system

Abstract

To provide a system according to an embodiment which removes surrounding noise and provides information based on location information.SOLUTION: A system according to an embodiment includes a sound collection part, a removal part, a sound transmission part, a location information acquisition part, and an information providing part. The sound collection part collects surrounding sounds of a user. The removal part removes noise from the sound collected by the sound collection part. The sound transmission part transmits to the user sound from which the noise has been removed by the removal part by bone conduction. The location information acquisition part acquires the user's location information. The information providing part provides information according to the location information acquired by the location information acquisition part by transmitting it to the user from the sound transmission part by bone conduction.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had problems such as difficulty in hearing voices in noisy environments and insufficient provision of information based on location information.

[0005] The system according to the embodiment aims to remove ambient noise and provide location-based information. [Means for solving the problem]

[0006] The system according to the embodiment includes a sound collection unit, a removal unit, an audio transmission unit, a position information acquisition unit, and an information provision unit. The sound collection unit collects sounds around the user. The removal unit removes noise from the sounds collected by the sound collection unit. The audio transmission unit transmits the sounds from which the noise has been removed by the removal unit to the user via bone conduction. The position information acquisition unit acquires position information of the user. The information provision unit provides information corresponding to the position information acquired by the position information acquisition unit by transmitting it to the user via bone conduction from the audio transmission unit. [Effects of the Invention]

[0007] Embodiments of the system can filter out ambient noise and provide location-based information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a device equipped with bone conduction and AI. This device aggregates surrounding sounds while filtering out noise and delivers the sound directly to the brain when surrounding conversations are difficult to hear. The system collects sounds around the user, filters out noise, and transmits the sounds to the user via bone conduction. It also provides location information, traffic information (such as weather and traffic congestion information), and sales information when the user is in a retail store. This eliminates the need to look at flyers or check information on a mobile phone beforehand. For example, the system includes a sound collection unit that collects sounds around the user. This sound collection unit collects surrounding sounds using a microphone or other device. Next, it includes a noise removal unit that removes noise from the collected sounds. This noise removal unit uses AI to analyze and remove noise. For example, it can filter out surrounding noise and extract only the sound of conversation. The removed sounds are then transmitted to the user via bone conduction. The audio transmission unit uses bone conduction technology to transmit sound directly to the brain through the user's skull. This allows the user to hear sounds without blocking their ears. Furthermore, it includes a location information acquisition unit that acquires the user's location information. This location information is acquired using technologies such as GPS. The information providing unit provides information based on the acquired location information. For example, if a user is in a specific location, information related to that location is provided. The information providing unit uses AI to provide traffic information, such as local weather information and traffic congestion information. For example, if the weather worsens while the user is out, the AI ​​analyzes the weather information and notifies the user. Providing traffic congestion information also allows the user to avoid traffic jams. Furthermore, the information providing unit uses AI to provide sale information when the user is in a retail store or other store. For example, if the user is in a supermarket, the AI ​​analyzes the store's sale information and notifies the user. This allows the user to obtain advantageous information in real time. In this way, the present invention improves user convenience by using bone conduction and AI to aggregate surrounding sounds while filtering out noise and provide information based on location information and surrounding conditions. This allows the system to efficiently collect sounds around the user, filter out noise, transmit sounds, acquire location information, and provide information.

[0029] The system according to the embodiment includes a sound collection unit, a removal unit, an audio transmission unit, a location information acquisition unit, and an information provision unit. The sound collection unit collects sounds around the user. Examples of sounds around the user include, but are not limited to, environmental sounds, conversation sounds, and traffic sounds. The sound collection unit collects the surrounding sounds using, for example, a microphone. Examples of microphones include directional microphones, omnidirectional microphones, and noise-canceling microphones. The removal unit removes noise from the collected sounds. The removal unit analyzes and removes noise using AI. For example, the removal unit removes noise using frequency filtering or a noise reduction algorithm. For example, the removal unit filters the surrounding noise and extracts only the conversation sounds. The audio transmission unit transmits the sound from which the noise has been removed by the removal unit to the user via bone conduction. The audio transmission unit transmits the sound directly to the brain through the user's skull using bone conduction technology. Bone conduction technology includes bone conduction earphones and bone conduction headsets. The location information acquisition unit acquires the user's location information. The location information acquisition unit acquires location information using technologies such as GPS. GPS includes dual-band GPS and assisted GPS (A-GPS). The information provision unit provides information according to the location information acquired by the location information acquisition unit. The information provision unit uses AI to provide surrounding weather information and traffic information such as traffic congestion information. For example, if the weather turns bad while the user is out, the AI ​​analyzes the weather information and notifies the user. The information provision unit also uses AI to provide sale information when the user is in a retail store or other store. For example, if the user is in a supermarket, the AI ​​analyzes the store's sale information and notifies the user. This allows the system to efficiently collect sounds around the user, remove noise, transmit voice, acquire location information, and provide information.

[0030] The sound collection unit collects sounds around the user. Examples of sounds around the user include, but are not limited to, environmental sounds, conversation sounds, and traffic sounds. The sound collection unit collects the surrounding sounds using, for example, a microphone. Microphones include directional microphones, omnidirectional microphones, and noise-canceling microphones. Specifically, directional microphones focus on collecting sounds from a specific direction, while omnidirectional microphones collect sounds evenly from all directions. Noise-canceling microphones reduce ambient noise and collect clear audio. These microphones may be built into the user's device or connected as an external device. The sound collection unit converts the collected sounds into digital signals in real time and transmits them to a central database. Furthermore, the sound collection unit can flexibly respond to specific situations and environments by adjusting the frequency and sensitivity of sound collection. For example, by collecting sounds at high sensitivity in a quiet room and at low sensitivity in a noisy outdoor environment, it is possible to efficiently collect only the necessary sounds. This allows the sound collection unit to collect high-quality audio data in a variety of environments, improving the performance of the entire system.

[0031] The noise removal unit removes noise from the collected audio. It uses AI to analyze and remove noise. Specifically, it uses frequency filtering and noise reduction algorithms. Frequency filtering removes unwanted noise by emphasizing or suppressing specific frequency bands. The noise reduction algorithm analyzes the collected audio data and identifies and removes noise components. AI uses these algorithms to process the audio data in real time to generate clear audio. For example, it can filter out ambient noise and extract only the conversational sounds. Furthermore, AI learns from past audio data and noise patterns to achieve more accurate noise removal. This allows the noise removal unit to effectively remove unwanted noise from the collected audio data and provide users with clear audio. Furthermore, the noise removal unit can select the optimal noise removal algorithm depending on the environment and situation. For example, it can apply mild noise reduction in quiet indoor environments and strong noise reduction in noisy outdoor environments. This allows the noise removal unit to always maintain optimal audio quality and support a comfortable audio experience for users.

[0032] The audio transmission unit transmits the sound from which the noise has been removed by the noise removal unit to the user via bone conduction. The audio transmission unit uses bone conduction technology to transmit sound directly to the brain through the user's skull. Bone conduction includes bone conduction earphones and bone conduction headsets. Specifically, bone conduction earphones are worn around the ears and transmit sound through vibrations. This allows the user to hear external sounds without blocking the ears, improving safety. Bone conduction headsets are worn on the head and similarly transmit sound through vibrations. This allows for comfortable listening even when used for long periods of time. The audio transmission unit can transmit clear audio processed by the noise removal unit to the user without causing discomfort. Furthermore, the audio transmission unit has a function to adjust volume and sound quality, allowing for an audio experience tailored to the user's preferences. For example, the audio transmission unit may be equipped with a function to automatically adjust volume or an equalizer function to emphasize bass and treble. This allows the audio transmission unit to provide the user with high-quality audio and a comfortable audio experience.

[0033] The location information acquisition unit acquires user location information. The location information acquisition unit acquires location information using technologies such as GPS. GPS includes dual-band GPS and assisted GPS (A-GPS). Specifically, dual-band GPS is a technology that acquires highly accurate location information using multiple frequency bands, and assisted GPS (A-GPS) is a technology that improves the acquisition speed of location information by using mobile phone base station information. By combining these technologies, the location information acquisition unit can acquire highly accurate location information in real time, both indoors and outdoors. Furthermore, the location information acquisition unit has a function to adjust the frequency and accuracy of location information acquisition, allowing it to efficiently acquire necessary information while minimizing battery consumption. For example, by acquiring location information more frequently while moving and less frequently while stationary, battery consumption can be reduced. This allows the location information acquisition unit to acquire user location information accurately and efficiently, improving overall system performance.

[0034] The information providing unit provides information according to the location information acquired by the location information acquiring unit. The information providing unit uses AI to provide traffic information, such as local weather information and traffic congestion information. Specifically, the AI ​​analyzes local weather information based on the location information and notifies the user. For example, if the weather worsens while the user is out, the AI ​​analyzes the weather information and notifies the user. The information providing unit also uses AI to provide sale information when the user is in a retail store or other store. For example, if the user is in a supermarket, the AI ​​analyzes the store's sale information and notifies the user. Furthermore, the information providing unit can learn the user's past behavioral history and preferences to provide more personalized information. For example, if the user prefers a particular brand of product, it will prioritize sale information for that brand. The information providing unit can also provide optimal information to the user based on information updated in real time. For example, if traffic congestion information is updated, the AI ​​calculates the optimal detour route and notifies the user. This allows the information providing unit to always provide the user with the latest information and support a comfortable lifestyle.

