system

Abstract

The system according to this embodiment aims to enable visually impaired individuals to obtain visual information while traveling or in their daily lives. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, an audio output unit, and an identification unit. The acquisition unit acquires landscape images. The analysis unit analyzes the landscape images acquired by the acquisition unit. The audio output unit outputs the information analyzed by the analysis unit as audio. The identification unit identifies the color and freshness of food, the design of clothing, etc., in everyday life.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for visually impaired people to obtain visual information at travel destinations or in daily life, and there is room for improvement.

[0005] The system according to the embodiment aims to enable visually impaired people to obtain visual information at travel destinations or in daily life.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, an audio output unit, and an identification unit. The acquisition unit acquires landscape images. The analysis unit analyzes the landscape images acquired by the acquisition unit. The audio output unit outputs the information analyzed by the analysis unit in audio. The identification unit identifies the color, taste, freshness of food, the design of clothing, etc. in daily life. [Effects of the Invention]

[0007] The system according to this embodiment allows visually impaired individuals to obtain visual information while traveling or in their daily lives. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI smart glasses guide system according to an embodiment of the present invention is a system that provides support to enable visually impaired people to live their lives more freely. When a visually impaired person is wearing smart glasses, the smart glasses capture the surrounding scenery with a camera and transmit the scenery image to the AI ​​in real time. The AI ​​uses image recognition technology to identify objects, colors, backgrounds, etc., contained in the scenery image and conveys the analyzed information to the visually impaired person via earphones. Since the earphones are bone conduction type, ambient sounds can also be heard, ensuring safe use. For example, the AI ​​can convey information such as, "There is a building ahead. There is a road sign on the right." Furthermore, in daily life, by using smart glasses and earphones, it is possible to identify the color and freshness of food, the design of clothing, etc., and provide advice. For example, the AI ​​can convey information such as, "This apple is red and fresh," or "This clothing is blue and has a simple design." This mechanism allows visually impaired people to live their lives more freely without the assistance of caregivers, reducing the burden on family members and cohabitants. It can also be used in facilities for people with disabilities as a substitute for caregivers, contributing to the alleviation of financial difficulties. This means that the AI ​​smart glasses guide system can provide support to help visually impaired people live their lives more freely.

[0029] The AI ​​smart glasses guide system according to this embodiment comprises an acquisition unit, an analysis unit, an audio output unit, and an identification unit. The acquisition unit acquires landscape images. The acquisition unit acquires landscape images using, for example, a camera mounted on smart glasses. The acquisition unit can acquire still images and videos. The acquisition unit, for example, uses the smart glasses' camera to acquire high-resolution images and transmits them to the AI ​​in real time. The acquisition unit can adjust the resolution and frame rate of the landscape images. The analysis unit analyzes the landscape images acquired by the acquisition unit. The analysis unit identifies objects, colors, backgrounds, etc., contained in the landscape images using, for example, an image recognition algorithm. The analysis unit, for example, uses the AI ​​to identify buildings, road signs, plants, etc., from the landscape images and analyzes their information. The analysis unit can use deep learning technology to improve the accuracy of image recognition. The audio output unit outputs the information analyzed by the analysis unit as audio. The audio output unit outputs the analysis results as audio using, for example, speech synthesis technology. The audio output unit can output audio using bone conduction earphones. The voice output unit provides information such as, for example, "There is a building ahead. There is a road sign on the right," via voice. The voice output unit can adjust the type and volume of the voice. The identification unit identifies things like the color and freshness of food, and the design of clothing in everyday life. For example, the identification unit can have the AI ​​identify the color and freshness of food and provide advice. For example, the identification unit can have the AI ​​provide information such as, "This apple is red and fresh," via voice. The identification unit can also identify the design of clothing and provide advice. For example, the identification unit can have the AI ​​provide information such as, "This clothing is blue and has a simple design," via voice. As a result, the AI ​​smart glasses guide system according to this embodiment can provide support to help visually impaired people live their lives more freely.

