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 a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technology has 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 sound from which 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. Effect of the Invention

[0007] The system according to the embodiment can filter out background noise and provide location-based information. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a device equipped with bone conduction and AI. This device is a device that collects surrounding voices while removing noise and delivers the voices directly to the brain when it is difficult to hear surrounding conversations. The system collects sounds around the user, removes noise, and then transmits the sounds to the user via bone conduction. In addition, it provides location information, traffic information such as weather and traffic congestion information in the surrounding conditions, and sale information when in a retail store. This eliminates the need to look at flyers in advance or check information on a mobile phone. 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 the like. Next, it includes a removal unit that removes noise from the collected sounds. This removal unit uses AI to analyze and remove noise. For example, it is possible to filter surrounding noise and extract only conversation sounds. The removed sounds are transmitted to the user via bone conduction. The voice transmission unit uses bone conduction technology to transmit sounds directly to the brain through the user's skull. This allows the user to hear sounds without blocking their ears. In addition, there is a location information acquisition unit that acquires the user's location information. This location information is acquired using a technology such as GPS. The information providing unit provides information according to the acquired location information. For example, when the 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 surrounding weather information and traffic congestion information. For example, when the weather gets bad while the user is out, the AI ​​analyzes the weather information and notifies the user. In addition, by providing traffic congestion information, the user can avoid traffic congestion. Furthermore, the information providing unit uses AI to provide sale information when the user is in a retail store or other store. For example, when the user is in a supermarket, the AI ​​analyzes the sale information of that store and notifies the user. This allows the user to obtain advantageous information in real time. In this way, the present invention can improve the convenience of the user by using bone conduction and AI to aggregate surrounding sounds while removing noise, and provide information according to location information and surrounding conditions. This allows the system to efficiently collect sounds around the user, remove noise, transmit voice, obtain location information, and provide information.

[0029] The system according to the embodiment includes a sound collection unit, a removal unit, a voice transmission unit, a position information acquisition unit, and an information provision unit. The sound collection unit collects sounds around the user. The sounds around the user include, for example, environmental sounds, conversation sounds, traffic sounds, etc., but are not limited to such examples. The sound collection unit collects surrounding sounds using, for example, a microphone. The microphone includes a directional microphone, an omnidirectional microphone, a noise canceling microphone, etc. The removal unit removes noise from the collected sounds. The removal unit analyzes and removes noise using AI. For example, the noise is removed using frequency filtering or a noise reduction algorithm. The removal unit filters, for example, the surrounding noise and extracts only the conversation sound. The voice transmission unit transmits the sound from which the noise has been removed by the removal unit to the user by bone conduction. The voice transmission unit transmits the sound directly to the brain through the user's skull using bone conduction technology. Bone conduction includes bone conduction earphones and bone conduction headsets. The position information acquisition unit acquires the user's position information. The location information acquisition unit acquires location information using technologies such as GPS. GPS includes dual-band GPS and auxiliary 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 gets bad while the user is out, the AI ​​analyzes the weather information and notifies the user. In addition, the information provision 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 sale information of the store 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 surrounding sounds using, for example, a microphone. The microphones include directional microphones, omnidirectional microphones, and noise-canceling microphones. Specifically, a directional microphone intensively collects sounds from a specific direction, while an omnidirectional microphone collects sounds evenly from all directions. A noise-canceling microphone has a function of reducing ambient noise and collecting clear voice. These microphones may be built into the user's device or may be 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, sound can be collected with high sensitivity in a quiet room and with low sensitivity in a noisy outdoor environment, thereby efficiently collecting only the necessary sounds. This enables the sound collection unit to collect high-quality sound data in a variety of environments, improving the performance of the entire system.

[0031] The removal unit removes noise from the collected sound. The removal unit uses AI to analyze and remove noise. Specifically, noise is removed using frequency filtering and noise reduction algorithms. Frequency filtering removes unnecessary noise by emphasizing or suppressing sounds in a specific frequency band. The noise reduction algorithm analyzes the collected voice data, identifies and removes noise components. AI uses these algorithms to process voice data in real time to generate clear voice. For example, it can filter ambient noise and extract only conversation sounds. In addition, AI learns past voice data and noise patterns to achieve more accurate noise removal. This allows the removal unit to effectively remove unnecessary noise from the collected voice data and provide the user with clear voice. Furthermore, the removal unit can select the optimal noise removal algorithm according to different environments and situations. For example, mild noise reduction is performed in a quiet indoor environment, and strong noise reduction is applied in a noisy outdoor environment. This allows the removal unit to always maintain optimal voice quality and support the user's comfortable voice experience.

[0032] The audio transmission unit transmits the sound from which the noise has been removed by the removal unit to the user by bone conduction. The audio transmission unit transmits the sound directly to the brain through the user's skull using bone conduction technology. Bone conduction includes bone conduction earphones and bone conduction headsets. Specifically, bone conduction earphones are worn around the ears and transmit sound through vibration. This allows the user to hear external sounds without blocking the ears, improving safety. The bone conduction headset is worn on the head and similarly transmits sound through vibration. This allows the user to hear the sound comfortably even when used for a long time. The audio transmission unit can transmit the clear sound processed by the removal unit to the user without any discomfort. Furthermore, the audio transmission unit has a function of adjusting the volume and sound quality, and can provide an audio experience according to the user's preferences. For example, the audio transmission unit can be equipped with a function of automatically adjusting the volume and an equalizer function of emphasizing low and high tones. This allows the audio transmission unit to provide the user with high-quality audio and achieve a comfortable audio experience.

[0033] 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 auxiliary GPS (A-GPS). Specifically, dual-band GPS is a technology that acquires highly accurate location information using multiple frequency bands, and auxiliary GPS (A-GPS) is a technology that improves the acquisition speed of location information by using base station information of mobile phones. By combining these technologies, the location information acquisition unit can acquire highly accurate location information in real time regardless of whether it is indoors or outdoors. Furthermore, the location information acquisition unit has a function of adjusting the acquisition frequency and accuracy of the location information, and can efficiently acquire necessary information while minimizing battery consumption. For example, by acquiring location information frequently while moving and acquiring it less frequently while stationary, it is possible to suppress battery consumption. As a result, the location information acquisition unit can acquire the user's location information accurately and efficiently, and improve the performance of the entire system.

[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 surrounding weather information and traffic information such as traffic congestion information. Specifically, the AI ​​analyzes surrounding weather information based on the location information and notifies the user. For example, if the weather gets bad while the user is out, the AI ​​analyzes the weather information and notifies the user. In addition, 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 sale information of that store and notifies the user. Furthermore, the information providing unit can learn the user's past behavior history and preferences and provide more personalized information. For example, if the user prefers products of a particular brand, sale information of that brand is provided preferentially. In addition, the information providing unit can provide optimal information to the user based on information updated in real time. For example, when traffic congestion information is updated, the AI ​​calculates the optimal detour route and notifies the user. In this way, the information providing unit can always provide the latest information to the user and support a comfortable life.

