[0058] The conversion unit can adjust the level of detail of the conversion based on the importance of the sound information during conversion. For example, detailed conversion can be performed for important sound information (e.g., siren sound). Standard conversion can be performed for general sound information (e.g., environmental sound). Furthermore, simplified conversion can be performed for unnecessary sound information (e.g., noise). As a result, important sound information can be converted into detailed information by adjusting the level of detail of the conversion based on the importance of the sound information.

[0059] During analysis, the analysis unit can apply different analysis algorithms depending on the category of sound information. For example, a voice recognition algorithm can be applied to voice information. Also, an environmental sound analysis algorithm can be applied to environmental sounds. Furthermore, a warning sound analysis algorithm can be applied to warning sounds. In this way, by applying different analysis algorithms depending on the category of sound information, more appropriate analysis can be performed.

[0060] The providing unit can customize the means of providing based on the user's current living situation at the time of providing. For example, when the user is exercising, audio information can be provided by converting it into vibration or visual information. In addition, when the user is in a quiet environment, the volume can be lowered. Furthermore, when the user is in a noisy environment, the volume can be raised. In this way, by customizing the means of providing based on the user's current living situation, more appropriate sound information can be provided.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects sound information. The collection unit can collect sound information using, for example, earphones that capture ambient sound. Earphones that capture ambient sound can collect surrounding sounds with high accuracy. For example, in an educational setting, the teacher's voice is collected. The collected sound information is then transmitted to the analysis unit via 5G communication. 5G communication is fast and has low latency, so sound information can be transmitted in real time. For example, once the teacher's voice is collected, it is immediately transmitted to the analysis unit. Step 2: The analysis unit analyzes the collected sound information. The analysis unit can analyze the sound information using, for example, voice recognition technology or frequency analysis technology. For example, if the teacher's voice is difficult to hear, the analysis unit analyzes the voice and generates information to convert it into a voice that is easy to hear. Step 3: The conversion unit converts the sound information analyzed by the analysis unit. The conversion unit can convert the sound information using, for example, a generation AI. The generation AI can adjust the tone and frequency of the voice. For example, if the teacher's voice is difficult to hear, the generation AI can analyze the voice and convert it into an easier-to-listen voice. In addition, for the hearing impaired, important sound information such as a siren can be converted into visual information. For example, the sound of a siren can be notified by flashing lights or vibrations. Step 4: The providing unit provides the sound information converted by the converting unit. The providing unit can provide the sound information through a speaker, for example. It can also transmit the sound information to a digital device. This allows the sound information providing system to provide optimal sound information to the user. For example, in educational settings, it becomes easier to hear the teacher's voice, improving learning outcomes. Also, for people with hearing impairments, important sound information can be received through other sensory information, such as vision, improving safety.

[0063] (Example 2) The sound information providing system according to the embodiment of the present invention is a system that combines a generation AI, a device such as an earphone for external sound capture, and 5G communication to provide optimal sound information for a user. In this system, an earphone for external sound capture collects surrounding sounds, and the collected sounds are transmitted to the generation AI through 5G communication. The generation AI analyzes the collected sounds and converts them into optimal sound information for the user. For example, in an educational setting, the voice of a teacher that is difficult to hear in terms of tone of voice, etc., is converted into a voice that is easy to hear. In addition, for the hearing impaired, important sound information such as a siren is converted into other sensory information such as visual information and delivered to them. This mechanism can solve the problems of sound in the world. For example, in an educational setting, the teacher's voice becomes easier to hear, improving learning effects. In addition, for the hearing impaired, important sound information can be received through other sensory information such as visual information, improving safety. As a result, the sound information providing system can provide optimal sound information for the user.