[0030] The acquisition unit acquires landscape images. For example, the acquisition unit acquires landscape images using a camera mounted on smart glasses. The acquisition unit can acquire still images and videos. Specifically, the camera on the smart glasses acquires high-resolution images and transmits them to the AI ​​in real time. The acquisition unit can adjust the resolution and frame rate of the landscape images. For example, by setting the camera resolution high, images with sharp details can be acquired, and by increasing the frame rate, smooth videos can be acquired even in moving scenes. Furthermore, the acquisition unit can be equipped with an infrared camera or a high-sensitivity sensor to acquire high-quality images even in low-light environments. This allows for accurate acquisition of visual information even at night or in dark places. In addition, the acquisition unit can acquire a wide-angle lens to capture a wide landscape at once. This allows the user to have a broader understanding of their surroundings. The acquisition unit can use data compression technology to compress the acquired image data and transmit it to the AI ​​efficiently. This enables real-time data processing and improves the response speed of the entire system.

[0031] The analysis unit analyzes the landscape images acquired by the acquisition unit. For example, the analysis unit uses image recognition algorithms to identify objects, colors, and backgrounds within the landscape images. Specifically, the AI ​​identifies buildings, road signs, plants, etc., from the landscape images and analyzes this information. The analysis unit can utilize deep learning techniques to improve the accuracy of image recognition. For example, it can use a convolutional neural network (CNN) to extract features from the image and identify objects. Furthermore, the analysis unit can use stereo cameras and depth sensors to estimate the position and distance of objects. This allows the user to accurately understand the spatial relationships of surrounding objects. The analysis unit can continuously learn its analysis algorithms and improve accuracy based on past data and user feedback. For example, if a user misidentifies a particular object, this information is incorporated as feedback to modify the algorithm. Additionally, the analysis unit can improve the accuracy and reliability of the analysis by combining multiple image recognition algorithms. For example, combining an object recognition algorithm with a color recognition algorithm can yield more accurate analysis results. This allows the analysis unit to quickly and accurately analyze the acquired landscape images and provide useful information to the user.

[0032] The audio output unit outputs the information analyzed by the analysis unit as audio. For example, the audio output unit uses speech synthesis technology to output the analysis results as audio. Specifically, a speech synthesis engine converts the analysis results from text to audio and conveys them to the user. The audio output unit can output audio using bone conduction earphones. This allows the user to receive necessary information in audio while still hearing ambient sounds. The audio output unit can adjust the type and volume of the audio. For example, users can select male or female voices, different accents, or languages ​​according to their preferences. Adjusting the volume also makes the information easier to hear in noisy environments. Furthermore, the audio output unit can output the analysis results at an appropriate time according to the context. For example, if there is an obstacle ahead while the user is walking, it can immediately provide that information via audio. The audio output unit can also adjust the content and timing of the audio output based on user feedback. This enables the provision of optimal information to the user. When outputting multiple pieces of information simultaneously, the audio output unit can set information priorities and prioritize the transmission of important information. This allows the user to quickly grasp the necessary information.

[0033] The identification unit identifies the color and freshness of food, the design of clothing, and other aspects of daily life. For example, the AI ​​can identify the color and freshness of food and provide advice. Specifically, the AI ​​analyzes images of food and determines freshness based on color, shape, and surface condition. For example, if an apple is a vibrant red color and has no blemishes or discoloration on its surface, it will be determined to be fresh. Based on the analysis results, the identification unit will communicate information such as, "This apple is red and fresh," via voice. The identification unit can also identify the design of clothing and provide advice. For example, the AI ​​analyzes images of clothing and identifies the color, design, and material. Based on the analysis results, it will communicate information such as, "This clothing is blue and has a simple design," via voice. Furthermore, the identification unit can provide more personalized advice based on the user's preferences and past selection history. For example, considering the colors and designs the user has previously preferred, it will provide advice such as, "This blue clothing suits your taste." The identification unit can also identify the nutritional value and allergy information of food and provide appropriate advice to the user. This allows users to obtain information to maintain a healthy diet. The identification unit can support users in various situations in daily life, providing assistance to visually impaired individuals to live their lives more freely.