[0035] The information providing unit can provide at least one of surrounding weather information and traffic congestion information using AI. The information providing unit can provide surrounding weather information using AI, for example. 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. Part or all of the above-mentioned processing in the information providing unit can be performed using AI, for example, or can 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 an analysis result.

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

[0037] The sound collection unit can collect surrounding sounds with a microphone. The sound collection unit collects surrounding sounds using, for example, a microphone. The 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 surrounding noise and collecting clear sounds. As a result, the sound collection unit collects surrounding sounds with a microphone, thereby improving the accuracy of sound collection. A part 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 sound data collected by a microphone into AI and outputs an analysis result.

[0038] The removal unit can filter ambient noise and extract only conversation sounds. The removal unit can remove ambient noise using, for example, frequency filtering. For example, sounds in a specific frequency band are filtered to extract only conversation sounds. The removal unit can also remove noise using a noise reduction algorithm. For example, noise is analyzed and removed using AI. As a result, the removal unit filters ambient noise and extracts only conversation sounds, 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 to AI and outputs an analysis result 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, and therefore has high accuracy. The auxiliary GPS (A-GPS) uses a mobile phone network to acquire location information even in places where the GPS signal is weak. As a result, the location information acquisition unit improves the acquisition accuracy of location information by using GPS technology. A part or all of the above-mentioned 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 an analysis result.

[0040] The sound collection unit can analyze the user's past sound environment history and select the optimal collection method when collecting sounds. For example, the sound collection unit refers to past recording data to analyze the user's past sound environment history. For example, the sound collection unit preferentially collects sounds that the user liked to hear in the past. The sound collection unit can also filter sounds that the user avoided in the past so as not to collect them. Furthermore, the sound collection unit can collect sounds suitable for a specific time period from the user's past sound environment history. For example, the optimal collection method is selected based on sounds that the user heard 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. Part 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 past recording data into AI and outputs an analysis result.

[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 exercise and preferentially collects sounds related to the surrounding safety. In addition, when the user is reading, the activity sensor can detect the stationary state and filter unnecessary sounds to maintain a quiet environment. Furthermore, when the user is in a meeting, the activity sensor can detect a conversation, preferentially collect conversation sounds, and remove noise. This allows the sound collection unit to perform filtering based on the user's current activity status, thereby enabling appropriate sound collection. Part 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 data from the activity sensor into AI and outputs analysis results.

[0042] When collecting sounds, the sound collection unit can preferentially collect highly relevant sounds in consideration of the geographical location information of the user. The sound collection unit uses, for example, a GPS to acquire the geographical location information of the user. For example, when the user is in a park, the GPS detects the location of the park and preferentially collects natural sounds. In addition, when the user is in a city, the sound collection unit can also detect the location in the city and preferentially collect traffic sounds and people's voices. Furthermore, when the user is at home, the sound collection unit can also detect the location of the home using the GPS and preferentially collect sounds from home appliances and voices of family members. This allows the sound collection unit to preferentially collect highly relevant sounds in consideration of the geographical location information of the user. Part or all of the above-mentioned 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 to AI and outputs analysis results.

[0043] The sound collection unit can analyze the social media activity of the user and collect related sounds when collecting sounds. For example, the sound collection unit refers to social media data to analyze the social media activity of the user. For example, if the user posts about music, the sound collection unit preferentially collects the music. In addition, if the user participates in an event, the sound collection unit can also collect sounds related to the event. Furthermore, if the user posts about a specific topic, the sound collection unit can also collect sounds related to the topic. In this way, the sound collection unit can collect related sounds by analyzing the social media activity of the user. Part 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 an analysis result.

[0044] The removal unit can analyze the surrounding environmental sounds in real time and perform optimal filtering when removing noise. The removal unit uses, for example, streaming data analysis technology to analyze the surrounding environmental sounds in real time. For example, the surrounding traffic sounds are analyzed in real time to leave only important sounds. The removal unit can also analyze the voices of people in the vicinity in real time and extract only conversation sounds. Furthermore, the removal unit can also analyze the surrounding natural sounds in real time to maintain a relaxing sound environment. This allows the removal unit to perform optimal filtering by analyzing the surrounding environmental sounds in real time. Part or all of the above-mentioned processing in the removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the removal unit can perform filtering using an AI model that inputs environmental sound data collected in real time to AI and outputs the analysis results.

[0045] The removal unit can optimize the removal algorithm by referring to the user's past sound environment data when removing noise. The removal unit, for example, uses past recording data to refer to the user's past sound environment data. For example, the removal unit leaves sounds that the user liked to listen to in the past and removes other sounds. The removal unit can 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 from 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. A part or all of the above-mentioned processing in the removal unit may be performed, for example, using AI, or may be performed without using AI. For example, the removal unit can optimize the noise removal algorithm by inputting past recording data into AI and using an AI model that outputs an analysis result.

[0046] The removal unit can perform optimal filtering in consideration of the geographical location information of the user when removing noise. The removal unit uses, for example, a GPS to acquire the geographical location information of the user. For example, when the user is in a park, the GPS detects the location of the park, leaves natural sounds, and removes traffic sounds. In addition, when the user is in a city, the removal unit can also detect the location in the city using the GPS, leave conversation sounds, and remove background noise. Furthermore, when the user is at home, the removal unit can also detect the location of the home using the GPS, leave home appliance sounds, and remove external noise. This allows the removal unit to perform optimal filtering in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the removal unit can perform filtering using an AI model that inputs GPS data to 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 leaves sounds related to the surrounding safety and removes other sounds. In addition, when the user is reading, the removal unit can also remove unnecessary sounds to maintain a quiet environment. 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. Part or all of the above-mentioned processing in the removal unit may be performed, for example, using AI, or may be performed without using AI. For example, the removal unit can customize the removal algorithm by using an AI model that inputs past activity data into AI and outputs analysis results.

[0048] The voice transmission unit can select the optimal transmission method by referring to the user's past voice transmission history when transmitting voice. 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 preferentially transmits sounds that the user has liked to hear in the past. The voice transmission unit can also filter sounds that the user has avoided in the past and not transmit them. Furthermore, the voice transmission unit can also transmit sounds suitable for a specific time period from the user's past voice transmission history. Thereby, the voice transmission unit can select the optimal transmission method by referring to the user's past voice transmission history. A part or all of the above-mentioned processing in the voice transmission unit may be performed, for example, using AI or may be performed without using AI. For example, the voice transmission unit can transmit sounds using an AI model that inputs past voice data into AI and outputs an analysis result.