[0064] The sound information providing system according to the embodiment includes a collection unit, an analysis unit, a conversion unit, and a provision unit. The collection unit collects sound information. The collection unit can collect sound information using, for example, earphones that capture external sound. The earphones that capture external sound can collect surrounding sounds with high accuracy. For example, in an educational setting, the teacher's voice is collected. Next, the collected sound information is transmitted to the analysis unit through 5G communication. Since 5G communication is high speed and has low latency, sound information can be transmitted in real time. For example, when the teacher's voice is collected, it is immediately transmitted to the analysis unit. The analysis unit analyzes the collected sound information. The analysis unit can analyze the sound information using, for example, voice recognition technology or frequency analysis technology. For example, if the teacher's voice is difficult to hear, the analysis unit analyzes the voice and generates information for converting it into a voice that is easy to hear. The conversion unit converts the sound information analyzed by the analysis unit. The conversion unit can convert the sound information using, for example, a generation AI. The generation AI can adjust the tone and frequency of the voice. For example, if a teacher's voice is difficult to hear, the generation AI analyzes the voice and converts it into an easier-to-hear voice. In addition, important sound information such as a siren can be converted into visual information for the hearing impaired. For example, the sound of a siren can be notified by flashing lights or vibrations. The providing unit provides the sound information converted by the converting unit. The providing unit can provide the sound information through, for example, a speaker. It can also be transmitted to a digital device. This allows the sound information providing system to provide optimal sound information for the user. For example, in educational settings, the teacher's voice becomes easier to hear, improving learning outcomes. In addition, important sound information can be received by other sensory information such as vision for the hearing impaired, improving safety.

[0065] The collection unit collects sound information. The collection unit can collect sound information using, for example, earphones that capture external sound. The earphones that capture external sound can collect surrounding sounds with high accuracy. Specifically, the earphones that capture external sound have multiple microphones built in, and these microphones capture surrounding sounds three-dimensionally. This allows the direction and distance of the sound to be accurately grasped. For example, in an educational setting, the voice of a teacher is collected. Since the teacher's voice is often affected by noise and reverberation in the classroom, high-precision collection is required. The earphones that capture external sound can remove these noises and collect the teacher's voice clearly. Next, the collected sound information is transmitted to the analysis unit through 5G communication. Since 5G communication is high-speed and low-latency, sound information can be transmitted in real time. For example, when the teacher's voice is collected, it is immediately transmitted to the analysis unit. This minimizes the delay in sound information and enables real-time analysis. Furthermore, the collection unit can adjust the collection range and sensitivity of the sound information. For example, it is possible to emphasize sounds in a specific frequency band or preferentially collect sounds from a specific direction. This allows the collection unit to flexibly respond to various environments and situations.

[0066] The analysis unit analyzes the collected sound information. The analysis unit can analyze the sound information using, for example, voice recognition technology or frequency analysis technology. Specifically, the collected voice data is converted into text data using voice recognition technology. This allows the content of the voice to be analyzed as text information. In addition, the frequency analysis technology can be used to analyze the frequency components of the voice and extract specific voice patterns and features. For example, if the teacher's voice is difficult to hear, the analysis unit analyzes the voice and generates information for converting it into a voice that is easy to hear. Specifically, the frequency components of the voice are analyzed and filtering information for removing noise and reverberation is generated. In addition, information for adjusting the tone and pitch of the voice can also be generated. Furthermore, the analysis unit can classify the collected sound information and detect specific sound events or situations. For example, it can detect siren sounds or alarm sounds in an emergency and provide information for responding quickly. This allows the analysis unit to analyze the collected sound information from multiple angles and provide information that is useful to the user.

[0067] The conversion unit converts the sound information analyzed by the analysis unit. The conversion unit can convert the sound information using, for example, a generation AI. The generation AI can adjust the tone and frequency of the voice. Specifically, the generation AI learns the characteristics of the voice and builds a model to convert it into a voice that is easy to hear. For example, if the teacher's voice is difficult to hear, the generation AI analyzes the voice and converts it into a voice that is easy to hear. Specifically, the generation AI removes noise from the voice and converts it into a clear voice. The generation AI can also adjust the tone and pitch of the voice and convert it into a voice that is easy to hear. Furthermore, the generation AI can adjust the speed and rhythm of the voice. This enables voice conversion according to the needs of the user. In addition, important sound information such as a siren can be converted into visual information for the hearing impaired. For example, the sound of a siren can be notified by flashing lights or vibrations. Specifically, the generation AI analyzes the voice data and detects a specific sound event. Then, it generates visual information or a vibration pattern corresponding to the sound event. This allows the hearing impaired to receive important sound information visually and tactilely. This allows the conversion unit to provide sound information in an optimal form for the user.