[0035] The information providing unit can provide at least one of surrounding weather information and traffic congestion information using AI. The information providing unit, for example, provides surrounding weather information using AI. For example, AI analyzes weather forecast data and provides weather information to the user. The information providing unit can also provide traffic congestion information using AI. For example, AI analyzes traffic data and provides traffic congestion information to the user. In this way, the information providing unit can provide surrounding weather information and traffic congestion information by using AI. Some or all of the above-mentioned processing in the information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs weather forecast data and traffic data into AI and outputs the analysis results.

[0036] The information providing unit can provide sale information when the user is in a retail store or other store using AI. The information providing unit, for example, uses AI to provide sale information for the retail store. For example, AI analyzes sale data for the store and provides the sale information to the user. The information providing unit can also use AI to provide sale information for other stores. For example, AI analyzes sale data for the store and provides the sale information to the user. In this way, the information providing unit can provide sale information when the user is in a retail store or other store by using AI. Some or all of the above-described processing in the information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs sale data for the store into AI and outputs the analysis results.

[0037] The sound collection unit can collect ambient sounds using a microphone. The sound collection unit collects ambient sounds using, for example, a microphone. Microphones include directional microphones, omnidirectional microphones, and noise-canceling microphones. For example, a directional microphone is suitable for collecting sounds from a specific direction. An omnidirectional microphone is suitable for collecting sounds from all directions. A noise-canceling microphone is suitable for removing ambient noise and collecting clear sounds. As a result, the sound collection unit collects ambient sounds using a microphone, thereby improving the accuracy of sound collection. Some or all of the above-described processing in the sound collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the sound collection unit can collect sounds using an AI model that inputs sound data collected by the microphone into AI and outputs analysis results.

[0038] The removal unit can filter ambient noise and extract only the conversation sound. The removal unit can remove ambient noise using, for example, frequency filtering. For example, it can filter sounds in a specific frequency band and extract only the conversation sound. The removal unit can also remove noise using a noise reduction algorithm. For example, it can analyze and remove noise using AI. As a result, the removal unit can filter ambient noise and extract only the conversation sound, thereby improving the clarity of the voice. Some or all of the above-mentioned processing in the removal unit can be performed using, for example, AI, or can be performed without using AI. For example, the removal unit can remove noise using an AI model that inputs collected sound data into AI and outputs the analysis results of noise removal.

[0039] The location information acquisition unit can acquire location information using GPS technology. The location information acquisition unit acquires location information using, for example, GPS. GPS includes dual-band GPS and auxiliary GPS (A-GPS). For example, dual-band GPS acquires location information using multiple frequency bands, resulting in high accuracy. Assisted GPS (A-GPS) uses a mobile phone network to acquire location information even in places where the GPS signal is weak. This improves the accuracy of acquiring location information by using GPS technology. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can acquire location information using an AI model that inputs GPS data into AI and outputs analysis results.

[0040] When collecting sounds, the sound collection unit can analyze the user's past sound environment history and select the optimal collection method. The sound collection unit, for example, refers to past recording data to analyze the user's past sound environment history. For example, it prioritizes collecting sounds that the user liked to listen to in the past. The sound collection unit can also filter sounds that the user avoided in the past so that they are not collected. Furthermore, the sound collection unit can collect sounds suitable for a specific time period from the user's past sound environment history. For example, it selects the optimal collection method based on sounds the user listened to in a specific time period in the past. In this way, the sound collection unit can select the optimal sound collection method by analyzing the user's past sound environment history. Some or all of the above-described processing in the sound collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the sound collection unit can collect sounds using an AI model that inputs past recording data into AI and outputs analysis results.

[0041] The sound collection unit can perform filtering based on the user's current activity status when collecting sounds. The sound collection unit uses, for example, an activity sensor to grasp the user's current activity status. For example, when the user is exercising, the activity sensor detects the movement and prioritizes collecting sounds related to the user's safety. Furthermore, when the user is reading, the activity sensor can detect the user's stationary state and filter out unnecessary sounds to maintain a quiet environment. Furthermore, when the user is in a meeting, the activity sensor can detect conversation, prioritize collecting conversation sounds, and remove noise. This enables the sound collection unit to perform filtering based on the user's current activity status, thereby enabling appropriate sound collection. Some or all of the above-described processing in the sound collection unit may be performed using, for example, AI, or may be performed without AI. For example, the sound collection unit can collect sounds using an AI model that inputs data from the activity sensor into AI and outputs analysis results.

[0042] When collecting sounds, the sound collection unit can prioritize collecting highly relevant sounds by taking into account the user's geographical location information. The sound collection unit, for example, uses GPS to acquire the user's geographical location information. For example, when the user is in a park, the GPS detects the location of the park and prioritizes collecting natural sounds. Furthermore, when the user is in a city, the sound collection unit can also detect the city location by using the GPS and prioritize collecting traffic sounds and people's voices. Furthermore, when the user is at home, the sound collection unit can detect the home location by using the GPS and prioritize collecting sounds from home appliances and voices of family members. This allows the sound collection unit to prioritize collecting highly relevant sounds by taking into account the user's geographical location information. Some or all of the above-described processing in the sound collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the sound collection unit can collect sounds using an AI model that inputs GPS data into AI and outputs analysis results.

[0043] The sound collection unit can analyze the user's social media activity and collect related sounds when collecting sounds. The sound collection unit, for example, refers to social media data to analyze the user's social media activity. For example, if the user posts about music, the sound collection unit prioritizes collecting that music. Also, if the user is participating in an event, the sound collection unit can collect sounds related to the event. Furthermore, if the user posts about a specific topic, the sound collection unit can collect sounds related to that topic. In this way, the sound collection unit can collect related sounds by analyzing the user's social media activity. Some or all of the above-mentioned processing in the sound collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the sound collection unit can collect sounds using an AI model that inputs social media data into AI and outputs analysis results.

[0044] During noise removal, the removal unit can analyze surrounding environmental sounds in real time and perform optimal filtering. The removal unit, for example, uses streaming data analysis technology to analyze surrounding environmental sounds in real time. For example, the removal unit can analyze surrounding traffic sounds in real time and leave only important sounds. The removal unit can also analyze surrounding people's voices in real time and extract only conversation sounds. Furthermore, the removal unit can analyze surrounding natural sounds in real time to maintain a relaxing sound environment. This enables the removal unit to analyze surrounding environmental sounds in real time and perform optimal filtering. Some or all of the above-described processing in the removal unit may be performed using, for example, AI, or may be performed without AI. For example, the removal unit can perform filtering using an AI model that inputs environmental sound data collected in real time into AI and outputs analysis results.

[0045] When removing noise, the removal unit can optimize the removal algorithm by referring to the user's past sound environment data. The removal unit, for example, uses past recording data to refer to the user's past sound environment data. For example, the removal unit may retain sounds that the user liked to listen to in the past and remove other sounds. The removal unit may also preferentially remove sounds that the user avoided in the past. Furthermore, the removal unit can apply a noise removal algorithm suitable for a specific time period based on the user's past sound environment data. This allows the removal unit to optimize the removal algorithm by referring to the user's past sound environment data. Some or all of the above-described processing in the removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the removal unit may optimize the noise removal algorithm using an AI model that inputs past recording data into AI and outputs analysis results.

[0046] The elimination unit can perform optimal filtering by taking into account the user's geographical location information when removing noise. The elimination unit, for example, uses GPS to acquire the user's geographical location information. For example, when the user is in a park, the GPS detects the location of the park, retains natural sounds, and removes traffic sounds. Furthermore, when the user is in a city, the elimination unit can also detect the city location by GPS, retain conversation sounds, and remove background noise. Furthermore, when the user is at home, the GPS can detect the home location by GPS, retain sounds from home appliances, and remove external noise. This allows the elimination unit to perform optimal filtering by taking into account the user's geographical location information. Some or all of the above-described processing in the elimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the elimination unit can perform filtering using an AI model that inputs GPS data into AI and outputs analysis results.

[0047] The removal unit can customize the removal algorithm by referring to the user's activity history when removing noise. The removal unit, for example, uses past activity data to refer to the user's activity history. For example, when the user is exercising, the removal unit can leave surrounding safety-related sounds and remove other sounds. The removal unit can also remove unnecessary sounds to maintain a quiet environment when the user is reading. Furthermore, when the user is in a meeting, the removal unit can preferentially leave conversation sounds and remove background sounds. This allows the removal unit to customize the removal algorithm by referring to the user's activity history. Some or all of the above-described processing in the removal unit may be performed using, for example, AI, or may be performed without AI. For example, the removal unit can customize the removal algorithm using an AI model that inputs past activity data into AI and outputs analysis results.

[0048] When transmitting voice, the voice transmission unit can select the optimal transmission method by referring to the user's past voice transmission history. The voice transmission unit, for example, uses past voice data to refer to the user's past voice transmission history. For example, the voice transmission unit prioritizes transmitting sounds that the user has liked to hear in the past. The voice transmission unit can also filter out sounds that the user has avoided in the past and prevent them from being transmitted. Furthermore, the voice transmission unit can transmit sounds that are appropriate for a specific time period based on the user's past voice transmission history. In this way, the voice transmission unit can select the optimal transmission method by referring to the user's past voice transmission history. Some or all of the above-described processing in the voice transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice transmission unit can transmit sound using an AI model that inputs past voice data into AI and outputs analysis results.

