system

Abstract

The system according to the embodiment aims to transmit visual and auditory information directly to the brains of people with visual or hearing impairments. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a transmission unit. The collection unit collects video and audio data. The analysis unit analyzes the data collected by the collection unit and learns how to generate electrical signals. The generation unit converts the video and audio data into electrical signals based on the data learned by the analysis unit. The transmission unit sends the electrical signals generated by the generation unit to the brain.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide visually or hearing impaired people with means to transmit visual or auditory information directly to the brain, and there is room for improvement.

[0005] The system according to the embodiment aims to transmit visual and auditory information directly to the brains of people with visual or hearing impairments. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a transmission unit. The collection unit collects video and audio data. The analysis unit analyzes the data collected by the collection unit and learns how to generate electrical signals. The generation unit converts the video and audio data into electrical signals based on the data learned by the analysis unit. The transmission unit sends the electrical signals generated by the generation unit to the brain. [Effects of the Invention]

[0007] The system according to the embodiment can transmit visual and auditory information directly to the brain of a person with visual or hearing impairments. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The visual and auditory assistance system according to an embodiment of the present invention is a technology that enables visually or hearing-impaired individuals to see and hear. This visual and auditory assistance system collects and analyzes video and audio data, converts it into electrical signals, and transmits them to the brain, thereby enabling visual and hearing-impaired individuals to see and hear. Specifically, a collection unit is first provided to collect video and audio data. This collection unit collects video and audio using sensors such as cameras and microphones. Next, an analysis unit is provided to analyze the collected data. This analysis unit uses AI to analyze the video and audio data and learns what kind of electrical signals are generated. A generation unit is provided to generate electrical signals based on the analysis results. This generation unit converts video and audio into electrical signals based on the data learned by the analysis unit. Finally, a transmission unit is provided to send the generated electrical signals to the brain. This transmission unit transmits the electrical signals generated by the generation unit to the brain, enabling visual and hearing-impaired individuals to see and hear. For example, a visual and auditory assistance system may collect video using a camera and sound using a microphone. The collected video and audio data is analyzed using AI, which learns what kind of electrical signals it will produce. Based on the learned data, the video and audio are converted into electrical signals and sent to the brain, allowing even those with visual or hearing impairments to see and hear. In this way, the visual and hearing assistance system enables those with visual or hearing impairments to see and hear.

[0029] The visual and auditory assistance system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a transmission unit. The collection unit collects video and audio data. The collection unit can collect video and audio data using sensors such as a camera and a microphone. For example, the collection unit collects video using a camera and audio using a microphone. The collection unit can also collect video and audio data in real time. The analysis unit analyzes the data collected by the collection unit and learns what kind of electrical signal the data will be. The analysis unit can analyze video and audio data using AI, for example, and learn what kind of electrical signal the data will be. For example, the analysis unit can analyze video and audio data using AI technology such as deep learning or neural networks and learn what kind of electrical signal the data will be. The generation unit converts video and audio data into electrical signals based on the data learned by the analysis unit. For example, the generation unit can convert video data and audio data into electrical signals based on the data learned by the analysis unit. For example, the generation unit converts video and audio data into electrical signals using a conversion algorithm. The transmitting unit sends the electrical signal generated by the generating unit to the brain. The transmitting unit can, for example, send the electrical signal generated by the generating unit to the brain. For example, the transmitting unit adjusts the transmission path and transmission timing to send the generated electrical signal to the brain. In this way, the visual and auditory assistance system according to the embodiment enables even a person with a visual or hearing impairment to see and hear.

[0030] The collection unit can collect video and sound using a camera or microphone sensor. The collection unit can, for example, collect video using a camera. For example, the collection unit collects video using a high-resolution camera. The collection unit can also collect sound using a microphone. For example, the collection unit collects sound using a high-sensitivity microphone. Furthermore, the collection unit can combine a camera and a microphone to simultaneously collect video and sound. For example, the collection unit combines a camera and a microphone to simultaneously collect video and sound and acquire data in real time. This allows the collection unit to efficiently collect video and sound data.

[0031] The analysis unit can use AI to analyze video and sound data and learn how to generate electrical signals. The analysis unit can, for example, use deep learning to analyze video and sound data. For example, the analysis unit can use a deep learning algorithm to analyze video and sound data and learn what kind of electrical signals are generated. The analysis unit can also analyze video and sound data using a neural network. For example, the analysis unit can use a neural network model to analyze video and sound data and learn what kind of electrical signals are generated. Furthermore, the analysis unit can combine multiple AI technologies to analyze video and sound data. For example, the analysis unit can combine deep learning and a neural network to analyze video and sound data and learn what kind of electrical signals are generated. This enables the analysis unit to accurately analyze video and sound data and learn how to convert it into electrical signals.

[0032] The generation unit can convert video data and audio data into electrical signals based on the data learned by the analysis unit. The generation unit can convert video and audio into electrical signals using, for example, a conversion algorithm. For example, the generation unit can convert video and audio into electrical signals using a deep learning model. The generation unit can also convert video and audio into electrical signals using a neural network model. Furthermore, the generation unit can combine multiple conversion algorithms to convert video and audio into electrical signals. For example, the generation unit can combine deep learning and a neural network to convert video and audio into electrical signals. This allows the generation unit to accurately convert video and audio into electrical signals.

[0033] The transmitting unit can send the electrical signals generated by the generating unit to the brain. For example, the transmitting unit can send the electrical signals generated by the generating unit to the brain. For example, the transmitting unit adjusts the transmission path and transmission timing to send the generated electrical signals to the brain. The transmitting unit can also adjust the transmission path and transmission strength to ensure the safety of the transmission. For example, the transmitting unit optimizes the transmission path and adjusts the transmission strength to safely send the generated electrical signals to the brain. In this way, the transmitting unit can send the generated electrical signals to the brain, enabling even people with visual or hearing impairments to see and hear.