[0068] The providing unit provides the sound information converted by the converting unit. The providing unit can provide the sound information through, for example, a speaker. Specifically, the providing unit transmits the converted voice data to a speaker and reproduces clear voice. Transmission to a digital device is also possible. For example, the voice data is transmitted to a smartphone or tablet so that the user can listen to the voice individually. This allows the sound information providing system to provide optimal sound information for the user. For example, in an educational setting, the teacher's voice becomes easier to hear, improving learning effectiveness. Specifically, clear voice can be delivered to everyone through a speaker in the classroom. In addition, by using an individual device, it is also possible to provide individual voice guidance to specific students. Furthermore, for hearing-impaired people, important sound information can be received through other sensory information such as vision, improving safety. Specifically, visual information is displayed on a smartphone or tablet to notify important voice events. In addition, information can be conveyed tactilely using a vibration device. This allows the providing unit to provide sound information to users in a variety of ways, enabling flexible response according to the needs of the user.

[0069] The conversion unit can adjust the tone or frequency of the voice. The conversion unit can, for example, adjust the tone of the voice. For example, the pitch or timbre of the voice can be adjusted. The conversion unit can also adjust the frequency. For example, the low frequency or high frequency can be adjusted. By adjusting the tone or frequency of the voice, the voice can be converted into a voice that is easy to hear. Some or all of the above-mentioned processing in the conversion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs voice data to a generation AI, and the generation AI can adjust the tone or frequency of the voice.

[0070] The conversion unit can convert sound information into visual information. The conversion unit can, for example, convert sound information into text. For example, the conversion unit can convert voice data into text data. The conversion unit can also convert sound information into images. For example, the conversion unit can convert voice data into image data. Furthermore, the conversion unit can also convert sound information into video. For example, the conversion unit can convert voice data into video data. As a result, important information can be provided to the hearing impaired by converting sound information into visual information. A part or all of the above-mentioned processing in the conversion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input voice data into a generation AI, and the generation AI can convert the voice data into text data or image data.

[0071] The conversion unit can convert sound information into haptic information. The conversion unit can convert sound information into a vibration pattern, for example. For example, voice data can be converted into a vibration pattern. The conversion unit can also convert sound information into haptic feedback, for example, voice data can be converted into haptic feedback. As a result, important information can be provided to the hearing impaired by converting sound information into haptic information. A part or all of the above-mentioned processing in the conversion unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the conversion unit can input voice data into a generation AI, and the generation AI can convert the voice data into a vibration pattern or haptic feedback.

[0072] The providing unit can provide the converted sound information to the user. The providing unit can provide the sound information through, for example, a speaker. For example, the converted sound information can be played on the speaker. The providing unit can also transmit to a digital device. For example, the converted sound information can be transmitted to a smartphone or a tablet. As a result, the converted sound information can be provided to the user, so that the user can receive optimal sound information. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the converted sound information to the generation AI, and the generation AI can determine how to provide the sound information to the user.

[0073] The collection unit can collect sound information using earphones that capture external sounds. The collection unit can collect sound information using, for example, earphones that capture external sounds. The earphones that capture external sounds can collect surrounding sounds with high accuracy. For example, in an educational setting, the voice of a teacher is collected. The earphones that capture external sounds may have a noise canceling function, and important sound information can be collected by removing surrounding noise. As a result, sound information can be collected with high accuracy by using the earphones that capture external sounds. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input sound information collected by the earphones that capture external sounds to the generation AI, and the generation AI can optimize the method of collecting sound information.

[0074] The collection unit can estimate the user's emotion and adjust the collection timing of the sound information based on the estimated emotion of the user. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The collection unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, more appropriate sound information can be collected by adjusting the collection timing of the sound information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input a user's emotional data to the generation AI, and the generation AI can adjust the timing of collecting sound information.