[0049] The audio transmission unit can customize the transmission method based on the user's current activity status when transmitting audio. The audio transmission unit, for example, uses an activity sensor to grasp the user's current activity status. For example, when the user is exercising, the activity sensor detects the activity and prioritizes transmitting sounds related to safety in the surrounding area. Furthermore, when the user is reading, the activity sensor can detect the user's stationary state and filter out unnecessary sounds to maintain a quiet environment. Furthermore, when the user is in a meeting, the activity sensor can detect conversation and prioritize transmitting the conversation sounds and remove noise. This allows the audio transmission unit to customize the transmission method based on the user's current activity status, thereby enabling appropriate audio transmission. Some or all of the above-described processing in the audio transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the audio transmission unit can transmit audio using an AI model that inputs data from the activity sensor into AI and outputs analysis results.

[0050] When transmitting voice, the voice transmission unit can select the optimal transmission method taking into account the user's geographical location information. The voice transmission unit, for example, uses GPS to acquire the user's geographical location information. For example, when the user is in a park, the GPS detects the location of the park and prioritizes transmitting natural sounds. Furthermore, when the user is in a city, the voice transmission unit can also detect the city location using the GPS and prioritize transmitting traffic sounds and people's voices. Furthermore, when the user is at home, the GPS can detect the home location using the GPS and prioritize transmitting sounds from home appliances and voices of family members. This allows the voice transmission unit to select the optimal transmission method taking into account the user's geographical location information. Some or all of the above-described processing in the voice transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice transmission unit can transmit sound using an AI model that inputs GPS data into AI and outputs analysis results.

[0051] The audio transmission unit can analyze the user's social media activity and transmit related audio when transmitting audio. For example, the audio transmission unit refers to social media data to analyze the user's social media activity. For example, if the user posts about music, the audio transmission unit prioritizes transmitting that music. Also, if the user is participating in an event, the audio transmission unit can transmit audio related to the event. Furthermore, if the user posts about a specific topic, the audio transmission unit can transmit audio related to that topic. In this way, the audio transmission unit can transmit related audio by analyzing the user's social media activity. Some or all of the above-described processing in the audio transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio transmission unit can transmit audio using an AI model that inputs social media data into AI and outputs analysis results.

[0052] When acquiring location information, the location information acquisition unit can select the optimal acquisition method by referring to the user's past movement history. The location information acquisition unit, for example, uses past movement data to refer to the user's past movement history. For example, it prioritizes acquiring locations that the user has frequently visited in the past. The location information acquisition unit can also acquire location information suitable for a specific time period from the user's past movement history. Furthermore, the location information acquisition unit can filter out locations that the user has avoided in the past and prevent them from being acquired. In this way, the location information acquisition unit can select the optimal location information acquisition method by referring to the user's past movement history. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can acquire location information using an AI model that inputs past movement data into AI and outputs analysis results.

[0053] The location information acquisition unit can customize the acquisition method based on the user's current activity status when acquiring location information. The location information acquisition unit, for example, uses an activity sensor to grasp the user's current activity status. For example, when the user is exercising, the activity sensor detects the user's activity, acquires location information in real time, and provides a safe route. Furthermore, when the user is reading, the activity sensor can detect the user's stationary state and set the frequency of location information acquisition low to reduce battery consumption. Furthermore, when the user is in a meeting, the activity sensor can detect the meeting, temporarily stop acquiring location information, and resume it after the meeting ends. This allows the location information acquisition unit to customize the acquisition method based on the user's current activity status, thereby enabling appropriate location information acquisition. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the location information acquisition unit can acquire location information using an AI model that inputs data from the activity sensor into AI and outputs analysis results.

[0054] When acquiring location information, the location information acquisition unit can select the optimal acquisition method taking into account the user's geographical location information. The location information acquisition unit, for example, uses GPS to acquire the user's geographical location information. For example, if the user is in an urban area, the GPS detects the location of the urban area and prioritizes acquiring location information of public transportation. Furthermore, if the user is in a suburban area, the GPS can detect the location of the suburban area and prioritize acquiring location information suitable for traveling by car. Furthermore, if the user is in a tourist destination, the GPS can detect the location of the tourist destination and prioritize acquiring location information of tourist spots. This allows the location information acquisition unit to select the optimal acquisition method taking into account the user's geographical location information. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can acquire location information using an AI model that inputs GPS data into AI and outputs analysis results.

[0055] The location information acquisition unit can analyze the user's social media activity and acquire related location information when acquiring location information. The location information acquisition unit, for example, refers to social media data to analyze the user's social media activity. For example, if the user is participating in a specific event, the location information acquisition unit can prioritize acquiring location information for that event. Also, if the user posts about a specific location, the location information acquisition unit can prioritize acquiring location information for that location. Furthermore, if the user is traveling, the location information acquisition unit can prioritize acquiring location information for travel destinations. In this way, the location information acquisition unit can acquire related location information by analyzing the user's social media activity. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can acquire location information using an AI model that inputs social media data into AI and outputs analysis results.

[0056] When providing information, the information providing unit can select optimal information by referring to the user's past information acquisition history. The information providing unit, for example, uses past information data to refer to the user's past information acquisition history. For example, the information providing unit can preferentially provide information that the user has previously preferred. The information providing unit can also filter information that the user has previously avoided and not provide it. Furthermore, the information providing unit can provide information suitable for a specific time period based on the user's past information acquisition history. In this way, the information providing unit can select optimal information by referring to the user's past information acquisition history. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs past information data into AI and outputs analysis results.

[0057] The information providing unit can customize information based on the user's current activity status when providing information. The information providing unit, for example, uses an activity sensor to grasp the user's current activity status. For example, if the user is exercising, the activity sensor detects the exercise and provides information related to the exercise. Furthermore, if the user is reading, the activity sensor can detect the user's stationary state and provide information related to the reading. Furthermore, if the user is in a meeting, the activity sensor can detect the meeting and provide information related to the meeting. This enables the information providing unit to customize information based on the user's current activity status and provide appropriate information. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs data from the activity sensor into AI and outputs analysis results.

[0058] When providing information, the information providing unit can select optimal information taking into account the user's geographical location information. The information providing unit, for example, uses GPS to acquire the user's geographical location information. For example, when the user is in an urban area, the GPS detects the location of the urban area and prioritizes providing information about public transportation. Furthermore, when the user is in a suburban area, the information providing unit can detect the location of the suburban area and prioritize providing information suitable for traveling by car. Furthermore, when the user is in a tourist destination, the GPS can detect the location of the tourist destination and prioritize providing information about tourist spots. This allows the information providing unit to select optimal information taking into account the user's geographical location information. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs GPS data into AI and outputs analysis results.

[0059] The information providing unit can analyze the user's social media activity and provide related information when providing information. For example, the information providing unit refers to social media data to analyze the user's social media activity. For example, if the user is participating in a specific event, the information providing unit can provide information related to the event. Furthermore, if the user posts about a specific place, the information providing unit can also provide information related to the place. Furthermore, if the user is traveling, the information providing unit can also provide information related to the travel destination. In this way, the information providing unit can provide related information by analyzing the user's social media activity. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs social media data into AI and outputs analysis results.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When collecting sounds around the user, the sound collection unit can identify the direction of the sound and prioritize collecting sounds from a specific direction. For example, when a user is having a conversation, the sound collection unit can identify the direction of the person they are talking to and prioritize collecting sounds from that direction. Also, when a user is listening to music, the sound collection unit can identify the direction of the speaker and prioritize collecting sounds from that direction. Furthermore, when the user is out and about, the sound collection unit can identify the direction of surrounding traffic sounds and prioritize collecting sounds related to safety. This allows the sound collection unit to collect sounds more appropriately by identifying the direction of the sound.

[0062] When removing noise, the removal unit can perform filtering taking into account the user's hearing characteristics. For example, if the user has difficulty hearing high-frequency sounds, the removal unit can prioritize removing high-frequency noise. Also, if the user has difficulty hearing low-frequency sounds, the removal unit can also prioritize removing low-frequency noise. Furthermore, if the user has difficulty hearing sounds in a specific frequency band, the removal unit can also prioritize removing noise in that frequency band. This allows the removal unit to perform more appropriate noise removal by taking the user's hearing characteristics into account.

[0063] When transmitting audio, the audio transmission unit can adjust the frequency characteristics of the audio based on the user's hearing characteristics. For example, if the user has difficulty hearing high-pitched sounds, the audio transmission unit can emphasize and transmit high-pitched sounds. Also, if the user has difficulty hearing low-pitched sounds, the audio transmission unit can emphasize and transmit low-pitched sounds. Furthermore, if the user has difficulty hearing sounds in a specific frequency band, the audio transmission unit can emphasize and transmit sounds in that frequency band. In this way, the audio transmission unit can adjust the frequency characteristics of the audio based on the user's hearing characteristics, thereby enabling more appropriate audio transmission.

[0064] When acquiring location information, the location information acquisition unit can adjust the acquisition frequency taking into account the user's movement speed. For example, if the user is moving at high speed, the location information acquisition unit can set the location information acquisition frequency high and provide real-time location information. Also, if the user is moving at low speed, the location information acquisition unit can set the location information acquisition frequency low to reduce battery consumption. Furthermore, if the user is stationary, the location information acquisition unit can temporarily stop acquiring location information and resume it when necessary. This allows the location information acquisition unit to acquire more appropriate location information by taking into account the user's movement speed.

