System

Abstract

An object of a system according to an embodiment is to enable a visually impaired person to grasp a surrounding situation by voice.SOLUTION: A system includes a moving image acquisition unit, an analysis unit, a voice conversion unit, and an information providing unit. The moving image acquisition unit acquires a moving image from a smartphone camera. The analysis unit analyzes the moving image acquired by the moving image acquisition unit. The voice conversion unit converts the result analyzed by the analysis unit into voice. The information providing unit provides the voice converted by the voice conversion unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology provides limited means for visually impaired people to understand their surroundings, leaving room for improvement.

[0005] The system according to the embodiment aims to enable visually impaired people to understand the situation around them through audio. [Means for solving the problem]

[0006] The system according to the embodiment includes a video acquisition unit, an analysis unit, a voice conversion unit, and an information provision unit. The video acquisition unit acquires video from a smartphone camera. The analysis unit analyzes the video acquired by the video acquisition unit. The voice conversion unit converts the results of the analysis by the analysis unit into voice. The information provision unit provides the user with the voice converted by the voice conversion unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a visually impaired person to understand the situation around them through audio. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 system according to the embodiment of the present invention is a system for visually impaired people to understand their surroundings. This system uses a generation AI to analyze video images acquired from a smartphone camera or the like, and converts the analysis results into audio to communicate to the user. This allows the system to enable visually impaired people to understand their surroundings.

[0029] The system according to the embodiment includes a video acquisition unit, an analysis unit, a voice conversion unit, and an information provision unit. The video acquisition unit acquires video from a smartphone camera. For example, if a visually impaired person is walking and holding a smartphone, the smartphone's camera captures video of the surroundings in real time. The analysis unit analyzes the video acquired by the video acquisition unit. For example, a generation AI recognizes objects, people, obstacles, etc. captured by the camera and analyzes their positions and movements. The generation AI performs analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The voice conversion unit converts the results of the analysis by the analysis unit into voice. For example, information necessary for the visually impaired person is provided by voice, such as "There is a pedestrian ahead," "There is a bicycle on the right," or "There is a step on the left." The information provision unit provides the user with the voice converted by the voice conversion unit. For example, the information is transmitted to the user through a speaker on a smartphone or a dedicated device. This allows the system according to the embodiment to enable the visually impaired person to understand their surroundings.

[0030] The video image acquisition unit can use a small camera attached to the glasses of the visually impaired person. For example, the video image acquisition unit attaches a small camera to the glasses of the visually impaired person and uses the camera to acquire video images of the surroundings. For example, a camera can be built into the frame of the glasses and capture video images aligned with the line of sight of the visually impaired person. This allows the visually impaired person to acquire video images without using their hands.

[0031] The video image acquisition unit can acquire video images of the surroundings of the visually impaired person using a drone. The video image acquisition unit, for example, flies the drone around the visually impaired person to acquire wide-ranging video images. For example, the drone flies in front of or to the side of the visually impaired person to capture the surrounding situation. This allows the visually impaired person to grasp a wide range of the surroundings.

[0032] The video image acquisition unit can simultaneously acquire surrounding audio data in addition to acquiring video images. For example, the video image acquisition unit can simultaneously collect surrounding audio data and send it to the generation AI. For example, a microphone can be built into the camera to record surrounding audio in real time. This allows visually impaired people to understand surrounding audio information.

[0033] The video image acquisition unit can attach a camera to a cane held by a visually impaired person and acquire video images in accordance with the movement of the cane. The video image acquisition unit, for example, attaches a small camera to the cane of the visually impaired person and acquires video images using the camera. For example, a camera is installed at the tip of the cane and captures video of the surroundings in accordance with the movement of the cane. This allows the visually impaired person to acquire video images in accordance with the movement of the cane.

[0034] When analyzing video images, the generative AI learns from past analysis results and can perform highly accurate analysis. For example, the generative AI learns from past analysis results and improves the accuracy of video image analysis. For example, it improves the accuracy of object recognition based on past data. In this way, the generative AI learns from past analysis results and improves the accuracy of analysis.

[0035] In addition to analyzing video images, the generative AI can also analyze environmental data such as the surrounding temperature or humidity and provide it to visually impaired people. For example, in addition to analyzing video images, the generative AI can collect and analyze environmental data such as the surrounding temperature and humidity. For example, it can acquire environmental data using a temperature sensor or humidity sensor. This allows visually impaired people to understand the surrounding environmental data.