[0075] The collection unit can analyze the surrounding environmental sound in real time and collect important sound information preferentially. The collection unit can, for example, analyze the surrounding environmental sound in real time and collect the siren sound of an emergency vehicle preferentially. The collection unit can also collect conversation sounds preferentially. Furthermore, the collection unit can also collect alarm sounds preferentially. Thereby, important sound information can be collected preferentially by analyzing the surrounding environmental sound in real time. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the surrounding environmental sound data to the generation AI, and the generation AI can collect important sound information preferentially.

[0076] The collection unit can collect sound information by emphasizing a specific frequency band. For example, the collection unit can collect sound information by emphasizing a low frequency band. For example, the heavy bass can be emphasized. The collection unit can also collect sound information by emphasizing a mid-frequency band. For example, conversation sounds can be made clear. Furthermore, the collection unit can also collect sound information by emphasizing a high frequency band. For example, warning sounds can be emphasized. As a result, important sound information can be collected by emphasizing a specific frequency band. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input sound information data to the generation AI, and the generation AI can collect the sound information by emphasizing a specific frequency band.

[0077] The collection unit can estimate the user's emotion and determine the priority of the sound information to be collected based on the estimated user's emotion. The collection unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The collection unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, the priority of the sound information to be collected according to the user's emotion is determined, so that more appropriate sound information can be collected. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input a user's emotional data to the generation AI and determine the priority of the sound information to be collected by the generation AI.

[0078] When collecting sound information, the collection unit can preferentially collect highly relevant sound information in consideration of the geographical location information of the user. For example, when the user is at school, the collection unit can preferentially collect the voice of the teacher. In addition, when the user is at a hospital, the collection unit can also preferentially collect the alarm sound of a medical device. Furthermore, when the user is at a park, the collection unit can also preferentially collect natural sounds. In this way, by considering the geographical location information of the user, highly relevant sound information can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the geographical location information of the user to the generation AI, and the generation AI can preferentially collect highly relevant sound information.

[0079] The collection unit can analyze the user's social media activities and collect related sound information when collecting sound information. For example, when the user posts about music on social media, the collection unit can preferentially collect music-related sound information. In addition, when the user posts about an event on social media, the collection unit can also preferentially collect event-related sound information. Furthermore, when the user posts about news on social media, the collection unit can also preferentially collect news-related sound information. In this way, the user's social media activities can be analyzed to collect related sound information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's social media data to the generation AI, and the generation AI can collect related sound information.

[0080] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The analysis unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in facial expression. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. This allows for more appropriate analysis by adjusting the accuracy of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a user's emotional data into the generation AI, which can then adjust the accuracy of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the sound information during the analysis. For example, the analysis unit can perform a detailed analysis on important sound information (e.g., siren sound). The analysis unit can also perform a standard analysis on general sound information (e.g., environmental sound). The analysis unit can also perform a simplified analysis on unnecessary sound information (e.g., noise). This allows important sound information to be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the sound information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input sound information data to the generation AI, which can then adjust the level of detail of the analysis based on the importance of the sound information.

[0082] The analysis unit can apply different analysis algorithms depending on the category of sound information during analysis. For example, the analysis unit can apply a voice recognition algorithm to voice information. The analysis unit can also apply an environmental sound analysis algorithm to environmental sounds. Furthermore, the analysis unit can apply a warning sound analysis algorithm to warning sounds. This allows more appropriate analysis to be performed by applying different analysis algorithms depending on the category of sound information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs sound information data to a generation AI, and the generation AI can apply different analysis algorithms depending on the category of sound information.

[0083] The analysis unit can estimate the user's emotion and determine the priority of analysis based on the estimated user's emotion. The analysis unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in facial expression. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the tone and speed of the voice are analyzed to calculate the emotion score. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, important sound information can be preferentially analyzed by determining the priority of analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a user's emotional data into the generation AI, which can then determine the priorities of analysis.