[0065] When providing information, the information providing unit can analyze the user's past information acquisition history and provide information that is likely to interest the user preferentially. For example, if the user has frequently acquired news of a specific genre in the past, the information providing unit can provide news of that genre preferentially. Also, if the user has frequently acquired sale information of a specific store in the past, the information providing unit can also provide sale information of that store preferentially. Furthermore, if the user has frequently acquired weather information for a specific region in the past, the information providing unit can also provide weather information for that region preferentially. This allows the information providing unit to provide more appropriate information by analyzing the user's past information acquisition history.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The sound collection unit collects sounds around the user. The sounds around the user include environmental sounds, conversation sounds, traffic sounds, etc. The sound collection unit collects the surrounding sounds using, for example, a directional microphone, an omnidirectional microphone, a noise-canceling microphone, etc. Step 2: The noise removal unit removes noise from the sounds collected by the sound collection unit. The noise removal unit uses AI to analyze the noise and removes it using frequency filtering and noise reduction algorithms. For example, it filters out ambient noise and extracts only the conversation sound. Step 3: The audio transmission unit transmits the sound, from which the noise has been removed by the noise reduction unit, to the user via bone conduction. The audio transmission unit uses bone conduction technology to transmit sound directly to the user's brain through the user's skull. Bone conduction technology includes bone conduction earphones and bone conduction headsets. Step 4: The location information acquisition unit acquires the user's location information. The location information acquisition unit acquires the location information using technology such as GPS. GPS includes dual-band GPS and assisted GPS (A-GPS). Step 5: The information providing unit provides information according to the location information acquired by the location information acquiring unit. The information providing unit uses AI to provide surrounding weather information and traffic information such as traffic congestion information. For example, if the weather turns bad while the user is out, the AI ​​analyzes the weather information and notifies the user. The information providing unit also uses AI to provide sale information when the user is in a retail store or other store. For example, if the user is in a supermarket, the AI ​​analyzes the store's sale information and notifies the user.

[0068] (Example 2) A system according to an embodiment of the present invention is a device equipped with bone conduction and AI. This device aggregates surrounding sounds while filtering out noise and delivers the sound directly to the brain when surrounding conversations are difficult to hear. The system collects sounds around the user, filters out noise, and transmits the sounds to the user via bone conduction. It also provides location information, traffic information (such as weather and traffic congestion information), and sales information when the user is in a retail store. This eliminates the need to look at flyers or check information on a mobile phone beforehand. For example, the system includes a sound collection unit that collects sounds around the user. This sound collection unit collects surrounding sounds using a microphone or other device. Next, it includes a noise removal unit that removes noise from the collected sounds. This noise removal unit uses AI to analyze and remove noise. For example, it can filter out surrounding noise and extract only the sound of conversation. The removed sounds are then transmitted to the user via bone conduction. The audio transmission unit uses bone conduction technology to transmit sound directly to the brain through the user's skull. This allows the user to hear sounds without blocking their ears. Furthermore, it includes a location information acquisition unit that acquires the user's location information. This location information is acquired using technologies such as GPS. The information providing unit provides information based on the acquired location information. For example, if a user is in a specific location, information related to that location is provided. The information providing unit uses AI to provide traffic information, such as local weather information and traffic congestion information. For example, if the weather worsens while the user is out, the AI ​​analyzes the weather information and notifies the user. Providing traffic congestion information also allows the user to avoid traffic jams. Furthermore, the information providing unit uses AI to provide sale information when the user is in a retail store or other store. For example, if the user is in a supermarket, the AI ​​analyzes the store's sale information and notifies the user. This allows the user to obtain advantageous information in real time. In this way, the present invention improves user convenience by using bone conduction and AI to aggregate surrounding sounds while filtering out noise and provide information based on location information and surrounding conditions. This allows the system to efficiently collect sounds around the user, filter out noise, transmit sounds, acquire location information, and provide information.

[0069] The system according to the embodiment includes a sound collection unit, a removal unit, an audio transmission unit, a location information acquisition unit, and an information provision unit. The sound collection unit collects sounds around the user. Examples of sounds around the user include, but are not limited to, environmental sounds, conversation sounds, and traffic sounds. The sound collection unit collects the surrounding sounds using, for example, a microphone. Examples of microphones include directional microphones, omnidirectional microphones, and noise-canceling microphones. The removal unit removes noise from the collected sounds. The removal unit analyzes and removes noise using AI. For example, the removal unit removes noise using frequency filtering or a noise reduction algorithm. For example, the removal unit filters the surrounding noise and extracts only the conversation sounds. The audio transmission unit transmits the sound from which the noise has been removed by the removal unit to the user via bone conduction. The audio transmission unit transmits the sound directly to the brain through the user's skull using bone conduction technology. Bone conduction technology includes bone conduction earphones and bone conduction headsets. The location information acquisition unit acquires the user's location information. The location information acquisition unit acquires location information using technologies such as GPS. GPS includes dual-band GPS and assisted GPS (A-GPS). The information provision unit provides information according to the location information acquired by the location information acquisition unit. The information provision unit uses AI to provide surrounding weather information and traffic information such as traffic congestion information. For example, if the weather turns bad while the user is out, the AI ​​analyzes the weather information and notifies the user. The information provision unit also uses AI to provide sale information when the user is in a retail store or other store. For example, if the user is in a supermarket, the AI ​​analyzes the store's sale information and notifies the user. This allows the system to efficiently collect sounds around the user, remove noise, transmit voice, acquire location information, and provide information.

[0070] The sound collection unit collects sounds around the user. Examples of sounds around the user include, but are not limited to, environmental sounds, conversation sounds, and traffic sounds. The sound collection unit collects the surrounding sounds using, for example, a microphone. Microphones include directional microphones, omnidirectional microphones, and noise-canceling microphones. Specifically, directional microphones focus on collecting sounds from a specific direction, while omnidirectional microphones collect sounds evenly from all directions. Noise-canceling microphones reduce ambient noise and collect clear audio. These microphones may be built into the user's device or connected as an external device. The sound collection unit converts the collected sounds into digital signals in real time and transmits them to a central database. Furthermore, the sound collection unit can flexibly respond to specific situations and environments by adjusting the frequency and sensitivity of sound collection. For example, by collecting sounds at high sensitivity in a quiet room and at low sensitivity in a noisy outdoor environment, it is possible to efficiently collect only the necessary sounds. This allows the sound collection unit to collect high-quality audio data in a variety of environments, improving the performance of the entire system.

[0071] The noise removal unit removes noise from the collected audio. It uses AI to analyze and remove noise. Specifically, it uses frequency filtering and noise reduction algorithms. Frequency filtering removes unwanted noise by emphasizing or suppressing specific frequency bands. The noise reduction algorithm analyzes the collected audio data and identifies and removes noise components. AI uses these algorithms to process the audio data in real time to generate clear audio. For example, it can filter out ambient noise and extract only the conversational sounds. Furthermore, AI learns from past audio data and noise patterns to achieve more accurate noise removal. This allows the noise removal unit to effectively remove unwanted noise from the collected audio data and provide users with clear audio. Furthermore, the noise removal unit can select the optimal noise removal algorithm depending on the environment and situation. For example, it can apply mild noise reduction in quiet indoor environments and strong noise reduction in noisy outdoor environments. This allows the noise removal unit to always maintain optimal audio quality and support a comfortable audio experience for users.

[0072] The audio transmission unit transmits the sound from which the noise has been removed by the noise removal unit to the user via bone conduction. The audio transmission unit uses bone conduction technology to transmit sound directly to the brain through the user's skull. Bone conduction includes bone conduction earphones and bone conduction headsets. Specifically, bone conduction earphones are worn around the ears and transmit sound through vibrations. This allows the user to hear external sounds without blocking the ears, improving safety. Bone conduction headsets are worn on the head and similarly transmit sound through vibrations. This allows for comfortable listening even when used for long periods of time. The audio transmission unit can transmit clear audio processed by the noise removal unit to the user without causing discomfort. Furthermore, the audio transmission unit has a function to adjust volume and sound quality, allowing for an audio experience tailored to the user's preferences. For example, the audio transmission unit may be equipped with a function to automatically adjust volume or an equalizer function to emphasize bass and treble. This allows the audio transmission unit to provide the user with high-quality audio and a comfortable audio experience.

[0073] The location information acquisition unit acquires user location information. The location information acquisition unit acquires location information using technologies such as GPS. GPS includes dual-band GPS and assisted GPS (A-GPS). Specifically, dual-band GPS is a technology that acquires highly accurate location information using multiple frequency bands, and assisted GPS (A-GPS) is a technology that improves the acquisition speed of location information by using mobile phone base station information. By combining these technologies, the location information acquisition unit can acquire highly accurate location information in real time, both indoors and outdoors. Furthermore, the location information acquisition unit has a function to adjust the frequency and accuracy of location information acquisition, allowing it to efficiently acquire necessary information while minimizing battery consumption. For example, by acquiring location information more frequently while moving and less frequently while stationary, battery consumption can be reduced. This allows the location information acquisition unit to acquire user location information accurately and efficiently, improving overall system performance.

[0074] The information providing unit provides information according to the location information acquired by the location information acquiring unit. The information providing unit uses AI to provide traffic information, such as local weather information and traffic congestion information. Specifically, the AI ​​analyzes local weather information based on the location information and notifies the user. For example, if the weather worsens while the user is out, the AI ​​analyzes the weather information and notifies the user. The information providing unit also uses AI to provide sale information when the user is in a retail store or other store. For example, if the user is in a supermarket, the AI ​​analyzes the store's sale information and notifies the user. Furthermore, the information providing unit can learn the user's past behavioral history and preferences to provide more personalized information. For example, if the user prefers a particular brand of product, it will prioritize sale information for that brand. The information providing unit can also provide optimal information to the user based on information updated in real time. For example, if traffic congestion information is updated, the AI ​​calculates the optimal detour route and notifies the user. This allows the information providing unit to always provide the user with the latest information and support a comfortable lifestyle.