[0034] During collection, the collection unit can analyze the user's past viewing history and listening history and select the optimal collection method. The collection unit can, for example, analyze the user's past viewing history and listening history and select the optimal collection method. For example, the collection unit can analyze the patterns of videos that the user has preferred to watch in the past and preferentially collect similar videos. The collection unit can also analyze the genres of music that the user has listened to in the past and preferentially collect sounds of the same genre. Furthermore, the collection unit can predict the videos and sounds that the user will prefer in a specific time period from the user's past viewing history and collect them according to that time period. This allows the collection unit to select the optimal collection method based on the user's past history, enabling more effective data collection.

[0035] The collection unit can perform filtering based on the user's current environment and situation during collection. The collection unit can perform filtering based on the user's current environment and situation, for example. For example, if the user is in a quiet environment, the collection unit can filter background sounds and collect clear audio. Also, if the user is in a dark place, the collection unit can adjust the brightness of the video to collect video that is easy to see. Furthermore, if the user is moving, the collection unit can filter vibrations and noise to collect stable video and audio. This allows the collection unit to perform filtering according to the user's environment and situation, enabling more appropriate data collection.

[0036] During collection, the collection unit can prioritize collecting highly relevant video and sound by taking into account the user's geographical location information. The collection unit can prioritize collecting highly relevant video and sound by taking into account the user's geographical location information, for example. For example, if the user is at a tourist spot, the collection unit can prioritize collecting famous places and sounds of that location. Furthermore, if the user is at home, the collection unit can prioritize collecting sounds and video from within the home. Furthermore, if the user is at an event venue, the collection unit can also prioritize collecting video and sound related to the event. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0037] At the time of collection, the collection unit can analyze the user's social media activities and collect related video and audio. The collection unit can, for example, analyze the user's social media activities and collect related video and audio. For example, the collection unit can collect related content based on video and audio shared by the user on social media. The collection unit can also analyze the content posted by accounts the user follows on social media and collect related video and audio. Furthermore, the collection unit can also collect related video and audio based on content that the user has "liked" on social media. This allows the collection unit to collect related data based on the user's social media activities, enabling more appropriate data collection.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video and sound. The analysis unit can adjust the level of detail of the analysis based on, for example, the importance of the video and sound. For example, the analysis unit can perform a detailed analysis of important video and sound and provide analysis results in detail. The analysis unit can also perform a simplified analysis of less important video and sound and provide only an overview. Furthermore, the analysis unit can dynamically adjust the accuracy of the analysis based on the importance of the video and sound. This allows the analysis unit to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the video and sound.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of video or sound. For example, the analysis unit can apply different analysis algorithms depending on the category of video or sound. For example, if the video is a natural landscape, the analysis unit can apply an algorithm that analyzes specific natural elements. Also, if the sound is music, the analysis unit can apply an algorithm that analyzes the rhythm and melody of the music. Furthermore, if the video is sports, the analysis unit can also apply an algorithm that analyzes the movements of players. In this way, the analysis unit can provide more appropriate analysis results by applying an analysis algorithm depending on the category of video or sound.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time when the video and audio were collected. The analysis unit can determine the analysis priority based on, for example, the time when the video and audio were collected. For example, the analysis unit can prioritize analyzing the most recent video and audio to provide real-time information. The analysis unit can also prioritize analyzing important video and audio from the past to provide historical information. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time when the video and audio were collected. This allows the analysis unit to provide more appropriate analysis results by determining the analysis priority based on the time when the video and audio were collected.

[0041] The analysis unit can adjust the order of analysis based on the relevance of video and sound during analysis. The analysis unit can adjust the order of analysis based on, for example, the relevance of video and sound. For example, the analysis unit can prioritize analyzing highly relevant video and sound and provide related information. The analysis unit can also analyze less relevant video and sound later. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of video and sound. In this way, the analysis unit can provide more appropriate analysis results by adjusting the order of analysis based on the relevance of video and sound.

[0042] The generation unit can adjust the level of detail of the electrical signal based on the characteristics of the video and sound during generation. The generation unit can adjust the level of detail of the electrical signal based on, for example, the characteristics of the video and sound. For example, the generation unit can generate a detailed electrical signal for high-resolution video. Furthermore, the generation unit can generate a simplified electrical signal for low-resolution video. Furthermore, the generation unit can dynamically adjust the level of detail of the electrical signal according to the characteristics of the sound. In this way, the generation unit can generate a more appropriate electrical signal by adjusting the level of detail of the electrical signal according to the characteristics of the video and sound.

[0043] The generation unit can apply different generation algorithms depending on the category of the video or sound during generation. For example, the generation unit can apply different generation algorithms depending on the category of the video or sound. For example, if the video is a natural landscape, the generation unit can generate an electrical signal that emphasizes a specific natural element. Furthermore, if the sound is music, the generation unit can generate an electrical signal that emphasizes the rhythm or melody of the music. Furthermore, if the video is sports, the generation unit can also generate an electrical signal that emphasizes the movements of the players. In this way, the generation unit can generate more appropriate electrical signals by applying a generation algorithm depending on the category of the video or sound.