[0049] The voice transmission unit can customize the transmission method based on the user's current activity status when transmitting voice. The voice transmission 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 transmits sounds related to the surrounding safety with priority. In addition, when the user is reading, the activity sensor can detect the stationary state and filter unnecessary sounds to maintain a quiet environment. Furthermore, when the user is in a meeting, the activity sensor can detect a conversation, transmit the conversation sound with priority, and remove noise. This allows the voice transmission unit to customize the transmission method based on the user's current activity status, thereby enabling appropriate voice transmission. Part or all of the above-mentioned processing in the voice transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice transmission unit can transmit sound using an AI model that inputs data from the activity sensor into AI and outputs an analysis result.

[0050] The voice transmission unit can select the optimal transmission method in consideration of the geographical location information of the user when transmitting voice. The voice transmission unit uses, for example, a GPS to acquire the geographical location information of the user. For example, when the user is in a park, the GPS detects the location of the park and transmits natural sounds preferentially. In addition, when the user is in a city, the voice transmission unit can also detect the location in the city and transmit traffic sounds and people's voices preferentially. Furthermore, when the user is at home, the voice transmission unit can detect the location of the home using the GPS and transmit the sounds of home appliances and the voices of family members preferentially. This allows the voice transmission unit to select the optimal transmission method in consideration of the geographical location information of the user. Part or all of the above-mentioned 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 an analysis result.

[0051] The voice transmission unit can analyze the social media activity of the user and transmit the related voice when transmitting the voice. For example, the voice transmission unit refers to social media data to analyze the social media activity of the user. For example, if the user posts about music, the music is preferentially transmitted. In addition, if the user participates in an event, the voice transmission unit can also transmit voice related to the event. Furthermore, if the user posts about a specific topic, the voice transmission unit can also transmit voice related to the topic. In this way, the voice transmission unit can transmit the related voice by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the voice transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice transmission unit can transmit the sound using an AI model that inputs social media data into AI and outputs the analysis result.

[0052] The location information acquisition unit can select the optimal acquisition method by referring to the user's past movement history when acquiring location information. The location information acquisition unit, for example, uses past movement data to refer to the user's past movement history. For example, the location information acquisition unit preferentially acquires places 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 places that the user has avoided in the past so as not to acquire them. This allows the location information acquisition unit to select the optimal location information acquisition method by referring to the user's past movement history. Part or all of the above-mentioned processing in the location information acquisition unit may be performed, for example, using 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 an analysis result.

[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 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 exercise, acquires location information in real time, and provides a safe route. In addition, when the user is reading, the location information acquisition unit can detect the stationary state of the activity sensor, set the acquisition frequency of location information low, and suppress 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. As a result, the location information acquisition unit can acquire appropriate location information by customizing the acquisition method based on the user's current activity status. Part or all of the above-mentioned 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 data from the activity sensor into AI and outputs an analysis result.

[0054] The location information acquisition unit can select the optimal acquisition method in consideration of the geographical location information of the user when acquiring the location information. The location information acquisition unit uses, for example, a GPS to acquire the geographical location information of the user. For example, when the user is in an urban area, the GPS detects the location of the urban area and preferentially acquires the location information of public transportation. In addition, when the user is in the suburbs, the GPS can detect the location of the suburbs and preferentially acquire location information suitable for traveling by car. Furthermore, when the user is in a tourist spot, the GPS can detect the location of the tourist spot and preferentially acquire the location information of the tourist spot. This allows the location information acquisition unit to select the optimal acquisition method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the location information acquisition unit may be performed, for example, using 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 an analysis result.

[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, when the user is participating in a specific event, the location information of the event is acquired preferentially. In addition, when the user posts about a specific place, the location information acquisition unit can also acquire location information of the place preferentially. Furthermore, when the user is traveling, the location information acquisition unit can also acquire location information of the travel destination preferentially. In this way, the location information acquisition unit can acquire related location information by analyzing the user's social media activity. A part or all of the above-mentioned 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 an analysis result.

[0056] The information providing unit can select optimal information by referring to the user's past information acquisition history when providing information. The information providing unit, for example, uses past information data to refer to the user's past information acquisition history. For example, information that the user has preferred to acquire in the past is preferentially provided. The information providing unit can also filter information that the user has avoided in the past so as not to provide it. Furthermore, the information providing unit can also provide information suitable for a specific time period from the user's past information acquisition history. This allows the information providing unit to select optimal information by referring to the user's past information acquisition history. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using 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 an analysis result.

[0057] The information providing unit can customize information based on the user's current activity status when providing information. The information providing 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 exercise and provides information related to the exercise. In addition, when the user is reading, the information providing unit can also detect the stationary state and provide information related to reading. Furthermore, when the user is in a meeting, the activity sensor can detect the meeting and provide information related to the meeting. This allows the information providing unit to customize information based on the user's current activity status, thereby enabling appropriate information provision. Part or all of the above-mentioned processing in the information providing unit may be performed, for example, using 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 an analysis result.

[0058] The information providing unit can select optimal information in consideration of the geographical location information of the user when providing information. The information providing unit uses, for example, a GPS to acquire the geographical location information of the user. For example, when the user is in an urban area, the GPS detects the location of the urban area and provides information on public transportation preferentially. In addition, when the user is in the suburbs, the GPS can detect the location of the suburbs and provide information suitable for traveling by car preferentially. Furthermore, when the user is in a tourist spot, the GPS can detect the location of the tourist spot and provide information on tourist spots preferentially. This allows the information providing unit to select optimal information in consideration of the geographical location information of the user. Part 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 GPS data into AI and outputs an analysis result.

[0059] The information providing unit can analyze the user's social media activity and provide related information when providing information. The information providing unit, for example, refers to social media data to analyze the user's social media activity. For example, when the user is participating in a specific event, the information providing unit can provide information related to the event. In addition, when the user posts about a specific place, the information providing unit can also provide information related to the place. Furthermore, when the user is traveling, the information providing unit can also provide information related to the travel destination. Thereby, the information providing unit can provide related information by analyzing the user's social media activity. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using 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 an analysis result.

[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 specify the direction of the sound and preferentially collect sounds from a specific direction. For example, when a user is having a conversation, the sound collection unit can specify the direction of the conversation partner and preferentially collect sounds from that direction. Also, when a user is listening to music, the sound collection unit can specify the direction of the speaker and preferentially collect sounds from that direction. Furthermore, when a user is out and about, the sound collection unit can specify the direction of surrounding traffic sounds and preferentially collect sounds related to safety. This allows the sound collection unit to collect sounds more appropriately by specifying the direction of the sound.