[0075] The information providing unit can provide at least one of surrounding weather information and traffic congestion information using AI. The information providing unit, for example, provides surrounding weather information using AI. For example, AI analyzes weather forecast data and provides weather information to the user. The information providing unit can also provide traffic congestion information using AI. For example, AI analyzes traffic data and provides traffic congestion information to the user. In this way, the information providing unit can provide surrounding weather information and traffic congestion information by using AI. Some or all of the above-mentioned processing in the information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs weather forecast data and traffic data into AI and outputs the analysis results.

[0076] The information providing unit can provide sale information when the user is in a retail store or other store using AI. The information providing unit, for example, uses AI to provide sale information for the retail store. For example, AI analyzes sale data for the store and provides the sale information to the user. The information providing unit can also use AI to provide sale information for other stores. For example, AI analyzes sale data for the store and provides the sale information to the user. In this way, the information providing unit can provide sale information when the user is in a retail store or other store by using AI. Some or all of the above-described processing in the information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs sale data for the store into AI and outputs the analysis results.

[0077] The sound collection unit can collect ambient sounds using a microphone. The sound collection unit collects ambient sounds using, for example, a microphone. Microphones include directional microphones, omnidirectional microphones, and noise-canceling microphones. For example, a directional microphone is suitable for collecting sounds from a specific direction. An omnidirectional microphone is suitable for collecting sounds from all directions. A noise-canceling microphone is suitable for removing ambient noise and collecting clear sounds. As a result, the sound collection unit collects ambient sounds using a microphone, thereby improving the accuracy of sound collection. Some or all of the above-described processing in the sound collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the sound collection unit can collect sounds using an AI model that inputs sound data collected by the microphone into AI and outputs analysis results.

[0078] The removal unit can filter ambient noise and extract only the conversation sound. The removal unit can remove ambient noise using, for example, frequency filtering. For example, it can filter sounds in a specific frequency band and extract only the conversation sound. The removal unit can also remove noise using a noise reduction algorithm. For example, it can analyze and remove noise using AI. As a result, the removal unit can filter ambient noise and extract only the conversation sound, thereby improving the clarity of the voice. Some or all of the above-mentioned processing in the removal unit can be performed using, for example, AI, or can be performed without using AI. For example, the removal unit can remove noise using an AI model that inputs collected sound data into AI and outputs the analysis results of noise removal.

[0079] The location information acquisition unit can acquire location information using GPS technology. The location information acquisition unit acquires location information using, for example, GPS. GPS includes dual-band GPS and auxiliary GPS (A-GPS). For example, dual-band GPS acquires location information using multiple frequency bands, resulting in high accuracy. Assisted GPS (A-GPS) uses a mobile phone network to acquire location information even in places where the GPS signal is weak. This improves the accuracy of acquiring location information by using GPS technology. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can acquire location information using an AI model that inputs GPS data into AI and outputs analysis results.

[0080] The sound collection unit can estimate the user's emotion and adjust the timing of sound collection based on the estimated user's emotion. The sound collection unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the sound collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The sound collection unit can also estimate the user's emotion using voice analysis technology. For example, the tone and speed of the user's voice can be analyzed to estimate the emotion. The sound collection unit can also estimate the user's emotion using a biosensor. For example, the heart rate and electrodermal activity can be measured to estimate the emotion. This allows the sound collection unit to estimate the user's emotion and adjust the timing of sound collection based on the estimated user's emotion, thereby enabling more appropriate sound collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the sound collection unit may be performed using, for example, AI, or without AI. For example, the sound collection unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0081] When collecting sounds, the sound collection unit can analyze the user's past sound environment history and select the optimal collection method. The sound collection unit, for example, refers to past recording data to analyze the user's past sound environment history. For example, it prioritizes collecting sounds that the user liked to listen to in the past. The sound collection unit can also filter sounds that the user avoided in the past so that they are not collected. Furthermore, the sound collection unit can collect sounds suitable for a specific time period from the user's past sound environment history. For example, it selects the optimal collection method based on sounds the user listened to in a specific time period in the past. In this way, the sound collection unit can select the optimal sound collection method by analyzing the user's past sound environment history. Some or all of the above-described processing in the sound collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the sound collection unit can collect sounds using an AI model that inputs past recording data into AI and outputs analysis results.

[0082] The sound collection unit can perform filtering based on the user's current activity status when collecting sounds. The sound collection unit uses, for example, an activity sensor to grasp the user's current activity status. For example, when the user is exercising, the activity sensor detects the movement and prioritizes collecting sounds related to the user's safety. Furthermore, when the user is reading, the activity sensor can detect the user's stationary state and filter out unnecessary sounds to maintain a quiet environment. Furthermore, when the user is in a meeting, the activity sensor can detect conversation, prioritize collecting conversation sounds, and remove noise. This enables the sound collection unit to perform filtering based on the user's current activity status, thereby enabling appropriate sound collection. Some or all of the above-described processing in the sound collection unit may be performed using, for example, AI, or may be performed without AI. For example, the sound collection unit can collect sounds using an AI model that inputs data from the activity sensor into AI and outputs analysis results.

[0083] The sound collection unit can estimate the user's emotion and prioritize sounds to be collected based on the estimated user's emotion. The sound collection unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the sound collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The sound collection unit can also estimate the user's emotion using voice analysis technology. For example, the sound collection unit can analyze the tone and speed of the user's voice to estimate the emotion. The sound collection unit can also estimate the user's emotion using a biosensor. For example, the sound collection unit can measure the heart rate or electrodermal activity to estimate the emotion. This enables the sound collection unit to estimate the user's emotion and prioritize sounds to be collected based on the estimated user's emotion, thereby enabling more appropriate sound collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the sound collection unit may be performed using, for example, AI, or without AI. For example, the sound collection unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0084] When collecting sounds, the sound collection unit can prioritize collecting highly relevant sounds by taking into account the user's geographical location information. The sound collection unit, for example, uses GPS to acquire the user's geographical location information. For example, when the user is in a park, the GPS detects the location of the park and prioritizes collecting natural sounds. Furthermore, when the user is in a city, the sound collection unit can also detect the city location by using the GPS and prioritize collecting traffic sounds and people's voices. Furthermore, when the user is at home, the sound collection unit can detect the home location by using the GPS and prioritize collecting sounds from home appliances and voices of family members. This allows the sound collection unit to prioritize collecting highly relevant sounds by taking into account the user's geographical location information. Some or all of the above-described processing in the sound collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the sound collection unit can collect sounds using an AI model that inputs GPS data into AI and outputs analysis results.

[0085] The sound collection unit can analyze the user's social media activity and collect related sounds when collecting sounds. The sound collection unit, for example, refers to social media data to analyze the user's social media activity. For example, if the user posts about music, the sound collection unit prioritizes collecting that music. Also, if the user is participating in an event, the sound collection unit can collect sounds related to the event. Furthermore, if the user posts about a specific topic, the sound collection unit can collect sounds related to that topic. In this way, the sound collection unit can collect related sounds by analyzing the user's social media activity. Some or all of the above-mentioned processing in the sound collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the sound collection unit can collect sounds using an AI model that inputs social media data into AI and outputs analysis results.

[0086] The removal unit can estimate the user's emotion and adjust the intensity of noise reduction based on the estimated user's emotion. The removal unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the removal unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The removal unit can also estimate the user's emotion using voice analysis technology. For example, the removal unit can analyze the tone and speed of the user's voice to estimate the emotion. The removal unit can also estimate the user's emotion using a biosensor. For example, the removal unit can measure the heart rate or electrodermal activity to estimate the emotion. This allows the removal unit to estimate the user's emotion and adjust the intensity of noise reduction based on the estimated user's emotion, thereby providing a more appropriate sound environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the removal unit may be performed using, for example, AI, or without AI. For example, the removal unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0087] During noise removal, the removal unit can analyze surrounding environmental sounds in real time and perform optimal filtering. The removal unit, for example, uses streaming data analysis technology to analyze surrounding environmental sounds in real time. For example, the removal unit can analyze surrounding traffic sounds in real time and leave only important sounds. The removal unit can also analyze surrounding people's voices in real time and extract only conversation sounds. Furthermore, the removal unit can analyze surrounding natural sounds in real time to maintain a relaxing sound environment. This enables the removal unit to analyze surrounding environmental sounds in real time and perform optimal filtering. Some or all of the above-described processing in the removal unit may be performed using, for example, AI, or may be performed without AI. For example, the removal unit can perform filtering using an AI model that inputs environmental sound data collected in real time into AI and outputs analysis results.

[0088] When removing noise, the removal unit can optimize the removal algorithm by referring to the user's past sound environment data. The removal unit, for example, uses past recording data to refer to the user's past sound environment data. For example, the removal unit may retain sounds that the user liked to listen to in the past and remove other sounds. The removal unit may also preferentially remove sounds that the user avoided in the past. Furthermore, the removal unit can apply a noise removal algorithm suitable for a specific time period based on the user's past sound environment data. This allows the removal unit to optimize the removal algorithm by referring to the user's past sound environment data. Some or all of the above-described processing in the removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the removal unit may optimize the noise removal algorithm using an AI model that inputs past recording data into AI and outputs analysis results.