[0036] Generative AI can display the results of video analysis in real time on a smartphone app for visually impaired users, providing information in conjunction with audio. Generative AI can, for example, build a system that displays the results of video analysis in real time on a smartphone app for visually impaired users, providing information in conjunction with audio. For example, the analysis results can be displayed as text or icons. This allows visually impaired users to understand the situation using both audio and visual information.

[0037] The generative AI can share the analysis results with the family or caregiver of the visually impaired person, thereby strengthening support. For example, the generative AI can build a system that shares the analysis results with the family and caregiver of the visually impaired person. For example, the analysis results can be notified to the smartphone of the family or caregiver. This allows the family and caregiver of the visually impaired person to share the analysis results and strengthen support.

[0038] The voice conversion unit can customize the tone or speed of the voice in converting the analysis results into voice according to the preferences of the visually impaired. For example, the voice conversion unit builds a system that customizes the tone or speed of the voice in converting the analysis results into voice according to the preferences of the visually impaired. For example, the voice conversion unit can increase the tone of the voice or adjust the speed. This makes it possible to customize the tone and speed of the voice according to the preferences of the visually impaired.

[0039] The voice conversion unit can communicate the analysis results not only by voice but also by vibration using a haptic feedback device. The voice conversion unit can, for example, build a system that communicates the analysis results not only by voice but also by vibration using a haptic feedback device. For example, a device worn by a visually impaired person communicates information by vibration. This allows the visually impaired person to obtain information through both voice and haptic feedback.

[0040] The speech conversion unit can display the analysis results not only as voice but also as text on a smartwatch carried by a visually impaired person. The speech conversion unit can, for example, build a system that displays the analysis results not only as voice but also as text on a smartwatch carried by a visually impaired person. For example, the analysis results can be displayed on the screen of the smartwatch. This allows visually impaired people to obtain information both as voice and text.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The system can be equipped with a vibration feedback function as a new means for the visually impaired to understand their surroundings. For example, a vibration motor could be built into a wristband or belt worn by the visually impaired, which would transmit information via vibration based on the analysis results. Information could be conveyed through vibration patterns, such as a strong vibration if there is an obstacle ahead, or the vibration motor on the right if there is an obstacle on the right side. This would allow the visually impaired to understand their surroundings using tactile information in addition to audio information.

[0043] The system can automatically provide information about a specific location when a visually impaired person reaches that location. For example, using the GPS function, when a visually impaired person approaches a specific landmark or building, information about that location is provided by voice. Specifically, location information can be provided in the form of, "This is X Park. There is a bench on the left," or "This is X Station. The ticket gate is on the right." This allows visually impaired people to quickly obtain the information they need when they reach their destination.

[0044] The system can display analysis results as text and icons on the smartphones and smartwatches carried by visually impaired people. For example, text information such as "There is a pedestrian ahead" or "There is a bicycle on the right" can be displayed on the smartphone screen. The location of obstacles can also be indicated by icons on the smartwatch screen. This allows visually impaired people to understand their surroundings using visual information in addition to audio information.

[0045] The system uses a camera attached to a cane used by a visually impaired person to capture video images in response to the cane's movements. For example, a small camera can be installed at the tip of the cane, capturing video of the surroundings in response to the cane's movements. This allows the visually impaired person to capture video images in response to the cane's movements and understand the situation around them. The camera's viewpoint can also be automatically adjusted in response to the cane's movements.

[0046] When a visually impaired person reaches a specific location, the system can provide the historical and cultural background of that location. For example, when a visually impaired person approaches a historical landmark using the GPS function, the system can provide audio information about the location's historical and cultural background. Specifically, the system can provide detailed information about the location, such as, "This is X-X Castle. It was built in the 16th century by X-X clan." This allows the visually impaired person to learn the background information of the places they visit.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The video acquisition unit acquires video from a smartphone camera. For example, if a visually impaired person is holding a smartphone while walking, the smartphone camera captures video of the surroundings in real time. Step 2: The analysis unit analyzes the video captured by the video capture unit. For example, the generation AI recognizes objects, people, obstacles, etc. captured by the camera and analyzes their positions and movements. The generation AI performs its analysis using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The speech conversion unit converts the results of the analysis by the analysis unit into speech, providing necessary information to the visually impaired in the form of, for example, "There is a pedestrian ahead," "There is a bicycle on the right," or "There is a step on the left." Step 4: The information providing unit provides the user with the voice converted by the voice conversion unit, for example, through the speaker of a smartphone or dedicated device.