[0084] The analysis unit can determine the priority of the analysis based on the time when the sound information was collected during the analysis. The analysis unit can, for example, preferentially analyze the latest sound information. The analysis unit can also analyze the current sound information while referring to past sound information. Furthermore, the analysis unit can also preferentially analyze sound information collected in a specific time period. As a result, the analysis priority can be determined based on the time when the sound information was collected, so that the latest sound information can be preferentially analyzed. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input sound information data to the generation AI, and the generation AI can determine the priority of the analysis based on the time when the sound information was collected.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the sound information during analysis. The analysis unit can, for example, prioritize analysis of highly relevant sound information. The analysis unit can also prioritize analysis of less relevant sound information. Furthermore, the analysis unit can evaluate the relevance of the sound information in real time and dynamically adjust the order of analysis. As a result, by adjusting the order of analysis based on the relevance of the sound information, highly relevant sound information can be analyzed with priority. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input sound information data to a generation AI, and the generation AI can adjust the order of analysis based on the relevance of the sound information.

[0086] The conversion unit can estimate the user's emotion and adjust the expression method of the conversion based on the estimated user's emotion. The conversion unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The conversion unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the conversion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, more appropriate sound information can be provided by adjusting the expression method of the conversion according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input a user's emotional data into the generation AI, which can then adjust the way the conversion is expressed.

[0087] The conversion unit can adjust the level of detail of the conversion based on the importance of the sound information during conversion. For example, the conversion unit can perform detailed conversion on important sound information (e.g., siren sound). The conversion unit can also perform standard conversion on general sound information (e.g., environmental sound). The conversion unit can also perform simplified conversion on unnecessary sound information (e.g., noise). This allows important sound information to be converted in detail by adjusting the level of detail of the conversion based on the importance of the sound information. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input sound information data to the generation AI, and the generation AI can adjust the level of detail of the conversion based on the importance of the sound information.

[0088] The conversion unit can apply different conversion algorithms depending on the category of sound information at the time of conversion. For example, the conversion unit can apply a voice conversion algorithm to voice information. The conversion unit can also apply an environmental sound conversion algorithm to environmental sound. Furthermore, the conversion unit can apply a warning sound conversion algorithm to warning sounds. This allows more appropriate conversion to be performed by applying different conversion algorithms depending on the category of sound information. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs sound information data to a generation AI, and the generation AI can apply different conversion algorithms depending on the category of sound information.

[0089] The conversion unit can estimate the user's emotion and adjust the length of the conversion based on the estimated user's emotion. The conversion unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The conversion unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the conversion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, more appropriate sound information can be provided by adjusting the length of the conversion according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input the user's emotion data to the generation AI, which can then adjust the length of the conversion.

[0090] The conversion unit can determine the priority of conversion based on the time when the sound information was collected during conversion. The conversion unit can, for example, preferentially convert the latest sound information. The conversion unit can also convert the current sound information while referring to past sound information. Furthermore, the conversion unit can also preferentially convert sound information collected in a specific time period. As a result, the conversion priority can be determined based on the time when the sound information was collected, so that the latest sound information can be preferentially converted. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input sound information data to the generation AI, and the generation AI can determine the priority of conversion based on the time when the sound information was collected.

[0091] The conversion unit can adjust the order of conversion based on the relevance of the sound information at the time of conversion. The conversion unit can, for example, preferentially convert highly relevant sound information. The conversion unit can also convert less relevant sound information later. Furthermore, the conversion unit can evaluate the relevance of the sound information in real time and dynamically adjust the order of conversion. As a result, by adjusting the order of conversion based on the relevance of the sound information, highly relevant sound information can be preferentially converted. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input sound information data to a generation AI, and the generation AI can adjust the order of conversion based on the relevance of the sound information.

[0092] The providing unit can estimate the user's emotion and adjust the method of providing based on the estimated user's emotion. The providing unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed to calculate the emotion score. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, more appropriate sound information can be provided by adjusting the method of providing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's emotional data to the generation AI, which can then adjust the method of providing.

[0093] The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit can preferentially select a providing method that the user has used in the past. The providing unit can also adjust the optimal volume and tone from the user's past usage history and provide it. Furthermore, the providing unit can also select the optimal providing method based on the time period and situation in which the user used it in the past. In this way, the optimal providing method can be selected by referring to the user's past usage history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past usage history data to the generation AI, and the generation AI can select the optimal providing method.