[0089] The removal unit can estimate the user's emotion and select the type of noise to be removed based on the estimated user's emotion. The removal unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the removal unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The removal unit can also estimate the user's emotion using voice analysis technology. For example, the removal unit can analyze the tone and speed of the user's voice to estimate the emotion. The removal unit can also estimate the user's emotion using a biosensor. For example, the removal unit can measure the heart rate or electrodermal activity to estimate the emotion. This allows the removal unit to estimate the user's emotion and select the type of noise to be removed based on the estimated user's emotion, thereby providing a more appropriate sound environment. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the removal unit may be performed using, for example, AI, or without AI. For example, the removal unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0090] The elimination unit can perform optimal filtering by taking into account the user's geographical location information when removing noise. The elimination unit, for example, uses GPS to acquire the user's geographical location information. For example, when the user is in a park, the GPS detects the location of the park, retains natural sounds, and removes traffic sounds. Furthermore, when the user is in a city, the elimination unit can also detect the city location by GPS, retain conversation sounds, and remove background noise. Furthermore, when the user is at home, the GPS can detect the home location by GPS, retain sounds from home appliances, and remove external noise. This allows the elimination unit to perform optimal filtering by taking into account the user's geographical location information. Some or all of the above-described processing in the elimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the elimination unit can perform filtering using an AI model that inputs GPS data into AI and outputs analysis results.

[0091] The removal unit can customize the removal algorithm by referring to the user's activity history when removing noise. The removal unit, for example, uses past activity data to refer to the user's activity history. For example, when the user is exercising, the removal unit can leave surrounding safety-related sounds and remove other sounds. The removal unit can also remove unnecessary sounds to maintain a quiet environment when the user is reading. Furthermore, when the user is in a meeting, the removal unit can preferentially leave conversation sounds and remove background sounds. This allows the removal unit to customize the removal algorithm by referring to the user's activity history. Some or all of the above-described processing in the removal unit may be performed using, for example, AI, or may be performed without AI. For example, the removal unit can customize the removal algorithm using an AI model that inputs past activity data into AI and outputs analysis results.

[0092] The voice transmission unit can estimate the user's emotion and adjust the intensity of voice transmission based on the estimated user's emotion. The voice transmission unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the voice transmission unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The voice transmission unit can also estimate the user's emotion using voice analysis technology. For example, the voice transmission unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the voice transmission unit can estimate the user's emotion using a biosensor. For example, the voice transmission unit can measure the heart rate or electrodermal activity to estimate the emotion. This allows the voice transmission unit to estimate the user's emotion and adjust the intensity of voice transmission based on the estimated user's emotion, thereby enabling more appropriate voice transmission. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the voice transmission unit may be performed using, for example, AI, or without AI. For example, the voice transmission unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0093] When transmitting voice, the voice transmission unit can select the optimal transmission method by referring to the user's past voice transmission history. The voice transmission unit, for example, uses past voice data to refer to the user's past voice transmission history. For example, the voice transmission unit prioritizes transmitting sounds that the user has liked to hear in the past. The voice transmission unit can also filter out sounds that the user has avoided in the past and prevent them from being transmitted. Furthermore, the voice transmission unit can transmit sounds that are appropriate for a specific time period based on the user's past voice transmission history. In this way, the voice transmission unit can select the optimal transmission method by referring to the user's past voice transmission history. Some or all of the above-described processing in the voice transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice transmission unit can transmit sound using an AI model that inputs past voice data into AI and outputs analysis results.

[0094] The audio transmission unit can customize the transmission method based on the user's current activity status when transmitting audio. The audio transmission unit, for example, uses an activity sensor to grasp the user's current activity status. For example, when the user is exercising, the activity sensor detects the activity and prioritizes transmitting sounds related to safety in the surrounding area. Furthermore, when the user is reading, the activity sensor can detect the user's stationary state and filter out unnecessary sounds to maintain a quiet environment. Furthermore, when the user is in a meeting, the activity sensor can detect conversation and prioritize transmitting the conversation sounds and remove noise. This allows the audio transmission unit to customize the transmission method based on the user's current activity status, thereby enabling appropriate audio transmission. Some or all of the above-described processing in the audio transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the audio transmission unit can transmit audio using an AI model that inputs data from the activity sensor into AI and outputs analysis results.

[0095] The voice transmission unit can estimate the user's emotion and determine the priority of the voice to be transmitted based on the estimated user's emotion. The voice transmission unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the voice transmission unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The voice transmission unit can also estimate the user's emotion using voice analysis technology. For example, the voice transmission unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the voice transmission unit can estimate the user's emotion using a biosensor. For example, the voice transmission unit can measure the heart rate or electrodermal activity to estimate the emotion. This allows the voice transmission unit to estimate the user's emotion and determine the priority of the voice to be transmitted based on the estimated user's emotion, thereby enabling more appropriate voice transmission. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the voice transmission unit may be performed using, for example, AI, or without AI. For example, the voice transmission unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0096] When transmitting voice, the voice transmission unit can select the optimal transmission method taking into account the user's geographical location information. The voice transmission unit, for example, uses GPS to acquire the user's geographical location information. For example, when the user is in a park, the GPS detects the location of the park and prioritizes transmitting natural sounds. Furthermore, when the user is in a city, the voice transmission unit can also detect the city location using the GPS and prioritize transmitting traffic sounds and people's voices. Furthermore, when the user is at home, the GPS can detect the home location using the GPS and prioritize transmitting sounds from home appliances and voices of family members. This allows the voice transmission unit to select the optimal transmission method taking into account the user's geographical location information. Some or all of the above-described processing in the voice transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice transmission unit can transmit sound using an AI model that inputs GPS data into AI and outputs analysis results.

[0097] The audio transmission unit can analyze the user's social media activity and transmit related audio when transmitting audio. For example, the audio transmission unit refers to social media data to analyze the user's social media activity. For example, if the user posts about music, the audio transmission unit prioritizes transmitting that music. Also, if the user is participating in an event, the audio transmission unit can transmit audio related to the event. Furthermore, if the user posts about a specific topic, the audio transmission unit can transmit audio related to that topic. In this way, the audio transmission unit can transmit related audio by analyzing the user's social media activity. Some or all of the above-described processing in the audio transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio transmission unit can transmit audio using an AI model that inputs social media data into AI and outputs analysis results.

[0098] The location information acquisition unit can estimate the user's emotion and adjust the frequency of location information acquisition based on the estimated user's emotion. The location information acquisition unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the location information acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The location information acquisition unit can also estimate the user's emotion using voice analysis technology. For example, the location information acquisition unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the location information acquisition unit can estimate the user's emotion using a biosensor. For example, the location information acquisition unit can measure the heart rate or electrodermal activity to estimate the emotion. This allows the location information acquisition unit to estimate the user's emotion and adjust the frequency of location information acquisition based on the estimated user's emotion, thereby enabling more appropriate location information acquisition. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0099] When acquiring location information, the location information acquisition unit can select the optimal acquisition method by referring to the user's past movement history. The location information acquisition unit, for example, uses past movement data to refer to the user's past movement history. For example, it prioritizes acquiring locations that the user has frequently visited in the past. The location information acquisition unit can also acquire location information suitable for a specific time period from the user's past movement history. Furthermore, the location information acquisition unit can filter out locations that the user has avoided in the past and prevent them from being acquired. In this way, the location information acquisition unit can select the optimal location information acquisition method by referring to the user's past movement history. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can acquire location information using an AI model that inputs past movement data into AI and outputs analysis results.

[0100] The location information acquisition unit can customize the acquisition method based on the user's current activity status when acquiring location information. The location information acquisition unit, for example, uses an activity sensor to grasp the user's current activity status. For example, when the user is exercising, the activity sensor detects the user's activity, acquires location information in real time, and provides a safe route. Furthermore, when the user is reading, the activity sensor can detect the user's stationary state and set the frequency of location information acquisition low to reduce battery consumption. Furthermore, when the user is in a meeting, the activity sensor can detect the meeting, temporarily stop acquiring location information, and resume it after the meeting ends. This allows the location information acquisition unit to customize the acquisition method based on the user's current activity status, thereby enabling appropriate location information acquisition. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the location information acquisition unit can acquire location information using an AI model that inputs data from the activity sensor into AI and outputs analysis results.

[0101] The location information acquisition unit can estimate the user's emotion and determine the priority of location information to be acquired based on the estimated user's emotion. The location information acquisition unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the location information acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The location information acquisition unit can also estimate the user's emotion using voice analysis technology. For example, the location information acquisition unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the location information acquisition unit can also estimate the user's emotion using a biosensor. For example, the location information acquisition unit can measure the heart rate or electrodermal activity to estimate the emotion. This allows the location information acquisition unit to estimate the user's emotion and determine the priority of location information to be acquired based on the estimated user's emotion, thereby enabling more appropriate location information to be acquired. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0102] When acquiring location information, the location information acquisition unit can select the optimal acquisition method taking into account the user's geographical location information. The location information acquisition unit, for example, uses GPS to acquire the user's geographical location information. For example, if the user is in an urban area, the GPS detects the location of the urban area and prioritizes acquiring location information of public transportation. Furthermore, if the user is in a suburban area, the GPS can detect the location of the suburban area and prioritize acquiring location information suitable for traveling by car. Furthermore, if the user is in a tourist destination, the GPS can detect the location of the tourist destination and prioritize acquiring location information of tourist spots. This allows the location information acquisition unit to select the optimal acquisition method taking into account the user's geographical location information. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can acquire location information using an AI model that inputs GPS data into AI and outputs analysis results.