[0049] (Example 2) The system according to the embodiment of the present invention is a system for visually impaired people to understand their surroundings. This system uses a generation AI to analyze video images acquired from a smartphone camera or the like, and converts the analysis results into audio to communicate to the user. This allows the system to enable visually impaired people to understand their surroundings.

[0050] The system according to the embodiment includes a video acquisition unit, an analysis unit, a voice conversion unit, and an information provision unit. The video acquisition unit acquires video from a smartphone camera. For example, if a visually impaired person is walking and holding a smartphone, the smartphone's camera captures video of the surroundings in real time. The analysis unit analyzes the video acquired by the video acquisition unit. For example, a generation AI recognizes objects, people, obstacles, etc. captured by the camera and analyzes their positions and movements. The generation AI performs analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The voice conversion unit converts the results of the analysis by the analysis unit into voice. For example, information necessary for the visually impaired person is provided by voice, such as "There is a pedestrian ahead," "There is a bicycle on the right," or "There is a step on the left." The information provision unit provides the user with the voice converted by the voice conversion unit. For example, the information is transmitted to the user through a speaker on a smartphone or a dedicated device. This allows the system according to the embodiment to enable the visually impaired person to understand their surroundings.

[0051] The video image acquisition unit can use a small camera attached to the glasses of the visually impaired person. For example, the video image acquisition unit attaches a small camera to the glasses of the visually impaired person and uses the camera to acquire video images of the surroundings. For example, a camera can be built into the frame of the glasses and capture video images aligned with the line of sight of the visually impaired person. This allows the visually impaired person to acquire video images without using their hands.

[0052] The video image acquisition unit can acquire video images of the surroundings of the visually impaired person using a drone. The video image acquisition unit, for example, flies the drone around the visually impaired person to acquire wide-ranging video images. For example, the drone flies in front of or to the side of the visually impaired person to capture the surrounding situation. This allows the visually impaired person to grasp a wide range of the surroundings.

[0053] The video image acquisition unit can automatically adjust the viewpoint or focus of the camera according to the emotional state of the visually impaired person using the emotion estimation function. For example, the video image acquisition unit uses the emotion estimation function to analyze the emotional state of the visually impaired person in real time and automatically adjust the viewpoint or focus of the camera. For example, if the visually impaired person is feeling anxious, detailed information about the surroundings is prioritized. This makes it possible to prioritize the acquisition of necessary information according to the emotional state of the visually impaired person.

[0054] The video image acquisition unit can simultaneously acquire surrounding audio data in addition to acquiring video images. For example, the video image acquisition unit can simultaneously collect surrounding audio data and send it to the generation AI. For example, a microphone can be built into the camera to record surrounding audio in real time. This allows visually impaired people to understand surrounding audio information.

[0055] The video image acquisition unit can attach a camera to a cane held by a visually impaired person and acquire video images in accordance with the movement of the cane. The video image acquisition unit, for example, attaches a small camera to the cane of the visually impaired person and acquires video images using the camera. For example, a camera is installed at the tip of the cane and captures video of the surroundings in accordance with the movement of the cane. This allows the visually impaired person to acquire video images in accordance with the movement of the cane.

[0056] The video acquisition unit can use the emotion estimation function to preferentially acquire video in situations in which the visually impaired person feels particularly anxious. For example, the video acquisition unit uses the emotion estimation function to identify situations in which the visually impaired person feels anxious and preferentially acquire video in those situations. For example, the video acquisition unit increases the frequency of camera capture in places and times when the visually impaired person feels anxious. This allows the video to be preferentially acquired in situations in which the visually impaired person feels anxious.

[0057] When analyzing video images, the generative AI learns from past analysis results and can perform highly accurate analysis. For example, the generative AI learns from past analysis results and improves the accuracy of video image analysis. For example, it improves the accuracy of object recognition based on past data. In this way, the generative AI learns from past analysis results and improves the accuracy of analysis.

[0058] In addition to analyzing video images, the generative AI can also analyze environmental data such as the surrounding temperature or humidity and provide it to visually impaired people. For example, in addition to analyzing video images, the generative AI can collect and analyze environmental data such as the surrounding temperature and humidity. For example, it can acquire environmental data using a temperature sensor or humidity sensor. This allows visually impaired people to understand the surrounding environmental data.