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

[0063] The audio transmission unit can adjust the frequency characteristics of the audio based on the user's hearing characteristics when transmitting the audio. For example, if the user has difficulty hearing high-pitched sounds, the audio transmission unit emphasizes and transmits the high-pitched sounds. Also, if the user has difficulty hearing low-pitched sounds, the audio transmission unit can also emphasize and transmit the low-pitched sounds. Furthermore, if the user has difficulty hearing sounds in a specific frequency band, the audio transmission unit can also emphasize and transmit the sounds in that frequency band. This allows the audio transmission unit to 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 moving speed. For example, when the user is moving at high speed, the location information acquisition unit sets the location information acquisition frequency high and provides real-time location information. Also, when 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, when 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 moving 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 particular 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 particular 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 of a particular region in the past, the information providing unit can also provide weather information of 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 process flow of the first embodiment will be briefly described 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 removal unit removes noise from the sounds collected by the sound collection unit. The 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 removal unit to the user via bone conduction. The audio transmission unit uses bone conduction technology to transmit the sound directly to the user's brain through the skull. Bone conduction 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 a technology such as GPS. GPS includes a dual-band GPS and an 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) The system according to the embodiment of the present invention is a device equipped with bone conduction and AI. This device is a device that collects surrounding voices while removing noise and delivers the voices directly to the brain when it is difficult to hear surrounding conversations. The system collects sounds around the user, removes noise, and then transmits the sounds to the user via bone conduction. In addition, it provides location information, traffic information such as weather and traffic congestion information in the surrounding conditions, and sale information when in a retail store. This eliminates the need to look at flyers in advance or check information on a mobile phone. 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 the like. Next, it includes a removal unit that removes noise from the collected sounds. This removal unit uses AI to analyze and remove noise. For example, it is possible to filter surrounding noise and extract only conversation sounds. The removed sounds are transmitted to the user via bone conduction. The voice transmission unit uses bone conduction technology to transmit sounds directly to the brain through the user's skull. This allows the user to hear sounds without blocking their ears. In addition, there is a location information acquisition unit that acquires the user's location information. This location information is acquired using a technology such as GPS. The information providing unit provides information according to the acquired location information. For example, when the 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 surrounding weather information and traffic congestion information. For example, when the weather gets bad while the user is out, the AI ​​analyzes the weather information and notifies the user. In addition, by providing traffic congestion information, the user can avoid traffic congestion. Furthermore, the information providing unit uses AI to provide sale information when the user is in a retail store or other store. For example, when the user is in a supermarket, the AI ​​analyzes the sale information of that store and notifies the user. This allows the user to obtain advantageous information in real time. In this way, the present invention can improve the convenience of the user by using bone conduction and AI to aggregate surrounding sounds while removing noise, and provide information according to location information and surrounding conditions. This allows the system to efficiently collect sounds around the user, remove noise, transmit voice, obtain location information, and provide information.

[0069] The system according to the embodiment includes a sound collection unit, a removal unit, a voice transmission unit, a position information acquisition unit, and an information provision unit. The sound collection unit collects sounds around the user. The sounds around the user include, for example, environmental sounds, conversation sounds, traffic sounds, etc., but are not limited to such examples. The sound collection unit collects surrounding sounds using, for example, a microphone. The microphone includes a directional microphone, an omnidirectional microphone, a noise canceling microphone, etc. The removal unit removes noise from the collected sounds. The removal unit analyzes and removes noise using AI. For example, the noise is removed using frequency filtering or a noise reduction algorithm. The removal unit filters, for example, the surrounding noise and extracts only the conversation sound. The voice transmission unit transmits the sound from which the noise has been removed by the removal unit to the user by bone conduction. The voice transmission unit transmits the sound directly to the brain through the user's skull using bone conduction technology. Bone conduction includes bone conduction earphones and bone conduction headsets. The position information acquisition unit acquires the user's position information. The location information acquisition unit acquires location information using technologies such as GPS. GPS includes dual-band GPS and auxiliary 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 gets bad while the user is out, the AI ​​analyzes the weather information and notifies the user. In addition, the information provision 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 sale information of the store 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 surrounding sounds using, for example, a microphone. The microphones include directional microphones, omnidirectional microphones, and noise-canceling microphones. Specifically, a directional microphone intensively collects sounds from a specific direction, while an omnidirectional microphone collects sounds evenly from all directions. A noise-canceling microphone has a function of reducing ambient noise and collecting clear voice. These microphones may be built into the user's device or may be 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, sound can be collected with high sensitivity in a quiet room and with low sensitivity in a noisy outdoor environment, thereby efficiently collecting only the necessary sounds. This enables the sound collection unit to collect high-quality sound data in a variety of environments, improving the performance of the entire system.

[0071] The removal unit removes noise from the collected sound. The removal unit uses AI to analyze and remove noise. Specifically, noise is removed using frequency filtering and noise reduction algorithms. Frequency filtering removes unnecessary noise by emphasizing or suppressing sounds in a specific frequency band. The noise reduction algorithm analyzes the collected voice data, identifies and removes noise components. AI uses these algorithms to process voice data in real time to generate clear voice. For example, it can filter ambient noise and extract only conversation sounds. In addition, AI learns past voice data and noise patterns to achieve more accurate noise removal. This allows the removal unit to effectively remove unnecessary noise from the collected voice data and provide the user with clear voice. Furthermore, the removal unit can select the optimal noise removal algorithm according to different environments and situations. For example, mild noise reduction is performed in a quiet indoor environment, and strong noise reduction is applied in a noisy outdoor environment. This allows the removal unit to always maintain optimal voice quality and support the user's comfortable voice experience.

[0072] The audio transmission unit transmits the sound from which the noise has been removed by the removal unit to the user by bone conduction. The audio transmission unit transmits the sound directly to the brain through the user's skull using bone conduction technology. Bone conduction includes bone conduction earphones and bone conduction headsets. Specifically, bone conduction earphones are worn around the ears and transmit sound through vibration. This allows the user to hear external sounds without blocking the ears, improving safety. The bone conduction headset is worn on the head and similarly transmits sound through vibration. This allows the user to hear the sound comfortably even when used for a long time. The audio transmission unit can transmit the clear sound processed by the removal unit to the user without any discomfort. Furthermore, the audio transmission unit has a function of adjusting the volume and sound quality, and can provide an audio experience according to the user's preferences. For example, the audio transmission unit can be equipped with a function of automatically adjusting the volume and an equalizer function of emphasizing low and high tones. This allows the audio transmission unit to provide the user with high-quality audio and achieve a comfortable audio experience.