[0103] The location information acquisition unit can analyze the user's social media activity and acquire related location information when acquiring location information. The location information acquisition unit, for example, refers to social media data to analyze the user's social media activity. For example, if the user is participating in a specific event, the location information acquisition unit can prioritize acquiring location information for that event. Also, if the user posts about a specific location, the location information acquisition unit can prioritize acquiring location information for that location. Furthermore, if the user is traveling, the location information acquisition unit can prioritize acquiring location information for travel destinations. In this way, the location information acquisition unit can acquire related location information by analyzing the user's social media activity. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can acquire location information using an AI model that inputs social media data into AI and outputs analysis results.

[0104] The information providing unit can estimate the user's emotion and adjust the type of information to be provided based on the estimated user's emotion. The information providing unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the information providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The information providing unit can also estimate the user's emotion using voice analysis technology. For example, the information providing unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the information providing unit can estimate the user's emotion using a biosensor. For example, the information providing unit can estimate the user's emotion by measuring the heart rate or electrodermal activity. This allows the information providing unit to estimate the user's emotion and adjust the type of information to be provided based on the estimated user's emotion, thereby providing more appropriate information. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or without AI. For example, the information providing unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0105] When providing information, the information providing unit can select optimal information by referring to the user's past information acquisition history. The information providing unit, for example, uses past information data to refer to the user's past information acquisition history. For example, the information providing unit can preferentially provide information that the user has previously preferred. The information providing unit can also filter information that the user has previously avoided and not provide it. Furthermore, the information providing unit can provide information suitable for a specific time period based on the user's past information acquisition history. In this way, the information providing unit can select optimal information by referring to the user's past information acquisition history. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs past information data into AI and outputs analysis results.

[0106] The information providing unit can customize information based on the user's current activity status when providing information. The information providing unit, for example, uses an activity sensor to grasp the user's current activity status. For example, if the user is exercising, the activity sensor detects the exercise and provides information related to the exercise. Furthermore, if the user is reading, the activity sensor can detect the user's stationary state and provide information related to the reading. Furthermore, if the user is in a meeting, the activity sensor can detect the meeting and provide information related to the meeting. This enables the information providing unit to customize information based on the user's current activity status and provide appropriate information. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs data from the activity sensor into AI and outputs analysis results.

[0107] The information providing unit can estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. The information providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the information providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The information providing unit can also estimate the user's emotions using voice analysis technology. For example, the information providing unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the information providing unit can estimate the user's emotions using a biosensor. For example, the information providing unit can measure the heart rate or electrodermal activity to estimate the emotion. This enables the information providing unit to estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions, thereby providing more appropriate information. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or without AI. For example, the information providing unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0108] When providing information, the information providing unit can select optimal information taking into account the user's geographical location information. The information providing unit, for example, uses GPS to acquire the user's geographical location information. For example, when the user is in an urban area, the GPS detects the location of the urban area and prioritizes providing information about public transportation. Furthermore, when the user is in a suburban area, the information providing unit can detect the location of the suburban area and prioritize providing information suitable for traveling by car. Furthermore, when the user is in a tourist destination, the GPS can detect the location of the tourist destination and prioritize providing information about tourist spots. This allows the information providing unit to select optimal information taking into account the user's geographical location information. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs GPS data into AI and outputs analysis results.

[0109] The information providing unit can analyze the user's social media activity and provide related information when providing information. For example, the information providing unit refers to social media data to analyze the user's social media activity. For example, if the user is participating in a specific event, the information providing unit can provide information related to the event. Furthermore, if the user posts about a specific place, the information providing unit can also provide information related to the place. Furthermore, if the user is traveling, the information providing unit can also provide information related to the travel destination. In this way, the information providing unit can provide related information by analyzing the user's social media activity. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs social media data into AI and outputs analysis results.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] When collecting sounds around the user, the sound collection unit can identify the direction of the sound and prioritize collecting sounds from a specific direction. For example, when a user is having a conversation, the sound collection unit can identify the direction of the person they are talking to and prioritize collecting sounds from that direction. Also, when a user is listening to music, the sound collection unit can identify the direction of the speaker and prioritize collecting sounds from that direction. Furthermore, when the user is out and about, the sound collection unit can identify the direction of surrounding traffic sounds and prioritize collecting sounds related to safety. This allows the sound collection unit to collect sounds more appropriately by identifying the direction of the sound.

[0112] When removing noise, the removal unit can perform filtering taking into account the user's hearing characteristics. For example, if the user has difficulty hearing high-frequency sounds, the removal unit can prioritize removing high-frequency noise. Also, if the user has difficulty hearing low-frequency sounds, the removal unit can also prioritize removing low-frequency noise. Furthermore, if the user has difficulty hearing sounds in a specific frequency band, the removal unit can also prioritize removing noise in that frequency band. This allows the removal unit to perform more appropriate noise removal by taking the user's hearing characteristics into account.

[0113] When transmitting audio, the audio transmission unit can adjust the frequency characteristics of the audio based on the user's hearing characteristics. For example, if the user has difficulty hearing high-pitched sounds, the audio transmission unit can emphasize and transmit high-pitched sounds. Also, if the user has difficulty hearing low-pitched sounds, the audio transmission unit can emphasize and transmit low-pitched sounds. Furthermore, if the user has difficulty hearing sounds in a specific frequency band, the audio transmission unit can emphasize and transmit sounds in that frequency band. In this way, the audio transmission unit can adjust the frequency characteristics of the audio based on the user's hearing characteristics, thereby enabling more appropriate audio transmission.

[0114] When acquiring location information, the location information acquisition unit can adjust the acquisition frequency taking into account the user's movement speed. For example, if the user is moving at high speed, the location information acquisition unit can set the location information acquisition frequency high and provide real-time location information. Also, if the user is moving at low speed, the location information acquisition unit can set the location information acquisition frequency low to reduce battery consumption. Furthermore, if the user is stationary, the location information acquisition unit can temporarily stop acquiring location information and resume it when necessary. This allows the location information acquisition unit to acquire more appropriate location information by taking into account the user's movement speed.

[0115] When providing information, the information providing unit can analyze the user's past information acquisition history and provide information that is likely to interest the user preferentially. For example, if the user has frequently acquired news of a specific genre in the past, the information providing unit can provide news of that genre preferentially. Also, if the user has frequently acquired sale information of a specific store in the past, the information providing unit can also provide sale information of that store preferentially. Furthermore, if the user has frequently acquired weather information for a specific region in the past, the information providing unit can also provide weather information for that region preferentially. This allows the information providing unit to provide more appropriate information by analyzing the user's past information acquisition history.

[0116] The sound collection unit can estimate the user's emotions and select the type of sounds to collect based on the estimated user's emotions. For example, if the user is relaxed, the sound collection unit can prioritize collecting natural sounds and calm sounds. Also, if the user is concentrating, the sound collection unit can remove ambient noise and collect only important sounds. Furthermore, if the user is excited, the sound collection unit can collect a wide range of ambient sounds and provide sounds that will increase the user's excitement. This allows the sound collection unit to estimate the user's emotions and collect more appropriate sounds.

[0117] The elimination unit can estimate the user's emotions and adjust the noise elimination algorithm based on the estimated user's emotions. For example, if the user is relaxed, the elimination unit can set the noise elimination strength low to maintain a natural sound environment. Alternatively, if the user is concentrating, the elimination unit can set the noise elimination strength high to thoroughly eliminate ambient noise. Furthermore, if the user is excited, the elimination unit can adjust the noise elimination algorithm to leave sounds that increase the user's excitement. This allows the elimination unit to perform more appropriate noise elimination by estimating the user's emotions.

[0118] The voice transmission unit can estimate the user's emotions and adjust the voice transmission method based on the estimated user's emotions. For example, if the user is relaxed, the voice transmission unit can prioritize transmitting a gentle voice. Also, if the user is concentrating, the voice transmission unit can prioritize transmitting a clear and intelligible voice. Furthermore, if the user is excited, the voice transmission unit can prioritize transmitting a powerful voice. In this way, the voice transmission unit can transmit voice more appropriately by estimating the user's emotions.

[0119] The location information acquisition unit can estimate the user's emotions and adjust the method for acquiring location information based on the estimated user's emotions. For example, if the user is relaxed, the location information acquisition unit can set the frequency of acquiring location information low to reduce battery consumption. Also, if the user is concentrating, the location information acquisition unit can set the frequency of acquiring location information high to provide real-time location information. Furthermore, if the user is excited, the location information acquisition unit can adjust the method for acquiring location information and prioritize acquiring information about places that will excite the user. This allows the location information acquisition unit to acquire more appropriate location information by estimating the user's emotions.