[0034] The audio output unit can output sound using bone conduction earphones. The audio output unit, for example, can use bone conduction earphones to allow the user to hear ambient sounds, thus enabling safe use. The audio output unit, for example, uses bone conduction earphones to transmit sound using a specific frequency band. The audio output unit can adjust the way the bone conduction earphones are worn. This allows the user to hear ambient sounds, thus enabling safe use. Some or all of the above processing in the audio output unit may be performed using AI, for example, or without AI. For example, when the audio output unit outputs sound using bone conduction earphones, the AI ​​can adjust the type and volume of the sound.

[0035] The identification unit can identify the color and freshness of food and provide advice. For example, the identification unit uses AI to identify the color and freshness of food and provide advice. For example, the identification unit uses AI to verbally communicate information such as, "This apple is red and fresh." The identification unit can adjust the evaluation criteria for the color and freshness of food. For example, the identification unit uses criteria such as hue, saturation, and brightness to identify the color of food. The identification unit uses criteria such as shelf life and changes in appearance to identify the freshness of food. In this way, by identifying the color and freshness of food and providing advice, it is possible to support the daily lives of visually impaired people. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies the color and freshness of food, AI may use criteria such as hue, saturation, and brightness for identification.

[0036] The identification unit can identify clothing designs and provide advice. For example, the identification unit uses AI to identify clothing designs and provide advice. For example, the identification unit uses AI to verbally communicate information such as, "This clothing is blue and has a simple design." The identification unit can adjust the criteria for evaluating clothing designs. For example, the identification unit uses criteria such as color combinations, shapes, and patterns to identify clothing designs. This allows for support in the daily lives of visually impaired individuals by identifying clothing designs and providing advice. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies clothing designs, AI may use criteria such as color combinations, shapes, and patterns for identification.

[0037] The acquisition unit can acquire landscape images using smart glasses. The acquisition unit can acquire landscape images using, for example, a camera mounted on the smart glasses. The acquisition unit can adjust the resolution of the smart glasses' camera. The acquisition unit can acquire landscape images using, for example, a high-resolution camera. The acquisition unit can adjust the way the smart glasses are worn. This allows visually impaired people to acquire the surrounding scenery in real time by using smart glasses. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, when the acquisition unit acquires landscape images using the smart glasses' camera, the AI ​​can adjust the camera's resolution.

[0038] The analysis unit can identify objects, colors, backgrounds, etc., contained in landscape images. For example, the analysis unit uses AI to identify objects, colors, backgrounds, etc., contained in landscape images. For example, the analysis unit uses AI to identify buildings, road signs, plants, etc., from landscape images and analyze their information. The analysis unit can adjust the identification criteria for objects, colors, and backgrounds. For example, the analysis unit identifies objects such as people, cars, and buildings. The analysis unit identifies colors using criteria such as hue, saturation, and brightness. The analysis unit identifies backgrounds such as natural landscapes and urban landscapes. By identifying objects, colors, backgrounds, etc., contained in landscape images, detailed information can be provided to visually impaired people. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit identifies objects, colors, backgrounds, etc., contained in landscape images, the AI ​​can adjust the identification criteria for objects, colors, and backgrounds.

[0039] The acquisition unit can select the optimal acquisition method when acquiring landscape images by referring to the user's past travel history. The acquisition unit can adjust the frequency of landscape image acquisition based on places the user has visited in the past, for example. The acquisition unit can analyze the user's past travel patterns to determine the optimal acquisition timing, for example. The acquisition unit can prioritize acquiring landscape images of places the user has shown interest in in the past, for example. This allows the acquisition unit to select the optimal method for acquiring landscape images by referring to the user's past travel history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, when the acquisition unit selects the optimal acquisition method by referring to the user's past travel history, the AI ​​can perform the travel history analysis.

[0040] The acquisition unit can filter landscape images based on the user's current location information when acquiring them. For example, if the user is in a specific area, the acquisition unit will prioritize acquiring landscape images related to that area. For example, the acquisition unit will adjust the range of landscape images to be acquired, taking into account the distance from the user's current location. For example, if the user is moving, the acquisition unit will update the location information in real time and acquire the most suitable landscape images. This allows for the provision of highly relevant information by filtering landscape images based on the user's current location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, when the acquisition unit filters landscape images based on the user's current location information, the AI ​​may perform location information analysis.