[0073] 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 auxiliary GPS (A-GPS). Specifically, dual-band GPS is a technology that acquires highly accurate location information using multiple frequency bands, and auxiliary GPS (A-GPS) is a technology that improves the acquisition speed of location information by using base station information of mobile phones. By combining these technologies, the location information acquisition unit can acquire highly accurate location information in real time regardless of whether it is indoors or outdoors. Furthermore, the location information acquisition unit has a function of adjusting the acquisition frequency and accuracy of the location information, and can efficiently acquire necessary information while minimizing battery consumption. For example, by acquiring location information frequently while moving and acquiring it less frequently while stationary, it is possible to suppress battery consumption. As a result, the location information acquisition unit can acquire the user's location information accurately and efficiently, and improve the performance of the entire system.

[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 surrounding weather information and traffic information such as traffic congestion information. Specifically, the AI ​​analyzes surrounding weather information based on the location information and notifies the user. For example, if the weather gets bad while the user is out, the AI ​​analyzes the weather information and notifies the user. In addition, 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 sale information of that store and notifies the user. Furthermore, the information providing unit can learn the user's past behavior history and preferences and provide more personalized information. For example, if the user prefers products of a particular brand, sale information of that brand is provided preferentially. In addition, the information providing unit can provide optimal information to the user based on information updated in real time. For example, when traffic congestion information is updated, the AI ​​calculates the optimal detour route and notifies the user. In this way, the information providing unit can always provide the latest information to the user and support a comfortable life.

[0075] The information providing unit can provide at least one of surrounding weather information and traffic congestion information using AI. The information providing unit can provide surrounding weather information using AI, for example. 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. Part or all of the above-mentioned processing in the information providing unit can be performed using AI, for example, or can 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 an analysis result.

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

[0077] The sound collection unit can collect surrounding sounds with a microphone. The sound collection unit collects surrounding sounds using, for example, a microphone. The 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 surrounding noise and collecting clear sounds. As a result, the sound collection unit collects surrounding sounds with a microphone, thereby improving the accuracy of sound collection. A part 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 sound data collected by a microphone into AI and outputs an analysis result.

[0078] The removal unit can filter ambient noise and extract only conversation sounds. The removal unit can remove ambient noise using, for example, frequency filtering. For example, sounds in a specific frequency band are filtered to extract only conversation sounds. The removal unit can also remove noise using a noise reduction algorithm. For example, noise is analyzed and removed using AI. As a result, the removal unit filters ambient noise and extracts only conversation sounds, 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 to AI and outputs an analysis result 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, and therefore has high accuracy. The auxiliary GPS (A-GPS) uses a mobile phone network to acquire location information even in places where the GPS signal is weak. As a result, the location information acquisition unit improves the acquisition accuracy of location information by using GPS technology. A part or all of the above-mentioned 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 an analysis result.

[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 uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the user's facial expression is captured by a camera, and the emotion is estimated using an emotion estimation algorithm. The sound collection unit can also estimate the user's emotion using a voice analysis technology. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. Furthermore, the sound collection unit can also estimate the user's emotion using a biosensor. For example, the heart rate and electrodermal activity are measured to estimate the emotion. As a result, the sound collection unit estimates the user's emotion and adjusts the timing of sound collection based on the estimated user's emotion, thereby enabling more appropriate sound collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sound collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sound collection unit can input image data of a user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0081] The sound collection unit can analyze the user's past sound environment history and select the optimal collection method when collecting sounds. For example, the sound collection unit refers to past recording data to analyze the user's past sound environment history. For example, the sound collection unit preferentially collects sounds that the user liked to hear in the past. The sound collection unit can also filter sounds that the user avoided in the past so as not to collect them. Furthermore, the sound collection unit can collect sounds suitable for a specific time period from the user's past sound environment history. For example, the optimal collection method is selected based on sounds that the user heard 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. Part 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 past recording data into AI and outputs an analysis result.

[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 exercise and preferentially collects sounds related to the surrounding safety. In addition, when the user is reading, the activity sensor can detect the stationary state and filter unnecessary sounds to maintain a quiet environment. Furthermore, when the user is in a meeting, the activity sensor can detect a conversation, preferentially collect conversation sounds, and remove noise. This allows the sound collection unit to perform filtering based on the user's current activity status, thereby enabling appropriate sound collection. Part 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 data from the activity sensor into AI and outputs analysis results.

[0083] The sound collection unit can estimate the user's emotion and determine the priority of sounds to be collected based on the estimated user's emotion. The sound collection unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the user's facial expression is photographed with a camera, and the emotion is estimated using an emotion estimation algorithm. The sound collection unit can also estimate the user's emotion using a voice analysis technology. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. Furthermore, the sound collection unit can also estimate the user's emotion using a biosensor. For example, the heart rate and electrodermal activity are measured to estimate the emotion. As a result, the sound collection unit estimates the user's emotion and determines the priority of sounds to be collected based on the estimated user's emotion, thereby enabling more appropriate sound collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sound collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sound collection unit can input image data of a user captured by 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 preferentially collect highly relevant sounds in consideration of the geographical location information of the user. The sound collection unit uses, for example, a GPS to acquire the geographical location information of the user. For example, when the user is in a park, the GPS detects the location of the park and preferentially collects natural sounds. In addition, when the user is in a city, the sound collection unit can also detect the location in the city and preferentially collect traffic sounds and people's voices. Furthermore, when the user is at home, the sound collection unit can also detect the location of the home using the GPS and preferentially collect sounds from home appliances and voices of family members. This allows the sound collection unit to preferentially collect highly relevant sounds in consideration of the geographical location information of the user. Part or all of the above-mentioned 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 to AI and outputs analysis results.

[0085] The sound collection unit can analyze the social media activity of the user and collect related sounds when collecting sounds. For example, the sound collection unit refers to social media data to analyze the social media activity of the user. For example, if the user posts about music, the sound collection unit preferentially collects the music. In addition, if the user participates in an event, the sound collection unit can also collect sounds related to the event. Furthermore, if the user posts about a specific topic, the sound collection unit can also collect sounds related to the topic. In this way, the sound collection unit can collect related sounds by analyzing the social media activity of the user. Part 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 an analysis result.

[0086] The removal unit can estimate the user's emotion and adjust the strength of noise removal based on the estimated user's emotion. The removal unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the user's facial expression is captured by a camera, and the emotion is estimated using an emotion estimation algorithm. The removal unit can also estimate the user's emotion using a voice analysis technology. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. Furthermore, the removal unit can estimate the user's emotion using a biosensor. For example, the heart rate and electrodermal activity are measured to estimate the emotion. Thereby, the removal unit can provide a more appropriate sound environment by estimating the user's emotion and adjusting the strength of noise removal based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the removal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the removal unit can input image data of a user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0087] The removal unit can analyze the surrounding environmental sounds in real time and perform optimal filtering when removing noise. The removal unit uses, for example, streaming data analysis technology to analyze the surrounding environmental sounds in real time. For example, the surrounding traffic sounds are analyzed in real time to leave only important sounds. The removal unit can also analyze the voices of people in the vicinity in real time and extract only conversation sounds. Furthermore, the removal unit can also analyze the surrounding natural sounds in real time to maintain a relaxing sound environment. This allows the removal unit to perform optimal filtering by analyzing the surrounding environmental sounds in real time. Part or all of the above-mentioned processing in the removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the removal unit can perform filtering using an AI model that inputs environmental sound data collected in real time to AI and outputs the analysis results.