[0059] The generation AI uses the emotion estimation function to change the priority of analysis results according to the emotional state of the visually impaired person, and can quickly provide the necessary information. For example, the generation AI uses the emotion estimation function to analyze the emotional state of the visually impaired person, and changes the priority of analysis results based on the results. For example, if the visually impaired person is feeling anxious, important information will be provided first. This makes it possible to quickly provide the necessary information according to the emotional state of the visually impaired person.

[0060] Generative AI can display the results of video analysis in real time on a smartphone app for visually impaired users, providing information in conjunction with audio. Generative AI can, for example, build a system that displays the results of video analysis in real time on a smartphone app for visually impaired users, providing information in conjunction with audio. For example, the analysis results can be displayed as text or icons. This allows visually impaired users to understand the situation using both audio and visual information.

[0061] The generative AI can share the analysis results with the family or caregiver of the visually impaired person, thereby strengthening support. For example, the generative AI can build a system that shares the analysis results with the family and caregiver of the visually impaired person. For example, the analysis results can be notified to the smartphone of the family or caregiver. This allows the family and caregiver of the visually impaired person to share the analysis results and strengthen support.

[0062] Using its emotion estimation function, the generative AI can prioritize analyzing objects or places that are of particular interest to visually impaired people and provide that information. For example, the generative AI can use its emotion estimation function to identify objects and places that are of interest to visually impaired people and build a system that prioritizes analyzing that information. For example, it can focus its analysis on objects of interest. This allows visually impaired people to prioritize obtaining information on objects and places that interest them.

[0063] The voice conversion unit can customize the tone or speed of the voice in converting the analysis results into voice according to the preferences of the visually impaired. For example, the voice conversion unit builds a system that customizes the tone or speed of the voice in converting the analysis results into voice according to the preferences of the visually impaired. For example, the voice conversion unit can increase the tone of the voice or adjust the speed. This makes it possible to customize the tone and speed of the voice according to the preferences of the visually impaired.

[0064] The voice conversion unit can communicate the analysis results not only by voice but also by vibration using a haptic feedback device. The voice conversion unit can, for example, build a system that communicates the analysis results not only by voice but also by vibration using a haptic feedback device. For example, a device worn by a visually impaired person communicates information by vibration. This allows the visually impaired person to obtain information through both voice and haptic feedback.

[0065] The voice conversion unit can use the emotion estimation function to adjust the tone or content of the voice depending on the emotional state of the visually impaired person, thereby providing a sense of security. For example, the voice conversion unit can use the emotion estimation function to analyze the emotional state of the visually impaired person, and build a system that adjusts the tone or content of the voice based on the results. For example, if the visually impaired person is feeling anxious, information can be provided in a calm tone. This allows the tone or content of the voice to be adjusted depending on the emotional state of the visually impaired person, providing a sense of security.

[0066] The speech conversion unit can display the analysis results not only as voice but also as text on a smartwatch carried by a visually impaired person. The speech conversion unit can, for example, build a system that displays the analysis results not only as voice but also as text on a smartwatch carried by a visually impaired person. For example, the analysis results can be displayed on the screen of the smartwatch. This allows visually impaired people to obtain information both as voice and text.

[0067] The voice conversion unit uses the emotion estimation function to provide analysis results by voice preferentially in situations where visually impaired people feel particularly anxious, thereby giving them a sense of security. For example, the voice conversion unit uses the emotion estimation function to identify situations where visually impaired people feel anxious, and builds a system where analysis results in those situations are provided by voice preferentially. For example, obstacle information is provided preferentially in places where people feel anxious. This allows analysis results to be provided by voice preferentially in situations where visually impaired people feel anxious, giving them a sense of security.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The system can be equipped with a vibration feedback function as a new means for the visually impaired to understand their surroundings. For example, a vibration motor could be built into a wristband or belt worn by the visually impaired, which would transmit information via vibration based on the analysis results. Information could be conveyed through vibration patterns, such as a strong vibration if there is an obstacle ahead, or the vibration motor on the right if there is an obstacle on the right side. This would allow the visually impaired to understand their surroundings using tactile information in addition to audio information.

[0070] The system can provide more detailed information about the surrounding situation depending on the emotional state of the visually impaired person. For example, if the emotional estimation function is used and the visually impaired person is feeling anxious, the system can provide more detailed analysis results in voice. Specifically, rather than just saying "There is a pedestrian ahead," the system can provide more detailed information such as "There is a pedestrian ahead. They are about 5 meters away. They are moving slowly." This allows the visually impaired person to act with peace of mind.