[0044] The generation unit can adjust the generation order of the electrical signals based on the time when the video and sound were collected during generation. The generation unit can adjust the generation order of the electrical signals based on, for example, the time when the video and sound were collected. For example, the generation unit can generate electrical signals with priority for the most recent video and sound. The generation unit can also generate electrical signals with priority for important past video and sound. Furthermore, the generation unit can dynamically adjust the generation order of the electrical signals based on the time when the video and sound were collected. In this way, the generation unit can generate more appropriate electrical signals by adjusting the generation order of the electrical signals based on the time when the video and sound were collected.

[0045] The generation unit can adjust the generation method of the electrical signal based on the relevance of the video and sound during generation. The generation unit can adjust the generation method of the electrical signal based on, for example, the relevance of the video and sound. For example, the generation unit can generate a detailed electrical signal for a highly relevant video or sound. Furthermore, the generation unit can generate a simplified electrical signal for a less relevant video or sound. Furthermore, the generation unit can dynamically adjust the generation method of the electrical signal based on the relevance of the video or sound. In this way, the generation unit can generate a more appropriate electrical signal by adjusting the generation method of the electrical signal based on the relevance of the video or sound.

[0046] At the time of transmission, the transmitting unit can select the optimal transmission method by referring to the user's past reaction data. The transmitting unit can, for example, select the optimal transmission method by referring to the user's past reaction data. For example, the transmitting unit preferentially selects a transmission method of an electrical signal to which the user responded favorably in the past. The transmitting unit can also avoid a transmission method of an electrical signal to which the user responded unpleasantly in the past. Furthermore, the transmitting unit can analyze the user's past reaction data and dynamically select the optimal transmission method. In this way, the transmitting unit can transmit a more appropriate electrical signal by selecting the optimal transmission method based on the user's past reaction data.

[0047] The transmitter can adjust the timing of transmission based on the user's current state during transmission. The transmitter can adjust the timing of transmission based on the user's current state, for example. For example, if the user is relaxed, the transmitter can adjust the timing of transmission of the electrical signal slowly. Also, if the user is excited, the transmitter can speed up the timing of transmission of the electrical signal. Furthermore, if the user is feeling stressed, the transmitter can minimize the timing of transmission of the electrical signal. In this way, the transmitter can transmit more appropriate electrical signals by adjusting the timing of transmission according to the user's current state.

[0048] The transmitting unit can select the optimal transmission method in consideration of the user's geographical location information at the time of transmission. The transmitting unit can select the optimal transmission method in consideration of, for example, the user's geographical location information. For example, the transmitting unit can transmit a gentle electrical signal when the user is in a quiet environment. Also, the transmitting unit can transmit a strong electrical signal when the user is in a noisy environment. Furthermore, the transmitting unit can dynamically select the optimal transmission method based on the user's geographical location information. This allows the transmitting unit to select the optimal transmission method by considering the user's geographical location information.

[0049] At the time of transmission, the transmitting unit can adjust the transmission method by analyzing the user's social media activity. For example, the transmitting unit can adjust the transmission method by analyzing the user's social media activity. For example, the transmitting unit can transmit a related electrical signal based on content shared by the user on social media. The transmitting unit can also analyze the activity of accounts the user follows on social media and transmit a related electrical signal. Furthermore, the transmitting unit can also transmit a related electrical signal based on content that the user "likes" on social media. In this way, the transmitting unit can transmit a more appropriate electrical signal by adjusting the transmission method based on the user's social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The visual and auditory assistance system may further include an action history recording unit that records the user's action history. The action history recording unit may record, for example, the location and activities the user performed. This allows the system to learn the user's action patterns and provide more appropriate visual and auditory assistance. For example, in places that the user frequently visits, the system may prioritize providing information related to that location. Also, when the user performs a specific activity, the system may provide visual and auditory assistance related to that activity. Furthermore, by analyzing the user's action history, the system may understand the user's preferences and interests and provide more personalized assistance.

[0052] The visual and auditory assistance system may further include a learning history recording unit that records the user's learning history. The learning history recording unit may record, for example, what images and sounds the user has studied. This allows the system to understand the user's learning patterns and provide more effective visual and auditory assistance. For example, if the user repeatedly studies a particular image or sound, the system may provide that image or sound preferentially. Furthermore, when the user studies new images or sounds, assistance to support that learning may be provided. Furthermore, by analyzing the user's learning history, the system may understand the user's learning progress and provide new learning content at the appropriate time.

[0053] The visual and auditory assistance system may further include a health data analysis unit that analyzes the user's health data. The health data analysis unit may analyze data such as the user's heart rate, blood pressure, and sleep patterns. This allows the system to understand the user's health condition and provide appropriate visual and auditory assistance. For example, if the user's heart rate is stable, the system may provide normal visual and auditory assistance. Alternatively, if the user's blood pressure is high, the system may provide relaxing images and sounds. Furthermore, by analyzing the user's sleep patterns, the system may provide visual and auditory assistance at a time when the user is most relaxed.

[0054] The visual and auditory assistance system may further include a lifestyle recorder that records the user's lifestyle. The lifestyle recorder may record data such as the user's diet, exercise, and sleep. This allows the system to understand the user's lifestyle and provide appropriate visual and auditory assistance. For example, if the user eats a healthy diet, the system may provide normal visual and auditory assistance. Also, if the user exercises, the system may provide images and sounds appropriate for exercise. Furthermore, by recording the user's sleep data, the system may provide visual and auditory assistance at a time when the user is most relaxed.