[0041] The image acquisition unit can prioritize acquiring images that are highly relevant to the user's surrounding sound environment when acquiring landscape images. For example, if the user is in a quiet place, the acquisition unit will prioritize acquiring landscape images that evoke a sense of tranquility. For example, if the user is in a noisy place, the acquisition unit will prioritize acquiring landscape images that match the atmosphere. For example, if the user is in nature, the acquisition unit will prioritize acquiring landscape images that harmonize with the sounds of nature. In this way, by considering the user's surrounding sound environment, it is possible to acquire highly relevant landscape images. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, when the acquisition unit acquires landscape images while considering the user's surrounding sound environment, the AI ​​can perform sound environment analysis.

[0042] The acquisition unit can analyze the user's social media activity and acquire relevant images when acquiring landscape images. For example, the acquisition unit can acquire relevant landscape images based on locations shared by the user on social media. For example, the acquisition unit can analyze the content of the user's social media posts and acquire landscape images that are likely to be of interest. For example, the acquisition unit can acquire relevant landscape images by referring to locations shared by the user's followers. In this way, relevant landscape images can be acquired by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, when the acquisition unit analyzes the user's social media activity and acquires landscape images, the AI ​​can analyze the content of social media posts.

[0043] The analysis unit can adjust the level of detail in its analysis of landscape images based on the importance of the image. For example, it will analyze images containing important landmarks in detail. For example, it will analyze general landscape images concisely. For example, it will analyze images that the user has shown particular interest in in detail. By adjusting the level of detail in the analysis based on the importance of the image, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit analyzes landscape images, the AI ​​may evaluate the importance of the image and adjust the level of detail in the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the image category when analyzing landscape images. For example, the analysis unit applies a building recognition algorithm to images of buildings. For example, the analysis unit applies a natural object recognition algorithm to images of natural landscapes. For example, the analysis unit applies a sign recognition algorithm to images of road signs. By applying different analysis algorithms depending on the image category, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit analyzes landscape images, the AI ​​can determine the image category and apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on when the images were taken when analyzing landscape images. For example, the analysis unit may prioritize the analysis of the most recent images. For example, the analysis unit may prioritize the analysis of images taken during a specific event period. For example, the analysis unit may prioritize the analysis of images that capture seasonal changes. By determining the priority of analysis based on when the images were taken, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit analyzes landscape images, the AI ​​may evaluate when the images were taken and determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the images when analyzing landscape images. For example, the analysis unit may prioritize the analysis of images related to the user's current location. For example, the analysis unit may prioritize the analysis of images related to the user's past travel history. For example, the analysis unit may prioritize the analysis of images that are highly relevant based on the user's interests. By adjusting the order of analysis based on the relevance of the images, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit analyzes landscape images, the AI ​​may evaluate the relevance of the images and adjust the order of analysis.

[0047] The audio output unit can adjust the level of detail in the audio output based on the importance of the information. For example, the audio output unit will provide detailed audio guidance for important information. For example, the audio output unit will provide concise audio guidance for general information. For example, the audio output unit will provide detailed audio guidance for information that the user has shown particular interest in. By adjusting the level of detail in the audio output unit based on the importance of the information, it becomes possible to provide more appropriate information. Some or all of the above processing in the audio output unit may be performed using AI, for example, or without AI. For example, when the audio output unit outputs information, the AI ​​can evaluate the importance of the information and adjust the level of detail in the audio.

[0048] The audio output unit can apply different audio output algorithms depending on the category of information when outputting information as audio. For example, the audio output unit can apply a gentle audio output algorithm to landscape information, a clear and concise audio output algorithm to road information, and a detailed audio output algorithm to building information. By applying different audio output algorithms depending on the category of information, it becomes possible to provide more appropriate information. Some or all of the above processing in the audio output unit may be performed using AI, for example, or without AI. For example, when the audio output unit outputs information as audio, the AI ​​can determine the category of the information and apply an appropriate audio output algorithm.

[0049] The audio output unit can determine the priority of audio output based on the timing of information acquisition. For example, the audio output unit may prioritize the latest information in audio announcements. For example, the audio output unit may prioritize the information during a specific event period in audio announcements. For example, the audio output unit may prioritize the information that captures seasonal changes in audio announcements. By determining the priority of audio based on the timing of information acquisition, it becomes possible to provide more appropriate information. Some or all of the above processing in the audio output unit may be performed using AI, for example, or without AI. For example, when the audio output unit outputs information in audio, the AI ​​may evaluate the timing of information acquisition and determine the priority of audio.