[0071] The system can automatically provide information about a specific location when a visually impaired person reaches that location. For example, using the GPS function, when a visually impaired person approaches a specific landmark or building, information about that location is provided by voice. Specifically, location information can be provided in the form of, "This is X Park. There is a bench on the left," or "This is X Station. The ticket gate is on the right." This allows visually impaired people to quickly obtain the information they need when they reach their destination.

[0072] The system can adjust the tone and content of the voice depending on the emotional state of the visually impaired person. For example, if the emotion estimation function is used and the visually impaired person is feeling nervous, the voice tone can be changed to a calmer tone to give them a sense of security. Specifically, the system can provide information in a calm tone, such as "There is an obstacle ahead. Please proceed slowly." This allows the visually impaired person to act with peace of mind.

[0073] The system can display analysis results as text and icons on the smartphones and smartwatches carried by visually impaired people. For example, text information such as "There is a pedestrian ahead" or "There is a bicycle on the right" can be displayed on the smartphone screen. The location of obstacles can also be indicated by icons on the smartwatch screen. This allows visually impaired people to understand their surroundings using visual information in addition to audio information.

[0074] The system can adjust the frequency of providing analysis results depending on the emotional state of the visually impaired person. For example, if the emotional estimation function indicates that the visually impaired person is feeling anxious, the system can provide analysis results more frequently. Specifically, information that is normally provided every minute can be provided every 30 seconds depending on the visually impaired person's emotional state. This allows the visually impaired person to act with peace of mind.

[0075] When a visually impaired person shows interest in a particular object or place, the system can provide that information preferentially. For example, the system can use emotion estimation to identify the object or place that the visually impaired person is interested in and provide detailed information about it. Specifically, the system can provide information of interest preferentially, such as "There is an art museum ahead. The exhibits are about ____." This allows visually impaired people to quickly obtain information that interests them.

[0076] The system uses a camera attached to a cane used by a visually impaired person to capture video images in response to the cane's movements. For example, a small camera can be installed at the tip of the cane, capturing video of the surroundings in response to the cane's movements. This allows the visually impaired person to capture video images in response to the cane's movements and understand the situation around them. The camera's viewpoint can also be automatically adjusted in response to the cane's movements.

[0077] When a visually impaired person reaches a specific location, the system can provide the historical and cultural background of that location. For example, when a visually impaired person approaches a historical landmark using the GPS function, the system can provide audio information about the location's historical and cultural background. Specifically, the system can provide detailed information about the location, such as, "This is X-X Castle. It was built in the 16th century by X-X clan." This allows the visually impaired person to learn the background information of the places they visit.

[0078] The system can customize how it presents analysis results depending on the emotional state of the visually impaired person. For example, if the emotional estimation function is used to indicate that the visually impaired person is relaxed, the system can present the analysis results accompanied by calming music. Specifically, information such as "There is a pedestrian ahead" can be presented accompanied by relaxing music. This allows the visually impaired person to receive information in a relaxed state.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The video acquisition unit acquires video from a smartphone camera. For example, if a visually impaired person is holding a smartphone while walking, the smartphone camera captures video of the surroundings in real time. Step 2: The analysis unit analyzes the video captured by the video capture unit. For example, the generation AI recognizes objects, people, obstacles, etc. captured by the camera and analyzes their positions and movements. The generation AI performs its analysis using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The speech conversion unit converts the results of the analysis by the analysis unit into speech, providing necessary information to the visually impaired in the form of, for example, "There is a pedestrian ahead," "There is a bicycle on the right," or "There is a step on the left." Step 4: The information providing unit provides the user with the voice converted by the voice conversion unit, for example, through the speaker of a smartphone or dedicated device.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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. [Explanation of symbols]

[0148] 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 video acquisition unit that acquires video from a smartphone camera; an analysis unit that analyzes the moving image acquired by the moving image acquisition unit; a voice conversion unit that converts the analysis result by the analysis unit into voice; an information providing unit that provides the user with the voice converted by the voice converting unit; A system characterized by:

2. The moving image acquisition unit Using drones to capture video images of the surroundings of visually impaired people 2. The system of claim 1.

3. The generating AI is When analyzing the video, the system learns from past analysis results and performs highly accurate analysis.

2. The system of claim 1.

4. The voice conversion unit When converting analysis results into speech, customize the tone or speed of speech according to the preferences of visually impaired people.

2. The system of claim 1.

5. The moving image acquisition unit Using emotion estimation to automatically adjust the camera's viewpoint or focus according to the emotional state of a visually impaired person 2. The system of claim 1.

Images