[0055] The visual and auditory assistance system may further include an interest recording unit that records the user's hobbies and interests. The interest recording unit may, for example, record the user's hobbies and interests. This allows the system to provide visual and auditory assistance based on the user's hobbies and interests. For example, if the user is interested in music, the system may provide music-related images and sounds. Also, if the user is interested in sports, the system may provide sports-related images and sounds. Furthermore, by analyzing the user's hobbies and interests, the system may help the user discover new hobbies and interests. This allows the system to provide a more fulfilling experience by providing visual and auditory assistance tailored to the user's hobbies and interests.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects video and audio data. The collection unit can collect video and audio data using sensors such as a camera and a microphone. For example, the collection unit can collect video and audio data using a camera and a microphone, respectively. The collection unit can also collect video and audio data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit and learns what kind of electrical signal it will produce. The analysis unit can use AI to analyze video and audio data and learn what kind of electrical signal it will produce. For example, it can use AI technologies such as deep learning and neural networks to analyze video and audio data and learn what kind of electrical signal it will produce. Step 3: The generator converts the video and audio data into electrical signals based on the data learned by the analyzer. The generator can convert the video and audio data into electrical signals based on the data learned by the analyzer. For example, it uses a conversion algorithm to convert the video and audio data into electrical signals. Step 4: The transmitter sends the electrical signal generated by the generator to the brain. The transmitter can send the electrical signal generated by the generator to the brain. For example, it adjusts the transmission path and transmission timing to send the generated electrical signal to the brain.

[0058] (Example 2) The visual and auditory assistance system according to an embodiment of the present invention is a technology that enables visually or hearing-impaired individuals to see and hear. This visual and auditory assistance system collects and analyzes video and audio data, converts it into electrical signals, and transmits them to the brain, thereby enabling visual and hearing-impaired individuals to see and hear. Specifically, a collection unit is first provided to collect video and audio data. This collection unit collects video and audio using sensors such as cameras and microphones. Next, an analysis unit is provided to analyze the collected data. This analysis unit uses AI to analyze the video and audio data and learns what kind of electrical signals are generated. A generation unit is provided to generate electrical signals based on the analysis results. This generation unit converts video and audio into electrical signals based on the data learned by the analysis unit. Finally, a transmission unit is provided to send the generated electrical signals to the brain. This transmission unit transmits the electrical signals generated by the generation unit to the brain, enabling visual and hearing-impaired individuals to see and hear. For example, a visual and auditory assistance system may collect video using a camera and sound using a microphone. The collected video and audio data is analyzed using AI, which learns what kind of electrical signals it will produce. Based on the learned data, the video and audio are converted into electrical signals and sent to the brain, allowing even those with visual or hearing impairments to see and hear. In this way, the visual and hearing assistance system enables those with visual or hearing impairments to see and hear.

[0059] The visual and auditory assistance system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a transmission unit. The collection unit collects video and audio data. The collection unit can collect video and audio data using sensors such as a camera and a microphone. For example, the collection unit collects video using a camera and audio using a microphone. The collection unit can also collect video and audio data in real time. The analysis unit analyzes the data collected by the collection unit and learns what kind of electrical signal the data will be. The analysis unit can analyze video and audio data using AI, for example, and learn what kind of electrical signal the data will be. For example, the analysis unit can analyze video and audio data using AI technology such as deep learning or neural networks and learn what kind of electrical signal the data will be. The generation unit converts video and audio data into electrical signals based on the data learned by the analysis unit. For example, the generation unit can convert video data and audio data into electrical signals based on the data learned by the analysis unit. For example, the generation unit converts video and audio data into electrical signals using a conversion algorithm. The transmitting unit sends the electrical signal generated by the generating unit to the brain. The transmitting unit can, for example, send the electrical signal generated by the generating unit to the brain. For example, the transmitting unit adjusts the transmission path and transmission timing to send the generated electrical signal to the brain. In this way, the visual and auditory assistance system according to the embodiment enables even a person with a visual or hearing impairment to see and hear.

[0060] The collection unit can collect video and sound using a camera or microphone sensor. The collection unit can, for example, collect video using a camera. For example, the collection unit collects video using a high-resolution camera. The collection unit can also collect sound using a microphone. For example, the collection unit collects sound using a high-sensitivity microphone. Furthermore, the collection unit can combine a camera and a microphone to simultaneously collect video and sound. For example, the collection unit combines a camera and a microphone to simultaneously collect video and sound and acquire data in real time. This allows the collection unit to efficiently collect video and sound data.

[0061] The analysis unit can use AI to analyze video and sound data and learn how to generate electrical signals. The analysis unit can, for example, use deep learning to analyze video and sound data. For example, the analysis unit can use a deep learning algorithm to analyze video and sound data and learn what kind of electrical signals are generated. The analysis unit can also analyze video and sound data using a neural network. For example, the analysis unit can use a neural network model to analyze video and sound data and learn what kind of electrical signals are generated. Furthermore, the analysis unit can combine multiple AI technologies to analyze video and sound data. For example, the analysis unit can combine deep learning and a neural network to analyze video and sound data and learn what kind of electrical signals are generated. This enables the analysis unit to accurately analyze video and sound data and learn how to convert it into electrical signals.

[0062] The generation unit can convert video data and audio data into electrical signals based on the data learned by the analysis unit. The generation unit can convert video and audio into electrical signals using, for example, a conversion algorithm. For example, the generation unit can convert video and audio into electrical signals using a deep learning model. The generation unit can also convert video and audio into electrical signals using a neural network model. Furthermore, the generation unit can combine multiple conversion algorithms to convert video and audio into electrical signals. For example, the generation unit can combine deep learning and a neural network to convert video and audio into electrical signals. This allows the generation unit to accurately convert video and audio into electrical signals.