[0050] The voice output unit can adjust the order of the audio based on the relevance of the information when outputting information by voice. For example, the voice output unit may prioritize providing information related to the user's current location. For example, the voice output unit may prioritize providing information related to the user's past travel history. For example, the voice output unit may prioritize providing information that is highly relevant based on the user's interests. By adjusting the order of the audio based on the relevance of the information, it becomes possible to provide more appropriate information. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, when the voice output unit outputs information by voice, the AI ​​may evaluate the relevance of the information and adjust the order of the audio.

[0051] The identification unit can adjust the level of detail of identification based on the importance of the information when identifying the color and freshness of food, the design of clothing, etc. For example, the identification unit can identify and provide important food freshness information in detail. For example, the identification unit can identify and provide general food color information in a concise manner. For example, the identification unit can identify and provide clothing designs that the user is particularly interested in in detail. By adjusting the level of detail of identification based on the importance of the information, it becomes possible to provide more appropriate information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies the color and freshness of food, the design of clothing, etc., the AI ​​can evaluate the importance of the information and adjust the level of detail of identification.

[0052] The identification unit can apply different identification algorithms depending on the category of information when identifying the color and freshness of food, the design of clothing, etc. For example, the identification unit can apply a color recognition algorithm to the color of food. For example, the identification unit can apply a freshness evaluation algorithm to the freshness of food. For example, the identification unit can apply a design recognition algorithm to the design of clothing. By applying different identification algorithms depending on the category of information, it becomes possible to provide more appropriate information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies the color and freshness of food, the design of clothing, etc., the AI ​​can determine the category of information and apply an appropriate identification algorithm.

[0053] The identification unit can determine the priority of identification based on the timing of information acquisition when identifying the color and freshness of food, clothing design, etc. For example, the identification unit may prioritize the identification of the most recent food information. For example, the identification unit may prioritize the identification of clothing designs during a specific event period. For example, the identification unit may prioritize the identification of food information that captures seasonal changes. By determining the priority of identification based on the timing of information acquisition, it becomes possible to provide more appropriate information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies the color and freshness of food, clothing design, etc., the AI ​​may evaluate the timing of information acquisition and determine the priority of identification.

[0054] The identification unit can adjust the order of identification based on the relevance of the information when identifying the color and freshness of food, the design of clothing, etc. For example, the identification unit may prioritize identifying food information related to the user's current meal. For example, the identification unit may prioritize identifying food information related to the user's past purchase history. For example, the identification unit may prioritize identifying clothing designs that are highly relevant based on the user's interests. By adjusting the order of identification based on the relevance of the information, it becomes possible to provide more appropriate information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies the color and freshness of food, the design of clothing, etc., the AI ​​may evaluate the relevance of the information and adjust the order of identification.

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

[0056] The analysis unit can analyze audio information contained in landscape images and provide it to the user via audio. For example, it can analyze car sounds and human voices contained in landscape images and provide the user with information such as "A car is passing ahead" or "There is a person talking on the right." It can also analyze natural sounds contained in landscape images and provide the user with information such as "There is a river flowing on the left" or "You can hear birds chirping." Furthermore, it can analyze audio information contained in landscape images in real time and provide information to the user immediately. As a result, by analyzing audio information contained in landscape images, more detailed information can be provided to visually impaired people.

[0057] The voice output unit can refer to the user's past voice command history and provide voice output tailored to the user's preferences. For example, if the user has frequently used a particular voice command in the past, the voice output unit can provide information based on that command. Also, if the user has preferred a particular voice output in the past, the voice output unit can prioritize using that voice output. Furthermore, by analyzing the user's past voice command history, it is possible to provide voice output tailored to the user's preferences. This allows for more appropriate voice output by referring to the user's past voice command history.