[0088] The removal unit can optimize the removal algorithm by referring to the user's past sound environment data when removing noise. The removal unit, for example, uses past recording data to refer to the user's past sound environment data. For example, the removal unit leaves sounds that the user liked to listen to in the past and removes other sounds. The removal unit can 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 from 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. A part or all of the above-mentioned processing in the removal unit may be performed, for example, using AI, or may be performed without using AI. For example, the removal unit can optimize the noise removal algorithm by inputting past recording data into AI and using an AI model that outputs an analysis result.

[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 uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the user's facial expression is captured by a camera, and the emotion is estimated using an emotion estimation algorithm. The removal unit can also estimate the user's emotion using a voice analysis technology. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. Furthermore, the removal unit can estimate the user's emotion using a biosensor. For example, the heart rate and electrodermal activity are measured to estimate the emotion. As a result, the removal unit can provide a more appropriate sound environment by estimating the user's emotion and selecting the type of noise to be removed based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the removal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the removal unit can input image data of a user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0090] The removal unit can perform optimal filtering in consideration of the geographical location information of the user when removing noise. The removal unit uses, for example, a GPS to acquire the geographical location information of the user. For example, when the user is in a park, the GPS detects the location of the park, leaves natural sounds, and removes traffic sounds. In addition, when the user is in a city, the removal unit can also detect the location in the city using the GPS, leave conversation sounds, and remove background noise. Furthermore, when the user is at home, the removal unit can also detect the location of the home using the GPS, leave home appliance sounds, and remove external noise. This allows the removal unit to perform optimal filtering in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the removal unit may be performed using, for example, AI, or may be performed without using AI. For example, the removal unit can perform filtering using an AI model that inputs GPS data to 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 leaves sounds related to the surrounding safety and removes other sounds. In addition, when the user is reading, the removal unit can also remove unnecessary sounds to maintain a quiet environment. 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. Part or all of the above-mentioned processing in the removal unit may be performed, for example, using AI, or may be performed without using AI. For example, the removal unit can customize the removal algorithm by 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 the voice transmission based on the estimated user's emotion. The voice transmission unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the user's facial expression is photographed with a camera, and the emotion is estimated using an emotion estimation algorithm. The voice transmission unit can also estimate the user's emotion using a voice analysis technology. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. Furthermore, the voice transmission unit can also estimate the user's emotion using a biosensor. For example, the heart rate and the electrodermal activity are measured to estimate the emotion. As a result, the voice transmission unit estimates the user's emotion and adjusts the intensity of the voice transmission based on the estimated user's emotion, thereby enabling more appropriate voice transmission. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice transmission unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the voice transmission unit can input image data of a user captured by a camera to the generation AI and have the generation AI estimate the user's emotions.

[0093] The voice transmission unit can select the optimal transmission method by referring to the user's past voice transmission history when transmitting voice. 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 preferentially transmits sounds that the user has liked to hear in the past. The voice transmission unit can also filter sounds that the user has avoided in the past and not transmit them. Furthermore, the voice transmission unit can also transmit sounds suitable for a specific time period from the user's past voice transmission history. Thereby, the voice transmission unit can select the optimal transmission method by referring to the user's past voice transmission history. A part or all of the above-mentioned processing in the voice transmission unit may be performed, for example, using AI or may be performed without using AI. For example, the voice transmission unit can transmit sounds using an AI model that inputs past voice data into AI and outputs an analysis result.

[0094] The voice transmission unit can customize the transmission method based on the user's current activity status when transmitting voice. The voice transmission 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 transmits sounds related to the surrounding safety with priority. In addition, when the user is reading, the activity sensor can detect the stationary state and filter unnecessary sounds to maintain a quiet environment. Furthermore, when the user is in a meeting, the activity sensor can detect a conversation, transmit the conversation sound with priority, and remove noise. This allows the voice transmission unit to customize the transmission method based on the user's current activity status, thereby enabling appropriate voice transmission. Part or all of the above-mentioned processing in the voice transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice transmission unit can transmit sound using an AI model that inputs data from the activity sensor into AI and outputs an analysis result.

[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 uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the user's facial expression is photographed with a camera, and the emotion is estimated using an emotion estimation algorithm. The voice transmission unit can also estimate the user's emotion using a voice analysis technology. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. Furthermore, the voice transmission unit can also estimate the user's emotion using a biosensor. For example, the heart rate and the electrodermal activity are measured to estimate the emotion. As a result, the voice transmission unit estimates the user's emotion and determines the priority of the voice to be transmitted based on the estimated user's emotion, thereby enabling more appropriate voice transmission. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice transmission unit can input image data of a user captured by a camera to the generation AI and have the generation AI estimate the user's emotions.

[0096] The voice transmission unit can select the optimal transmission method in consideration of the geographical location information of the user when transmitting voice. The voice transmission unit uses, for example, a GPS to acquire the geographical location information of the user. For example, when the user is in a park, the GPS detects the location of the park and transmits natural sounds preferentially. In addition, when the user is in a city, the voice transmission unit can also detect the location in the city and transmit traffic sounds and people's voices preferentially. Furthermore, when the user is at home, the voice transmission unit can detect the location of the home using the GPS and transmit the sounds of home appliances and the voices of family members preferentially. This allows the voice transmission unit to select the optimal transmission method in consideration of the geographical location information of the user. Part or all of the above-mentioned 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 an analysis result.

[0097] The voice transmission unit can analyze the social media activity of the user and transmit the related voice when transmitting the voice. For example, the voice transmission unit refers to social media data to analyze the social media activity of the user. For example, if the user posts about music, the music is preferentially transmitted. In addition, if the user participates in an event, the voice transmission unit can also transmit voice related to the event. Furthermore, if the user posts about a specific topic, the voice transmission unit can also transmit voice related to the topic. In this way, the voice transmission unit can transmit the related voice by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the voice transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice transmission unit can transmit the sound using an AI model that inputs social media data into AI and outputs the analysis result.