[0063] The transmitting unit can send the electrical signals generated by the generating unit to the brain. For example, the transmitting unit can send the electrical signals generated by the generating unit to the brain. For example, the transmitting unit adjusts the transmission path and transmission timing to send the generated electrical signals to the brain. The transmitting unit can also adjust the transmission path and transmission strength to ensure the safety of the transmission. For example, the transmitting unit optimizes the transmission path and adjusts the transmission strength to safely send the generated electrical signals to the brain. In this way, the transmitting unit can send the generated electrical signals to the brain, enabling even people with visual or hearing impairments to see and hear.

[0064] The collection unit can estimate the user's emotions and adjust the timing of collecting images and sounds based on the estimated user emotions. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting images and sounds based on the estimated user emotions. For example, if the user is relaxed, the collection unit adjusts the timing of collecting images and sounds to a leisurely pace. Also, if the user is excited, the collection unit can speed up the timing of collecting images and sounds to collect more data. Furthermore, if the user is feeling stressed, the collection unit can minimize the timing of collecting images and sounds to reduce the burden on the user. This allows the collection unit to adjust the collection timing according to the user's emotions, thereby enabling more appropriate data collection.

[0065] During collection, the collection unit can analyze the user's past viewing history and listening history and select the optimal collection method. The collection unit can, for example, analyze the user's past viewing history and listening history and select the optimal collection method. For example, the collection unit can analyze the patterns of videos that the user has preferred to watch in the past and preferentially collect similar videos. The collection unit can also analyze the genres of music that the user has listened to in the past and preferentially collect sounds of the same genre. Furthermore, the collection unit can predict the videos and sounds that the user will prefer in a specific time period from the user's past viewing history and collect them according to that time period. This allows the collection unit to select the optimal collection method based on the user's past history, enabling more effective data collection.

[0066] The collection unit can perform filtering based on the user's current environment and situation during collection. The collection unit can perform filtering based on the user's current environment and situation, for example. For example, if the user is in a quiet environment, the collection unit can filter background sounds and collect clear audio. Also, if the user is in a dark place, the collection unit can adjust the brightness of the video to collect video that is easy to see. Furthermore, if the user is moving, the collection unit can filter vibrations and noise to collect stable video and audio. This allows the collection unit to perform filtering according to the user's environment and situation, enabling more appropriate data collection.

[0067] The collection unit can estimate the user's emotions and determine the priority of the images and sounds to be collected based on the estimated user's emotions. The collection unit can, for example, estimate the user's emotions and determine the priority of the images and sounds to be collected based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can prioritize collecting natural scenery and calm sounds. Also, if the user is excited, the collection unit can prioritize collecting action scenes and intense music. Furthermore, if the user is sad, the collection unit can prioritize collecting comforting images and sounds. In this way, the collection unit can prioritize the images and sounds to be collected based on the user's emotions, enabling more appropriate data collection.

[0068] During collection, the collection unit can prioritize collecting highly relevant video and sound by taking into account the user's geographical location information. The collection unit can prioritize collecting highly relevant video and sound by taking into account the user's geographical location information, for example. For example, if the user is at a tourist spot, the collection unit can prioritize collecting famous places and sounds of that location. Furthermore, if the user is at home, the collection unit can prioritize collecting sounds and video from within the home. Furthermore, if the user is at an event venue, the collection unit can also prioritize collecting video and sound related to the event. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0069] At the time of collection, the collection unit can analyze the user's social media activities and collect related video and audio. The collection unit can, for example, analyze the user's social media activities and collect related video and audio. For example, the collection unit can collect related content based on video and audio shared by the user on social media. The collection unit can also analyze the content posted by accounts the user follows on social media and collect related video and audio. Furthermore, the collection unit can also collect related video and audio based on content that the user has "liked" on social media. This allows the collection unit to collect related data based on the user's social media activities, enabling more appropriate data collection.

[0070] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit can, for example, estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can display the analysis results as a visually calm graph or chart. Furthermore, if the user is excited, the analysis unit can display the analysis results as dynamic animations. Furthermore, if the user is feeling stressed, the analysis unit can display the analysis results as simple and intuitive icons. In this way, the analysis unit can provide more appropriate analysis results by adjusting the way the analysis is presented according to the user's emotions.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video and sound. The analysis unit can adjust the level of detail of the analysis based on, for example, the importance of the video and sound. For example, the analysis unit can perform a detailed analysis of important video and sound and provide analysis results in detail. The analysis unit can also perform a simplified analysis of less important video and sound and provide only an overview. Furthermore, the analysis unit can dynamically adjust the accuracy of the analysis based on the importance of the video and sound. This allows the analysis unit to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the video and sound.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the category of video or sound. For example, the analysis unit can apply different analysis algorithms depending on the category of video or sound. For example, if the video is a natural landscape, the analysis unit can apply an algorithm that analyzes specific natural elements. Also, if the sound is music, the analysis unit can apply an algorithm that analyzes the rhythm and melody of the music. Furthermore, if the video is sports, the analysis unit can also apply an algorithm that analyzes the movements of players. In this way, the analysis unit can provide more appropriate analysis results by applying an analysis algorithm depending on the category of video or sound.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit can, for example, estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short analysis result with only the main points. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the length of the analysis according to the user's emotions.

[0074] During analysis, the analysis unit can determine the analysis priority based on the time when the video and audio were collected. The analysis unit can determine the analysis priority based on, for example, the time when the video and audio were collected. For example, the analysis unit can prioritize analyzing the most recent video and audio to provide real-time information. The analysis unit can also prioritize analyzing important video and audio from the past to provide historical information. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time when the video and audio were collected. This allows the analysis unit to provide more appropriate analysis results by determining the analysis priority based on the time when the video and audio were collected.