[0058] The analysis unit can analyze temperature information contained in landscape images and provide users with information about the temperature. For example, by analyzing the temperature information contained in a landscape image, it can provide users with voice messages such as "The area in front is hot" or "The area on the right is cool." It can also analyze the temperature information contained in landscape images in real time and provide users with immediate information about the temperature. Furthermore, it can analyze the temperature information contained in landscape images and prompt users to take appropriate action. In this way, by analyzing the temperature information contained in landscape images, more detailed information can be provided to visually impaired people.

[0059] The identification unit can refer to the user's past identification history and provide identification results tailored to the user's preferences. For example, if the user has previously preferred the color or freshness of a particular food item, the identification unit can prioritize providing that information. Similarly, if the user has previously preferred a particular clothing design, the identification unit can prioritize providing that information. Furthermore, by analyzing the user's past identification history, it is possible to provide identification results tailored to the user's preferences. This allows for the provision of more appropriate identification results by referring to the user's past identification history.

[0060] The analysis unit can analyze humidity information contained in landscape images and provide users with information about humidity. For example, by analyzing the humidity information contained in landscape images, it can provide users with information such as "The area in front has high humidity" or "The area on the right has low humidity" via voice. It can also analyze the humidity information contained in landscape images in real time and provide users with information about humidity immediately. Furthermore, it can analyze the humidity information contained in landscape images and prompt users to take appropriate action. In this way, by analyzing the humidity information contained in landscape images, more detailed information can be provided to visually impaired people.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The acquisition unit acquires landscape images. The acquisition unit acquires landscape images using, for example, a camera mounted on smart glasses. The acquisition unit can acquire still images and videos. The acquisition unit, for example, uses the smart glasses' camera to acquire high-resolution images and transmits them to the AI ​​in real time. The acquisition unit can adjust the resolution and frame rate of the landscape images. Step 2: The analysis unit analyzes the landscape image acquired by the acquisition unit. The analysis unit identifies objects, colors, backgrounds, etc., contained in the landscape image using, for example, an image recognition algorithm. The analysis unit uses, for example, AI to identify buildings, road signs, plants, etc., from the landscape image and analyzes that information. The analysis unit may use deep learning technology to improve the accuracy of image recognition. Step 3: The audio output unit outputs the information analyzed by the analysis unit as audio. The audio output unit outputs the analysis results as audio, for example, using speech synthesis technology. The audio output unit can output audio using bone conduction earphones. The audio output unit can, for example, have the AI ​​communicate information such as, "There is a building ahead. There is a road sign on the right." The audio output unit can adjust the type and volume of the audio. Step 4: The identification unit identifies things like the color and freshness of food, and the design of clothing in everyday life. For example, the AI ​​in the identification unit can identify the color and freshness of food and provide advice. For example, the AI ​​in the identification unit can verbally convey information such as, "This apple is red and fresh." The identification unit can also identify the design of clothing and provide advice. For example, the AI ​​in the identification unit can verbally convey information such as, "This clothing is blue and has a simple design."

[0063] (Example of form 2) An AI smart glasses guide system according to an embodiment of the present invention is a system that provides support to enable visually impaired people to live their lives more freely. When a visually impaired person is wearing smart glasses, the smart glasses capture the surrounding scenery with a camera and transmit the scenery image to the AI ​​in real time. The AI ​​uses image recognition technology to identify objects, colors, backgrounds, etc., contained in the scenery image and conveys the analyzed information to the visually impaired person via earphones. Since the earphones are bone conduction type, ambient sounds can also be heard, ensuring safe use. For example, the AI ​​can convey information such as, "There is a building ahead. There is a road sign on the right." Furthermore, in daily life, by using smart glasses and earphones, it is possible to identify the color and freshness of food, the design of clothing, etc., and provide advice. For example, the AI ​​can convey information such as, "This apple is red and fresh," or "This clothing is blue and has a simple design." This mechanism allows visually impaired people to live their lives more freely without the assistance of caregivers, reducing the burden on family members and cohabitants. It can also be used in facilities for people with disabilities as a substitute for caregivers, contributing to the alleviation of financial difficulties. This means that the AI ​​smart glasses guide system can provide support to help visually impaired people live their lives more freely.