[0098] The location information acquisition unit can estimate the user's emotion and adjust the frequency of acquiring location information based on the estimated user's emotion. The location information acquisition unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the user's facial expression is captured by a camera, and the emotion is estimated using an emotion estimation algorithm. The location information acquisition unit can also estimate the user's emotion using a voice analysis technology. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. Furthermore, the location information acquisition unit can also estimate the user's emotion using a biosensor. For example, the heart rate and electrodermal activity are measured to estimate the emotion. As a result, the location information acquisition unit estimates the user's emotion and adjusts the frequency of acquiring location information based on the estimated user's emotion, thereby enabling more appropriate acquisition of location information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the 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 captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0099] The location information acquisition unit can select the optimal acquisition method by referring to the user's past movement history when acquiring location information. The location information acquisition unit, for example, uses past movement data to refer to the user's past movement history. For example, the location information acquisition unit preferentially acquires places 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 places that the user has avoided in the past so as not to acquire them. This allows the location information acquisition unit to select the optimal location information acquisition method by referring to the user's past movement history. Part or all of the above-mentioned processing in the location information acquisition unit may be performed, for example, using 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 an analysis result.

[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 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 exercise, acquires location information in real time, and provides a safe route. In addition, when the user is reading, the location information acquisition unit can detect the stationary state of the activity sensor, set the acquisition frequency of location information low, and suppress 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. As a result, the location information acquisition unit can acquire appropriate location information by customizing the acquisition method based on the user's current activity status. Part or all of the above-mentioned 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 data from the activity sensor into AI and outputs an analysis result.

[0101] The location information acquisition unit can estimate the user's emotion and determine the priority of the location information to be acquired based on the estimated user's emotion. The location information acquisition unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the user's facial expression is captured by a camera, and the emotion is estimated using an emotion estimation algorithm. The location information acquisition unit can also estimate the user's emotion using a voice analysis technology. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. Furthermore, the location information acquisition unit can also estimate the user's emotion using a biosensor. For example, the heart rate and electrodermal activity are measured to estimate the emotion. As a result, the location information acquisition unit can estimate the user's emotion and determine the priority of the location information to be acquired based on the estimated user's emotion, thereby enabling more appropriate acquisition of location information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the 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 captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0102] The location information acquisition unit can select the optimal acquisition method in consideration of the geographical location information of the user when acquiring the location information. The location information acquisition unit uses, for example, a GPS to acquire the geographical location information of the user. For example, when the user is in an urban area, the GPS detects the location of the urban area and preferentially acquires the location information of public transportation. In addition, when the user is in the suburbs, the GPS can detect the location of the suburbs and preferentially acquire location information suitable for traveling by car. Furthermore, when the user is in a tourist spot, the GPS can detect the location of the tourist spot and preferentially acquire the location information of the tourist spot. This allows the location information acquisition unit to select the optimal acquisition method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the location information acquisition unit may be performed, for example, using 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 an analysis result.

[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, when the user is participating in a specific event, the location information of the event is acquired preferentially. In addition, when the user posts about a specific place, the location information acquisition unit can also acquire location information of the place preferentially. Furthermore, when the user is traveling, the location information acquisition unit can also acquire location information of the travel destination preferentially. In this way, the location information acquisition unit can acquire related location information by analyzing the user's social media activity. A part or all of the above-mentioned 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 an analysis result.

[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 uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the user's facial expression is captured by a camera, and the emotion is estimated using an emotion estimation algorithm. The information providing unit can also estimate the user's emotion using a voice analysis technology. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. Furthermore, the information providing unit can also estimate the user's emotion using a biosensor. For example, the heart rate and electrodermal activity are measured to estimate the emotion. As a result, 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, thereby enabling more appropriate information to be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using an AI, or may be performed without using an 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] The information providing unit can select optimal information by referring to the user's past information acquisition history when providing information. The information providing unit, for example, uses past information data to refer to the user's past information acquisition history. For example, information that the user has preferred to acquire in the past is preferentially provided. The information providing unit can also filter information that the user has avoided in the past so as not to provide it. Furthermore, the information providing unit can also provide information suitable for a specific time period from the user's past information acquisition history. This allows the information providing unit to select optimal information by referring to the user's past information acquisition history. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using 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 an analysis result.

[0106] The information providing unit can customize information based on the user's current activity status when providing information. The information providing 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 exercise and provides information related to the exercise. In addition, when the user is reading, the information providing unit can also detect the stationary state and provide information related to reading. Furthermore, when the user is in a meeting, the activity sensor can detect the meeting and provide information related to the meeting. This allows the information providing unit to customize information based on the user's current activity status, thereby enabling appropriate information provision. Part or all of the above-mentioned processing in the information providing unit may be performed, for example, using 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 an analysis result.

[0107] The information providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. The information providing unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the user's facial expression is captured by a camera, and the emotion is estimated using an emotion estimation algorithm. The information providing unit can also estimate the user's emotion using a voice analysis technology. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. Furthermore, the information providing unit can also estimate the user's emotion using a biosensor. For example, the heart rate and electrodermal activity are measured to estimate the emotion. As a result, the information providing unit can provide more appropriate information by estimating the user's emotion and determining the priority of information to be provided based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the information providing unit may be performed using, for example, an AI, or may be performed without using an 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] The information providing unit can select optimal information in consideration of the geographical location information of the user when providing information. The information providing unit uses, for example, a GPS to acquire the geographical location information of the user. For example, when the user is in an urban area, the GPS detects the location of the urban area and provides information on public transportation preferentially. In addition, when the user is in the suburbs, the GPS can detect the location of the suburbs and provide information suitable for traveling by car preferentially. Furthermore, when the user is in a tourist spot, the GPS can detect the location of the tourist spot and provide information on tourist spots preferentially. This allows the information providing unit to select optimal information in consideration of the geographical location information of the user. Part 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 GPS data into AI and outputs an analysis result.

[0109] The information providing unit can analyze the user's social media activity and provide related information when providing information. The information providing unit, for example, refers to social media data to analyze the user's social media activity. For example, when the user is participating in a specific event, the information providing unit can provide information related to the event. In addition, when the user posts about a specific place, the information providing unit can also provide information related to the place. Furthermore, when the user is traveling, the information providing unit can also provide information related to the travel destination. Thereby, the information providing unit can provide related information by analyzing the user's social media activity. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using 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 an analysis result.

[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 specify the direction of the sound and preferentially collect sounds from a specific direction. For example, when a user is having a conversation, the sound collection unit can specify the direction of the conversation partner and preferentially collect sounds from that direction. Also, when a user is listening to music, the sound collection unit can specify the direction of the speaker and preferentially collect sounds from that direction. Furthermore, when a user is out and about, the sound collection unit can specify the direction of surrounding traffic sounds and preferentially collect sounds related to safety. This allows the sound collection unit to collect sounds more appropriately by specifying the direction of the sound.