[0094] The providing unit can customize the means of provision based on the user's current living situation at the time of provision. For example, when the user is exercising, the providing unit can convert audio information into vibration or visual information and provide it. In addition, the providing unit can provide the sound at a low volume when the user is in a quiet environment. Furthermore, the providing unit can provide the sound at a high volume when the user is in a noisy environment. In this way, more appropriate sound information can be provided by customizing the means of provision based on the user's current living situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's current living situation data into the generation AI, and the generation AI can customize the means of provision.

[0095] The providing unit can estimate the user's emotion and determine the priority of provision based on the estimated user's emotion. The providing unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed to calculate the emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, important sound information can be preferentially provided by determining the priority of provision according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of provision.

[0096] The providing unit can select the optimal providing method in consideration of the geographical location information of the user at the time of providing. For example, when the user is at school, the providing unit can provide the teacher's voice in a clear manner. In addition, when the user is at a hospital, the providing unit can also provide the alarm sound of a medical device in an emphasized manner. Furthermore, when the user is in a park, the providing unit can also provide the natural sound in an emphasized manner. In this way, the optimal providing method can be selected by considering the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the geographical location information of the user to the generation AI, and the generation AI can select the optimal providing method.

[0097] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. For example, when the user posts about music on social media, the providing unit can provide music-related sound information. In addition, when the user posts about an event on social media, the providing unit can also provide event-related sound information. Furthermore, when the user posts about news on social media, the providing unit can also provide news-related sound information. In this way, by analyzing the user's social media activity, it is possible to provide related sound information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's social media data to the generation AI, which can then suggest a means of provision.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit can increase the analysis accuracy of the sound information to provide more detailed information. When the user is relaxed, the analysis unit can lower the analysis accuracy to provide simple information. Furthermore, when the user is excited, the analysis unit can adjust the analysis accuracy to a medium level to provide moderate information. This allows for more appropriate analysis by adjusting the analysis accuracy according to the user's emotion.

[0100] The providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing method that the user has used with preference in the past can be preferentially selected. Also, the optimal volume and tone can be adjusted and provided based on the user's past usage history. Furthermore, the optimal providing method can be selected based on the time period and situation used by the user in the past. In this way, the optimal providing method can be selected by referring to the user's past usage history.

[0101] The conversion unit can estimate the user's emotion and adjust the conversion expression method based on the estimated user's emotion. For example, if the user is sad, the conversion unit can convert the sound information into a calm tone. Also, if the user is happy, the conversion unit can convert the sound information into a bright tone. Furthermore, if the user is angry, the conversion unit can convert the sound information into a calm tone. In this way, more appropriate sound information can be provided by adjusting the conversion expression method according to the user's emotion.

[0102] When collecting sound information, the collection unit can prioritize collection of highly relevant sound information by taking into consideration the geographical location information of the user. For example, when the user is at school, the voice of the teacher can be prioritized for collection. Also, when the user is at a hospital, the alarm sound of a medical device can be prioritized for collection. Furthermore, when the user is at a park, natural sounds can be prioritized for collection. In this way, by taking into consideration the geographical location information of the user, highly relevant sound information can be prioritized for collection.

[0103] The providing unit can estimate the user's emotion and adjust the method of providing based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide the sound information in a relaxing manner. Also, if the user is tired, the providing unit can provide the sound information in an uplifting manner. Furthermore, if the user is concentrating, the providing unit can provide the sound information in a non-disturbing manner. In this way, by adjusting the method of providing according to the user's emotion, more appropriate sound information can be provided.

[0104] The conversion unit can adjust the level of detail of the conversion based on the importance of the sound information during conversion. For example, detailed conversion can be performed for important sound information (e.g., siren sound). Standard conversion can be performed for general sound information (e.g., environmental sound). Furthermore, simplified conversion can be performed for unnecessary sound information (e.g., noise). As a result, important sound information can be converted into detailed information by adjusting the level of detail of the conversion based on the importance of the sound information.