[0075] The analysis unit can adjust the order of analysis based on the relevance of video and sound during analysis. The analysis unit can adjust the order of analysis based on, for example, the relevance of video and sound. For example, the analysis unit can prioritize analyzing highly relevant video and sound and provide related information. The analysis unit can also analyze less relevant video and sound later. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of video and sound. In this way, the analysis unit can provide more appropriate analysis results by adjusting the order of analysis based on the relevance of video and sound.

[0076] The generation unit can estimate the user's emotion and adjust the method for generating the electrical signal based on the estimated user's emotion. The generation unit can, for example, estimate the user's emotion and adjust the method for generating the electrical signal based on the estimated user's emotion. For example, the generation unit can generate a gentle electrical signal when the user is relaxed. Also, the generation unit can generate a strong electrical signal when the user is excited. Furthermore, the generation unit can generate an electrical signal that reduces stress when the user is feeling stressed. In this way, the generation unit can generate a more appropriate electrical signal by adjusting the method for generating the electrical signal according to the user's emotion.

[0077] The generation unit can adjust the level of detail of the electrical signal based on the characteristics of the video and sound during generation. The generation unit can adjust the level of detail of the electrical signal based on, for example, the characteristics of the video and sound. For example, the generation unit can generate a detailed electrical signal for high-resolution video. Furthermore, the generation unit can generate a simplified electrical signal for low-resolution video. Furthermore, the generation unit can dynamically adjust the level of detail of the electrical signal according to the characteristics of the sound. In this way, the generation unit can generate a more appropriate electrical signal by adjusting the level of detail of the electrical signal according to the characteristics of the video and sound.

[0078] The generation unit can apply different generation algorithms depending on the category of the video or sound during generation. For example, the generation unit can apply different generation algorithms depending on the category of the video or sound. For example, if the video is a natural landscape, the generation unit can generate an electrical signal that emphasizes a specific natural element. Furthermore, if the sound is music, the generation unit can generate an electrical signal that emphasizes the rhythm or melody of the music. Furthermore, if the video is sports, the generation unit can also generate an electrical signal that emphasizes the movements of the players. In this way, the generation unit can generate more appropriate electrical signals by applying a generation algorithm depending on the category of the video or sound.

[0079] The generation unit can estimate the user's emotion and determine the priority of the electrical signals to be generated based on the estimated user's emotion. The generation unit can, for example, estimate the user's emotion and determine the priority of the electrical signals to be generated based on the estimated user's emotion. For example, the generation unit can preferentially generate a gentle electrical signal when the user is relaxed. Furthermore, the generation unit can preferentially generate a strong electrical signal when the user is excited. Furthermore, the generation unit can preferentially generate an electrical signal that reduces stress when the user is feeling stressed. In this way, the generation unit can generate more appropriate electrical signals by determining the priority of the electrical signals according to the user's emotion.

[0080] The generation unit can adjust the generation order of the electrical signals based on the time when the video and sound were collected during generation. The generation unit can adjust the generation order of the electrical signals based on, for example, the time when the video and sound were collected. For example, the generation unit can generate electrical signals with priority for the most recent video and sound. The generation unit can also generate electrical signals with priority for important past video and sound. Furthermore, the generation unit can dynamically adjust the generation order of the electrical signals based on the time when the video and sound were collected. In this way, the generation unit can generate more appropriate electrical signals by adjusting the generation order of the electrical signals based on the time when the video and sound were collected.

[0081] The generation unit can adjust the generation method of the electrical signal based on the relevance of the video and sound during generation. The generation unit can adjust the generation method of the electrical signal based on, for example, the relevance of the video and sound. For example, the generation unit can generate a detailed electrical signal for a highly relevant video or sound. Furthermore, the generation unit can generate a simplified electrical signal for a less relevant video or sound. Furthermore, the generation unit can dynamically adjust the generation method of the electrical signal based on the relevance of the video or sound. In this way, the generation unit can generate a more appropriate electrical signal by adjusting the generation method of the electrical signal based on the relevance of the video or sound.

[0082] The transmitting unit can estimate the user's emotion and adjust the method of transmitting the electrical signal based on the estimated user's emotion. The transmitting unit can, for example, estimate the user's emotion and adjust the method of transmitting the electrical signal based on the estimated user's emotion. For example, if the user is relaxed, the transmitting unit can slowly transmit a gentle electrical signal. Also, if the user is excited, the transmitting unit can quickly transmit a strong electrical signal. Furthermore, if the user is feeling stressed, the transmitting unit can slowly transmit an electrical signal that reduces stress. In this way, the transmitting unit can transmit a more appropriate electrical signal by adjusting the method of transmitting the electrical signal according to the user's emotion.

[0083] At the time of transmission, the transmitting unit can select the optimal transmission method by referring to the user's past reaction data. The transmitting unit can, for example, select the optimal transmission method by referring to the user's past reaction data. For example, the transmitting unit preferentially selects a transmission method of an electrical signal to which the user responded favorably in the past. The transmitting unit can also avoid a transmission method of an electrical signal to which the user responded unpleasantly in the past. Furthermore, the transmitting unit can analyze the user's past reaction data and dynamically select the optimal transmission method. In this way, the transmitting unit can transmit a more appropriate electrical signal by selecting the optimal transmission method based on the user's past reaction data.

[0084] The transmitter can adjust the timing of transmission based on the user's current state during transmission. The transmitter can adjust the timing of transmission based on the user's current state, for example. For example, if the user is relaxed, the transmitter can adjust the timing of transmission of the electrical signal slowly. Also, if the user is excited, the transmitter can speed up the timing of transmission of the electrical signal. Furthermore, if the user is feeling stressed, the transmitter can minimize the timing of transmission of the electrical signal. In this way, the transmitter can transmit more appropriate electrical signals by adjusting the timing of transmission according to the user's current state.