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

[0113] The audio transmission unit can adjust the frequency characteristics of the audio based on the user's hearing characteristics when transmitting the audio. For example, if the user has difficulty hearing high-pitched sounds, the audio transmission unit emphasizes and transmits the high-pitched sounds. Also, if the user has difficulty hearing low-pitched sounds, the audio transmission unit can also emphasize and transmit the low-pitched sounds. Furthermore, if the user has difficulty hearing sounds in a specific frequency band, the audio transmission unit can also emphasize and transmit the sounds in that frequency band. This allows the audio transmission unit to 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 moving speed. For example, when the user is moving at high speed, the location information acquisition unit sets the location information acquisition frequency high and provides real-time location information. Also, when 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, when 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 moving 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 particular 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 particular 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 of a particular region in the past, the information providing unit can also provide weather information of 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 be collected based on the estimated user's emotions. For example, when the user is relaxed, the sound collection unit preferentially collects natural sounds and calm sounds. When the user is concentrating, the sound collection unit can also remove ambient noise and collect only important sounds. Furthermore, when the user is excited, the sound collection unit can collect ambient sounds over a wide range and provide sounds that increase the user's excitement. This allows the sound collection unit to estimate the user's emotions and collect more appropriate sounds.

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

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

[0119] The location information acquisition unit can estimate the user's emotions and adjust the method of acquiring location information based on the estimated user's emotions. For example, when the user is relaxed, the location information acquisition unit can set the frequency of acquiring location information low to reduce battery consumption. Also, when 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, when the user is excited, the location information acquisition unit can adjust the method of acquiring location information and preferentially acquire information on places that will increase the user's excitement. 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, when the user is relaxed, the information providing unit can provide preferentially information suitable for relaxation. Also, when the user is concentrating, the information providing unit can also provide preferentially information that helps the user to concentrate. Furthermore, when the user is excited, the information providing unit can also provide preferentially information that increases the user's excitement. This allows the information providing unit to provide more appropriate information by estimating the user's emotions.

[0121] The process flow of the second embodiment will be briefly described 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 removal unit removes noise from the sounds collected by the sound collection unit. The 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 removal unit to the user via bone conduction. The audio transmission unit uses bone conduction technology to transmit the sound directly to the user's brain through the skull. Bone conduction 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 a technology such as GPS. GPS includes a dual-band GPS and an 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

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

[0126] For example, the sound collection unit can collect surrounding sounds using the microphone 38B and the 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, and the like using AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[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 the 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 the voice according to instructions from the processor 46.

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

[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 performed in a secure state.

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

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

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

[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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] For example, the sound collection unit can collect surrounding sounds using the microphone 238 and the 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 voice 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, and the like using AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes 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 type 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 the voice according to instructions from the processor 46.

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

[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 performed 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

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

[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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] For example, the sound collecting unit can collect surrounding sounds using the microphone 238 and the camera 42 of the headset type terminal 314. The removing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes and removes noise using AI. The audio transmitting unit is realized by the control unit 46A of the headset type terminal 314, and transmits sound to the user using bone conduction technology. The location information acquiring unit acquires location information using the GPS function of the headset type terminal 314. The information providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides surrounding weather information, traffic congestion information, and the like using AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes 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, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 the voice according to instructions from the processor 46.

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

[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 performed in a secure state.

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

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

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

[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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] For example, the sound collecting unit can collect surrounding sounds using the microphone 238 and the camera 42 of the robot 414. The removing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes and removes noise using AI. The voice transmitting 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 acquiring unit acquires location information using the GPS function of the robot 414. The information providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides surrounding weather information, traffic congestion information, and the like using AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0177] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

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

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

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

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

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

[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 Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

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

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

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

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

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

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

[0192] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

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

[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 the 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; A system characterized by: (Appendix 2) The information providing unit is Provide 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 is AI provides special offers when you're in a retail store or other outlet 2. The system of claim 1, (Appendix 4) The sound collecting unit is Collecting surrounding sounds with a microphone 2. The system of claim 1, (Appendix 5) The removal unit includes: Filter out background noise and extract only the conversation 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 is To estimate a 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 is 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 is When collecting sound, it filters it based on the user's current activity. 2. The system of claim 1, (Appendix 10) The sound collecting unit is The user's emotion is estimated, and the priority of sounds to be collected is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The sound collecting unit is When collecting sounds, the system takes into account the user's geographic location information and prioritizes the collection of sounds that are highly relevant to the user. 2. The system of claim 1, (Appendix 12) The sound collecting unit is When collecting sounds, the user's social media activity is analyzed and related sounds are collected. 2. The system of claim 1, (Appendix 13) The removal unit includes: Estimating a user's emotion and adjusting the strength of noise reduction based on the estimated user's emotion 2. The system of claim 1, (Appendix 14) The removal unit includes: When removing noise, it analyzes the surrounding environmental sounds in real time and performs optimal filtering. 2. The system of claim 1, (Appendix 15) The removal unit includes: When removing noise, the removal algorithm is optimized by referring to the user's past sound environment data. 2. The system of claim 1, (Appendix 16) The removal unit includes: A 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 includes: When removing noise, optimal filtering is performed by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 18) The removal unit includes: 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 is Estimating a user's emotion and adjusting the strength of the voice transmission based on the estimated user's emotion 2. The system of claim 1, (Appendix 20) The audio transmission unit is 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 is Customize voice transmission based on the user's current activity status 2. The system of claim 1, (Appendix 22) The audio transmission unit is A user's emotion is estimated, and a priority order of speech to be transmitted is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 23) The audio transmission unit is 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 is When a voice message is sent, the user's social media activity is analyzed and relevant voice messages are sent. 2. The system of claim 1, (Appendix 25) The location information acquisition unit A user's emotion is estimated, and the frequency of acquiring location information is adjusted based on the estimated user's emotion. 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 2. The system of claim 1, (Appendix 28) The location information acquisition unit A user's emotion is estimated, and a priority order of location information to be acquired is determined based on the estimated user's emotion. 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 is Inferring a user's emotions and adjusting the type of information provided based on the estimated user's emotions 2. The system of claim 1, (Appendix 32) The information providing unit is 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 is When providing information, customize it based on the user's current activity 2. The system of claim 1, (Appendix 34) The information providing unit is Estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 35) The information providing unit is When providing information, the most appropriate information is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 36) The information providing unit is 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 the 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; A system characterized by:

2. The information providing unit is Provide at least one of the following traffic information using AI: local weather information or traffic congestion information 2. The system of claim 1 .

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

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

5. The removal unit includes: Filter out background noise and extract only the conversation 2. The system of claim 1 .

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

7. The sound collecting unit is Estimating the user's emotion and adjusting timing of sound collection based on the estimated user's emotion.

2. The system of claim 1 .

8. The sound collecting unit is When collecting sounds, the user's past sound environment history is analyzed and the optimal collection method is selected.

2. The system of claim 1 .

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