[0105] The collection unit can estimate the user's emotion and adjust the timing of collecting sound information based on the estimated emotion of the user. For example, when the user is relaxed, the collection unit can delay the timing of collecting sound information. Also, when the user is tense, the collection unit can advance the timing of collecting sound information. Furthermore, when the user is concentrating, the collection unit can not adjust the timing of collecting sound information. In this way, more appropriate sound information can be collected by adjusting the timing of collecting sound information according to the user's emotion.

[0106] During analysis, the analysis unit can apply different analysis algorithms depending on the category of sound information. For example, a voice recognition algorithm can be applied to voice information. Also, an environmental sound analysis algorithm can be applied to environmental sounds. Furthermore, a warning sound analysis algorithm can be applied to warning sounds. In this way, by applying different analysis algorithms depending on the category of sound information, more appropriate analysis can be performed.

[0107] The providing unit can customize the means of providing based on the user's current living situation at the time of providing. For example, when the user is exercising, audio information can be provided by converting it into vibration or visual information. In addition, when the user is in a quiet environment, the volume can be lowered. Furthermore, when the user is in a noisy environment, the volume can be raised. In this way, by customizing the means of providing based on the user's current living situation, more appropriate sound information can be provided.

[0108] The conversion unit can estimate the user's emotion and adjust the length of the conversion based on the estimated user's emotion. For example, if the user is impatient, the conversion unit can shorten the conversion of the sound information. Also, if the user is relaxed, the conversion unit can lengthen the conversion of the sound information. Furthermore, if the user is concentrating, the conversion unit can adjust the conversion of the sound information to a moderate degree. In this way, more appropriate sound information can be provided by adjusting the length of the conversion according to the user's emotion.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects sound information. The collection unit can collect sound information using, for example, earphones that capture ambient sound. Earphones that capture ambient sound can collect surrounding sounds with high accuracy. For example, in an educational setting, the teacher's voice is collected. The collected sound information is then transmitted to the analysis unit via 5G communication. 5G communication is fast and has low latency, so sound information can be transmitted in real time. For example, once the teacher's voice is collected, it is immediately transmitted to the analysis unit. Step 2: The analysis unit analyzes the collected sound information. The analysis unit can analyze the sound information using, for example, voice recognition technology or frequency analysis technology. For example, if the teacher's voice is difficult to hear, the analysis unit analyzes the voice and generates information to convert it into a voice that is easy to hear. Step 3: The conversion unit converts the sound information analyzed by the analysis unit. The conversion unit can convert the sound information using, for example, a generation AI. The generation AI can adjust the tone and frequency of the voice. For example, if the teacher's voice is difficult to hear, the generation AI can analyze the voice and convert it into an easier-to-listen voice. In addition, for the hearing impaired, important sound information such as a siren can be converted into visual information. For example, the sound of a siren can be notified by flashing lights or vibrations. Step 4: The providing unit provides the sound information converted by the converting unit. The providing unit can provide the sound information through a speaker, for example. It can also transmit the sound information to a digital device. This allows the sound information providing system to provide optimal sound information to the user. For example, in educational settings, it becomes easier to hear the teacher's voice, improving learning outcomes. Also, for people with hearing impairments, important sound information can be received through other sensory information, such as vision, improving safety.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] For example, the collection unit can collect sound information using the camera 42 or microphone 38B of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected sound information. For example, the conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the sound information using a generation AI. For example, the provision unit can provide the sound information through the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] For example, the collection unit can collect sound information using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected sound information. For example, the conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the sound information using a generation AI. For example, the provision unit can provide the sound information through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] 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.