[0120] The information providing unit can estimate the user's emotions and adjust the content of the information to be provided based on the estimated user's emotions. For example, if the user is relaxed, the information providing unit can provide information suitable for relaxation preferentially. Also, if the user is concentrating, the information providing unit can provide information that helps the user to concentrate preferentially. Furthermore, if the user is excited, the information providing unit can provide information that increases the user's excitement preferentially. This allows the information providing unit to provide more appropriate information by estimating the user's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The sound collection unit collects sounds around the user. The sounds around the user include environmental sounds, conversation sounds, traffic sounds, etc. The sound collection unit collects the surrounding sounds using, for example, a directional microphone, an omnidirectional microphone, a noise-canceling microphone, etc. Step 2: The noise removal unit removes noise from the sounds collected by the sound collection unit. The noise removal unit uses AI to analyze the noise and removes it using frequency filtering and noise reduction algorithms. For example, it filters out ambient noise and extracts only the conversation sound. Step 3: The audio transmission unit transmits the sound, from which the noise has been removed by the noise reduction unit, to the user via bone conduction. The audio transmission unit uses bone conduction technology to transmit sound directly to the user's brain through the user's skull. Bone conduction technology includes bone conduction earphones and bone conduction headsets. Step 4: The location information acquisition unit acquires the user's location information. The location information acquisition unit acquires the location information using technology such as GPS. GPS includes dual-band GPS and assisted GPS (A-GPS). Step 5: The information providing unit provides information according to the location information acquired by the location information acquiring unit. The information providing unit uses AI to provide surrounding weather information and traffic information such as traffic congestion information. For example, if the weather turns bad while the user is out, the AI ​​analyzes the weather information and notifies the user. The information providing unit also uses AI to provide sale information when the user is in a retail store or other store. For example, if the user is in a supermarket, the AI ​​analyzes the store's sale information and notifies the user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats including 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. The AIs other than the generation AI are, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but are not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or in whole by AI, but are not limited to these 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.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] For example, the sound collection unit can collect ambient sounds using the microphone 38B or camera 42 of the smart device 14. The removal unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes and removes noise using AI. The voice transmission unit is realized by the control unit 46A of the smart device 14 and transmits sound to the user using bone conduction technology. The location information acquisition unit acquires location information using the GPS function of the smart device 14. The information provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides surrounding weather information, traffic congestion information, etc. using AI. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] For example, the sound collection unit can collect ambient sounds using the microphone 238 and camera 42 of the smart glasses 214. The removal unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes and removes noise using AI. The audio transmission unit is realized by the control unit 46A of the smart glasses 214 and transmits sound to the user using bone conduction technology. The location information acquisition unit acquires location information using the GPS function of the smart glasses 214. The information provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides surrounding weather information, traffic congestion information, etc. using AI. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] 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.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] For example, the sound collection unit can collect ambient sounds using the microphone 238 and camera 42 of the headset terminal 314. The removal unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes and removes noise using AI. The audio transmission unit is realized by the control unit 46A of the headset terminal 314, and transmits sound to the user using bone conduction technology. The location information acquisition unit acquires location information using the GPS function of the headset terminal 314. The information provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides surrounding weather information, traffic congestion information, etc. using AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] 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.

[0168] 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.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] For example, the sound collection unit can collect surrounding sounds using the microphone 238 and camera 42 of the robot 414. The removal unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes and removes noise using AI. The voice transmission unit is realized by the control unit 46A of the robot 414, and transmits sound to the user using bone conduction technology. The location information acquisition unit acquires location information using the GPS function of the robot 414. The information provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides surrounding weather information, traffic congestion information, etc. using AI. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] 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.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] 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.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) a sound collection unit that collects sounds around the user; a removal unit that removes noise from the sound collected by the sound collection unit; a sound transmission unit that transmits the sound from which noise has been removed by the removal unit to the user by bone conduction; a location information acquisition unit that acquires location information of the user; an information providing unit that provides information corresponding to the location information acquired by the location information acquiring unit by transmitting the information to the user from the voice transmitting unit through bone conduction. A system characterized by: (Appendix 2) The information providing unit Provides at least one of the following traffic information using AI: local weather information or traffic congestion information 2. The system of claim 1. (Appendix 3) The information providing unit AI provides special offers when you're in a retail store or other store 2. The system of claim 1. (Appendix 4) The sound collecting unit Collecting ambient sounds with a microphone 2. The system of claim 1. (Appendix 5) The removal unit Filter out ambient noise and extract only the speech 2. The system of claim 1. (Appendix 6) The location information acquisition unit Obtain location information using GPS technology 2. The system of claim 1. (Appendix 7) The sound collecting unit Estimate the user's emotion and adjust the timing of sound collection based on the estimated user's emotion. 2. The system of claim 1. (Appendix 8) The sound collecting unit When collecting sounds, the system analyzes the user's past sound environment history and selects the optimal collection method. 2. The system of claim 1. (Appendix 9) The sound collecting unit When collecting sound, filtering is performed based on the user's current activity. 2. The system of claim 1. (Appendix 10) The sound collecting unit Estimate the user's emotions and prioritize the sounds to be collected based on the estimated user emotions. 2. The system of claim 1. (Appendix 11) The sound collecting unit When collecting sounds, the system takes into account the user's geographical location information and prioritizes the collection of highly relevant sounds. 2. The system of claim 1. (Appendix 12) The sound collecting unit When collecting sounds, analyze the user's social media activity and collect related sounds. 2. The system of claim 1. (Appendix 13) The removal unit Estimate the user's emotion and adjust the noise reduction strength based on the estimated user's emotion. 2. The system of claim 1. (Appendix 14) The removal unit When removing noise, the system analyzes the surrounding environmental sounds in real time and performs optimal filtering. 2. The system of claim 1. (Appendix 15) The removal unit When removing noise, the removal algorithm is optimized by referencing the user's past sound environment data. 2. The system of claim 1. (Appendix 16) The removal unit The user's emotion is estimated, and the type of noise to be removed is selected based on the estimated user's emotion. 2. The system of claim 1. (Appendix 17) The removal unit When removing noise, optimal filtering is performed taking into account the user's geographical location information. 2. The system of claim 1. (Appendix 18) The removal unit When removing noise, customize the removal algorithm by referring to the user's activity history. 2. The system of claim 1. (Appendix 19) The audio transmission unit Estimating a user's emotion and adjusting the intensity of voice transmission based on the estimated user's emotion 2. The system of claim 1. (Appendix 20) The audio transmission unit When transmitting voice, the optimal transmission method is selected by referring to the user's past voice transmission history. 2. The system of claim 1. (Appendix 21) The audio transmission unit Customize voice transmission based on the user's current activity 2. The system of claim 1. (Appendix 22) The audio transmission unit Estimate the user's emotions and prioritize the speech to be transmitted based on the estimated user emotions. 2. The system of claim 1. (Appendix 23) The audio transmission unit When transmitting voice, the optimal transmission method is selected taking into account the user's geographical location information. 2. The system of claim 1. (Appendix 24) The audio transmission unit When transmitting voice, analyze the user's social media activity and transmit relevant voice 2. The system of claim 1. (Appendix 25) The location information acquisition unit The system estimates the user's emotions and adjusts the frequency of location information acquisition based on the estimated user emotions. 2. The system of claim 1. (Appendix 26) The location information acquisition unit When acquiring location information, the optimal acquisition method is selected by referring to the user's past movement history. 2. The system of claim 1. (Appendix 27) The location information acquisition unit Customize location information acquisition based on the user's current activity status 2. The system of claim 1. (Appendix 28) The location information acquisition unit Estimate the user's emotions and determine the priority of location information to be acquired based on the estimated user emotions. 2. The system of claim 1. (Appendix 29) The location information acquisition unit When acquiring location information, the optimal acquisition method is selected taking into account the user's geographical location information. 2. The system of claim 1. (Appendix 30) The location information acquisition unit When location information is acquired, the user's social media activity is analyzed to acquire related location information. 2. The system of claim 1. (Appendix 31) The information providing unit Estimate the user's emotions and adjust the type of information provided based on the estimated user emotions. 2. The system of claim 1. (Appendix 32) The information providing unit When providing information, select the most appropriate information by referring to the user's past information acquisition history. 2. The system of claim 1. (Appendix 33) The information providing unit When providing information, customize it based on the user's current activity. 2. The system of claim 1. (Appendix 34) The information providing unit Estimate the user's emotions and prioritize the information to be provided based on the estimated user emotions. 2. The system of claim 1. (Appendix 35) The information providing unit When providing information, select the most appropriate information taking into account the user's geographical location information. 2. The system of claim 1. (Appendix 36) The information providing unit When you provide information, analyze your social media activity and provide you with relevant information 2. The system of claim 1. [Explanation of symbols]

[0195] 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 sound collection unit that collects sounds around the user; a removal unit that removes noise from the sound collected by the sound collection unit; a sound transmission unit that transmits the sound from which noise has been removed by the removal unit to the user by bone conduction; a location information acquisition unit that acquires location information of the user; an information providing unit that provides information corresponding to the location information acquired by the location information acquiring unit by transmitting the information to the user through bone conduction from the voice transmitting unit, The removal unit an emotion of the user is estimated, and if the category of the estimated emotion of the user is relaxation, an intensity of removing noise other than conversation sounds from the sound collected by the sound collection unit is set to a first level, and if the category of the estimated emotion of the user is concentration, an intensity of removing the noise is set to a second level that is stronger than the first level; A system characterized by:

2. The information providing unit Using AI to provide at least one of the following traffic information: local weather information or traffic congestion information 2. The system of claim 1.

3. The information providing unit AI provides special offers when you're in a retail store or other store 2. The system of claim 1.

4. The sound collecting unit Collecting ambient sounds with a microphone 2. The system of claim 1.

5. The removal unit Filter out ambient noise and extract only the speech 2. The system of claim 1.

6. The location information acquisition unit Obtain location information using GPS technology 2. The system of claim 1.

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