[0085] The transmitting unit can estimate the user's emotion and determine the transmission priority of the electrical signals based on the estimated user's emotion. The transmitting unit can, for example, estimate the user's emotion and determine the transmission priority of the electrical signals based on the estimated user's emotion. For example, the transmitting unit can preferentially transmit a gentle electrical signal when the user is relaxed. Also, the transmitting unit can preferentially transmit a strong electrical signal when the user is excited. Furthermore, the transmitting unit can also preferentially transmit an electrical signal that reduces stress when the user is feeling stressed. In this way, the transmitting unit can transmit more appropriate electrical signals by determining the transmission priority of the electrical signals according to the user's emotion.

[0086] The transmitting unit can select the optimal transmission method in consideration of the user's geographical location information at the time of transmission. The transmitting unit can select the optimal transmission method in consideration of, for example, the user's geographical location information. For example, the transmitting unit can transmit a gentle electrical signal when the user is in a quiet environment. Also, the transmitting unit can transmit a strong electrical signal when the user is in a noisy environment. Furthermore, the transmitting unit can dynamically select the optimal transmission method based on the user's geographical location information. This allows the transmitting unit to select the optimal transmission method by considering the user's geographical location information.

[0087] At the time of transmission, the transmitting unit can adjust the transmission method by analyzing the user's social media activity. For example, the transmitting unit can adjust the transmission method by analyzing the user's social media activity. For example, the transmitting unit can transmit a related electrical signal based on content shared by the user on social media. The transmitting unit can also analyze the activity of accounts the user follows on social media and transmit a related electrical signal. Furthermore, the transmitting unit can also transmit a related electrical signal based on content that the user "likes" on social media. In this way, the transmitting unit can transmit a more appropriate electrical signal by adjusting the transmission method based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and transmission unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects video and sound using the camera 42 and microphone 38B of the smart device 14. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and analyzes the video and sound data using AI to learn what kind of electrical signal will be generated. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and converts the video and sound into electrical signals based on the data learned by the analysis unit. The transmission unit, for example, is realized by the control unit 46A of the smart device 14, and sends the electrical signals generated by the generation unit to the brain, enabling people with visual or hearing impairments to see and hear. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and transmission unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects video and sound using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and analyzes video and sound data using AI to learn what kind of electrical signal will be generated. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and converts video and sound into electrical signals based on the data learned by the analysis unit. The transmission unit, for example, is realized by the control unit 46A of the smart glasses 214, and sends the electrical signals generated by the generation unit to the brain, enabling visually or hearing impaired people to see and hear. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and transmission unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects video and sound using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and analyzes the video and sound data using AI to learn what kind of electrical signal it will produce. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and converts the video and sound into electrical signals based on the data learned by the analysis unit. The transmission unit, for example, is realized by the control unit 46A of the headset-type terminal 314, and sends the electrical signals generated by the generation unit to the brain, enabling people with visual or hearing impairments to see and hear. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and transmission unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects video and sound using the camera 42 and microphone 238 of the robot 414. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the video and sound data using AI and learns what kind of electrical signal it will produce. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, converts the video and sound into electrical signals based on the data learned by the analysis unit. The transmission unit, realized, for example, by the control unit 46A of the robot 414, sends the electrical signals generated by the generation unit to the brain, enabling even visually or hearing impaired people to see and hear.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The visual and auditory assistance system may further include a biometric information acquisition unit that acquires the user's biometric information. The biometric information acquisition unit may acquire biometric information such as heart rate, body temperature, and electrical resistance of the skin. This allows the system to monitor the user's health and provide visual and auditory assistance at the appropriate time. For example, if the user's heart rate suddenly increases, the system may temporarily suspend visual and auditory assistance to ensure the user's safety. If the user's body temperature is abnormally high, the system may issue a warning and prompt the user to take appropriate action. Furthermore, if the electrical resistance of the skin changes, the system may estimate the user's stress level and provide relaxing images and sounds.

[0090] The visual and auditory assistance system may further include an action history recording unit that records the user's action history. The action history recording unit may record, for example, the location and activities the user performed. This allows the system to learn the user's action patterns and provide more appropriate visual and auditory assistance. For example, in places that the user frequently visits, the system may prioritize providing information related to that location. Also, when the user performs a specific activity, the system may provide visual and auditory assistance related to that activity. Furthermore, by analyzing the user's action history, the system may understand the user's preferences and interests and provide more personalized assistance.

[0091] The visual and auditory assistance system may further include an emotion adjustment unit that estimates the user's emotion and adjusts the visual and auditory assistance content based on the estimated emotion. For example, the emotion adjustment unit may provide calming images and sounds when the user is relaxed, or stimulating images and sounds when the user is excited. Furthermore, it may provide relaxing images and sounds when the user is stressed. This allows the system to provide visual and auditory assistance according to the user's emotion, thereby providing a more comfortable experience. For example, it may provide natural scenery and calming music when the user is relaxed, and action scenes and intense music when the user is excited.

[0092] The visual and auditory assistance system may further include a learning history recording unit that records the user's learning history. The learning history recording unit may record, for example, what images and sounds the user has studied. This allows the system to understand the user's learning patterns and provide more effective visual and auditory assistance. For example, if the user repeatedly studies a particular image or sound, the system may provide that image or sound preferentially. Furthermore, when the user studies new images or sounds, assistance to support that learning may be provided. Furthermore, by analyzing the user's learning history, the system may understand the user's learning progress and provide new learning content at the appropriate time.