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] For example, the collection unit can collect sound information using the camera 42 or microphone 238 of the headset type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected sound information. For example, the conversion unit is realized by the specific processing unit 290 of the data processing device 12, and converts the sound information using a generation AI. For example, the provision unit can provide the sound information through the speaker 240 of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] For example, the collecting unit can collect sound information using the camera 42 or microphone 238 of the robot 414. For example, the analyzing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected sound information. For example, the converting unit is realized by the specific processing unit 290 of the data processing device 12, and converts the sound information using a generating AI. For example, the providing unit can provide the sound information through the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection unit that collects sound information; an analysis unit that analyzes the sound information collected by the collection unit; A conversion unit that converts the sound information analyzed by the analysis unit; a providing unit that provides the sound information converted by the converting unit. A system characterized by: (Appendix 2) The conversion unit is Adjust the tone or frequency of your audio 2. The system of claim 1, (Appendix 3) The conversion unit is Converting sound information into visual information 2. The system of claim 1, (Appendix 4) The conversion unit is Converting sound information into tactile information 2. The system of claim 1, (Appendix 5) The providing unit is Providing the converted sound information to the user 2. The system of claim 1, (Appendix 6) The collecting unit includes: Collecting sound information using earphones that capture external sounds 2. The system of claim 1, (Appendix 7) The collecting unit includes: To estimate a user's emotion and adjust the timing of collecting sound information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyzes surrounding environmental sounds in real time and prioritizes collection of important sound information 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting sound information, specific frequency bands are emphasized. 2. The system of claim 1, (Appendix 10) The collecting unit includes: The user's emotion is estimated, and the priority of the sound information to be collected is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting sound information, the system takes into account the user's geographic location information and prioritizes collection of highly relevant sound information. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting sound information, the user's social media activity is analyzed to collect related sound information. 2. The system of claim 1, (Appendix 13) The analysis unit is Estimate user emotions and adjust analysis accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of analysis detail based on the importance of sound information 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of sound information 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate user emotions and determine analysis priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when sound information was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, the order of analysis is adjusted based on the relevance of the sound information 2. The system of claim 1, (Appendix 19) The conversion unit is Infer user emotions and adjust the way the transformation is expressed based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The conversion unit is During conversion, adjust the conversion detail level based on the importance of the sound information 2. The system of claim 1, (Appendix 21) The conversion unit is During conversion, different conversion algorithms are applied depending on the category of sound information. 2. The system of claim 1, (Appendix 22) The conversion unit is Inferring a user's emotion and adjusting the length of the transformation based on the estimated user's emotion 2. The system of claim 1, (Appendix 23) The conversion unit is During conversion, conversion priorities are determined based on when sound information was collected. 2. The system of claim 1, (Appendix 24) The conversion unit is During conversion, adjust the conversion order based on the relevance of the sound information 2. The system of claim 1, (Appendix 25) The providing unit is Inferring user emotion and adjusting the method of serving based on the estimated user emotion 2. The system of claim 1, (Appendix 26) The providing unit is When providing the service, the optimal method of providing the service is selected by referring to the user's past usage history. 2. The system of claim 1, (Appendix 27) The providing unit is At the time of delivery, customize the delivery vehicle based on the user's current life situation 2. The system of claim 1, (Appendix 28) The providing unit is Estimate user sentiment and determine priority of offerings based on the estimated user sentiment 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, the optimal method of providing the information is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The providing unit is At the time of delivery, we analyze your social media activity to suggest delivery methods. 2. The system of claim 1, [Explanation of symbols]

[0183] 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 sound information; an analysis unit that analyzes the sound information collected by the collection unit; A conversion unit that converts the sound information analyzed by the analysis unit; a providing unit that provides the sound information converted by the converting unit. A system characterized by:

2. The conversion unit is Adjust the tone or frequency of your audio 2. The system of claim 1 .

3. The conversion unit is Converting sound information into visual information 2. The system of claim 1 .

4. The conversion unit is Converting sound information into tactile information 2. The system of claim 1 .

5. The collecting unit includes: Collecting sound information using earphones that capture external sounds 2. The system of claim 1 .

6. The collecting unit includes: To estimate a user's emotion and adjust the timing of collecting sound information based on the estimated user's emotion.

2. The system of claim 1 .

7. The collecting unit includes: Analyzes surrounding environmental sounds in real time and prioritizes collection of important sound information 2. The system of claim 1 .

8. The collecting unit includes: When collecting sound information, specific frequency bands are emphasized.

2. The system of claim 1 .

9. The collecting unit includes: The user's emotion is estimated, and the priority of the sound information to be collected is determined based on the estimated user's emotion.

2. The system of claim 1 .

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