[0093] The visual and auditory assistance system may further include an emotion intensity adjustment unit that estimates the user's emotion and adjusts the intensity of the visual and auditory assistance based on the estimated emotion. For example, the emotion intensity adjustment unit may set the visual and auditory assistance intensity low when the user is relaxed. Alternatively, the emotion intensity adjustment unit may set the visual and auditory assistance intensity high when the user is excited. Furthermore, the emotion intensity adjustment unit may set the visual and auditory assistance intensity to a medium level when the user is stressed. This allows the system to provide a more comfortable experience by providing visual and auditory assistance intensity according to the user's emotion. For example, when the user is relaxed, calm images and sounds can be provided at a low intensity, and when the user is excited, stimulating images and sounds can be provided at a high intensity.

[0094] The visual and auditory assistance system may further include a health data analysis unit that analyzes the user's health data. The health data analysis unit may analyze data such as the user's heart rate, blood pressure, and sleep patterns. This allows the system to understand the user's health condition and provide appropriate visual and auditory assistance. For example, if the user's heart rate is stable, the system may provide normal visual and auditory assistance. Alternatively, if the user's blood pressure is high, the system may provide relaxing images and sounds. Furthermore, by analyzing the user's sleep patterns, the system may provide visual and auditory assistance at a time when the user is most relaxed.

[0095] The visual and auditory assistance system may further include an emotion customization unit that estimates the user's emotion and customizes the visual and auditory assistance content based on the estimated emotion. For example, the emotion customization unit may provide natural scenery and calming music when the user is relaxed. Alternatively, it may provide action scenes and intense music when the user is excited. Furthermore, it may provide relaxing images and sounds when the user is stressed. This allows the system to provide visual and auditory assistance according to the user's emotion, thereby providing a more comfortable experience. For example, it may provide calming images and sounds when the user is relaxed, and stimulating images and sounds when the user is excited.

[0096] The visual and auditory assistance system may further include a lifestyle recorder that records the user's lifestyle. The lifestyle recorder may record data such as the user's diet, exercise, and sleep. This allows the system to understand the user's lifestyle and provide appropriate visual and auditory assistance. For example, if the user eats a healthy diet, the system may provide normal visual and auditory assistance. Also, if the user exercises, the system may provide images and sounds appropriate for exercise. Furthermore, by recording the user's sleep data, the system may provide visual and auditory assistance at a time when the user is most relaxed.

[0097] The visual and auditory assistance system may further include an emotion frequency adjustment unit that estimates the user's emotion and adjusts the frequency of visual and auditory assistance based on the estimated emotion. For example, the emotion frequency adjustment unit may set the frequency of visual and auditory assistance low when the user is relaxed. Alternatively, the emotion frequency adjustment unit may set the frequency of visual and auditory assistance high when the user is excited. Furthermore, the emotion frequency adjustment unit may set the frequency of visual and auditory assistance to a medium level when the user is stressed. This allows the system to provide a more comfortable experience by adjusting the frequency of visual and auditory assistance according to the user's emotion. For example, when the user is relaxed, calm images and sounds may be provided at a low frequency, and when the user is excited, stimulating images and sounds may be provided at a high frequency.

[0098] The visual and auditory assistance system may further include an interest recording unit that records the user's hobbies and interests. The interest recording unit may, for example, record the user's hobbies and interests. This allows the system to provide visual and auditory assistance based on the user's hobbies and interests. For example, if the user is interested in music, the system may provide music-related images and sounds. Also, if the user is interested in sports, the system may provide sports-related images and sounds. Furthermore, by analyzing the user's hobbies and interests, the system may help the user discover new hobbies and interests. This allows the system to provide a more fulfilling experience by providing visual and auditory assistance tailored to the user's hobbies and interests.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects video and audio data. The collection unit can collect video and audio data using sensors such as a camera and a microphone. For example, the collection unit can collect video and audio data using a camera and a microphone, respectively. The collection unit can also collect video and audio data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit and learns what kind of electrical signal it will produce. The analysis unit can use AI to analyze video and audio data and learn what kind of electrical signal it will produce. For example, it can use AI technologies such as deep learning and neural networks to analyze video and audio data and learn what kind of electrical signal it will produce. Step 3: The generator converts the video and audio data into electrical signals based on the data learned by the analyzer. The generator can convert the video and audio data into electrical signals based on the data learned by the analyzer. For example, it uses a conversion algorithm to convert the video and audio data into electrical signals. Step 4: The transmitter sends the electrical signal generated by the generator to the brain. The transmitter can send the electrical signal generated by the generator to the brain. For example, it adjusts the transmission path and transmission timing to send the generated electrical signal to the brain.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] 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.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] 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.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects video and audio data; an analysis unit that analyzes the data collected by the collection unit and learns how to generate an electrical signal; a generation unit that converts video data and audio data into electrical signals based on the data learned by the analysis unit; a transmitting unit that transmits the electrical signal generated by the generating unit to the brain. A system characterized by:

2. The collecting unit Collecting images and sounds using camera or microphone sensors The system of claim 1 .

3. The analysis unit Using AI to analyze video and audio data and learn how to generate electrical signals The system of claim 1 .

4. The generation unit Based on the data learned by the analysis unit, video data and audio data are converted into electrical signals. The system of claim 1 .

5. The transmission unit The electrical signal generated by the generator is sent to the brain. The system of claim 1 .

6. The collecting unit Estimate the user's emotions and adjust the timing of video and audio collection based on the estimated user emotions. The system of claim 1 .

7. The collecting unit When collecting data, analyze the user's past viewing and listening history and select the optimal collection method. The system of claim 1 .

8. The collecting unit At the time of collection, filtering is performed based on the user's current environment and situation. The system of claim 1 .

Images