[0064] The AI ​​smart glasses guide system according to this embodiment comprises an acquisition unit, an analysis unit, an audio output unit, and an identification unit. The acquisition unit acquires landscape images. The acquisition unit acquires landscape images using, for example, a camera mounted on smart glasses. The acquisition unit can acquire still images and videos. The acquisition unit, for example, uses the smart glasses' camera to acquire high-resolution images and transmits them to the AI ​​in real time. The acquisition unit can adjust the resolution and frame rate of the landscape images. The analysis unit analyzes the landscape images acquired by the acquisition unit. The analysis unit identifies objects, colors, backgrounds, etc., contained in the landscape images using, for example, an image recognition algorithm. The analysis unit, for example, uses the AI ​​to identify buildings, road signs, plants, etc., from the landscape images and analyzes their information. The analysis unit can use deep learning technology to improve the accuracy of image recognition. The audio output unit outputs the information analyzed by the analysis unit as audio. The audio output unit outputs the analysis results as audio using, for example, speech synthesis technology. The audio output unit can output audio using bone conduction earphones. The voice output unit provides information such as, for example, "There is a building ahead. There is a road sign on the right," via voice. The voice output unit can adjust the type and volume of the voice. The identification unit identifies things like the color and freshness of food, and the design of clothing in everyday life. For example, the identification unit can have the AI ​​identify the color and freshness of food and provide advice. For example, the identification unit can have the AI ​​provide information such as, "This apple is red and fresh," via voice. The identification unit can also identify the design of clothing and provide advice. For example, the identification unit can have the AI ​​provide information such as, "This clothing is blue and has a simple design," via voice. As a result, the AI ​​smart glasses guide system according to this embodiment can provide support to help visually impaired people live their lives more freely.

[0065] The acquisition unit acquires landscape images. For example, the acquisition unit acquires landscape images using a camera mounted on smart glasses. The acquisition unit can acquire still images and videos. Specifically, the camera on the smart glasses acquires high-resolution images and transmits them to the AI ​​in real time. The acquisition unit can adjust the resolution and frame rate of the landscape images. For example, by setting the camera resolution high, images with sharp details can be acquired, and by increasing the frame rate, smooth videos can be acquired even in moving scenes. Furthermore, the acquisition unit can be equipped with an infrared camera or a high-sensitivity sensor to acquire high-quality images even in low-light environments. This allows for accurate acquisition of visual information even at night or in dark places. In addition, the acquisition unit can acquire a wide-angle lens to capture a wide landscape at once. This allows the user to have a broader understanding of their surroundings. The acquisition unit can use data compression technology to compress the acquired image data and transmit it to the AI ​​efficiently. This enables real-time data processing and improves the response speed of the entire system.

[0066] The analysis unit analyzes the landscape images acquired by the acquisition unit. For example, the analysis unit uses image recognition algorithms to identify objects, colors, and backgrounds within the landscape images. Specifically, the AI ​​identifies buildings, road signs, plants, etc., from the landscape images and analyzes this information. The analysis unit can utilize deep learning techniques to improve the accuracy of image recognition. For example, it can use a convolutional neural network (CNN) to extract features from the image and identify objects. Furthermore, the analysis unit can use stereo cameras and depth sensors to estimate the position and distance of objects. This allows the user to accurately understand the spatial relationships of surrounding objects. The analysis unit can continuously learn its analysis algorithms and improve accuracy based on past data and user feedback. For example, if a user misidentifies a particular object, this information is incorporated as feedback to modify the algorithm. Additionally, the analysis unit can improve the accuracy and reliability of the analysis by combining multiple image recognition algorithms. For example, combining an object recognition algorithm with a color recognition algorithm can yield more accurate analysis results. This allows the analysis unit to quickly and accurately analyze the acquired landscape images and provide useful information to the user.

[0067] The audio output unit outputs the information analyzed by the analysis unit as audio. For example, the audio output unit uses speech synthesis technology to output the analysis results as audio. Specifically, a speech synthesis engine converts the analysis results from text to audio and conveys them to the user. The audio output unit can output audio using bone conduction earphones. This allows the user to receive necessary information in audio while still hearing ambient sounds. The audio output unit can adjust the type and volume of the audio. For example, users can select male or female voices, different accents, or languages ​​according to their preferences. Adjusting the volume also makes the information easier to hear in noisy environments. Furthermore, the audio output unit can output the analysis results at an appropriate time according to the context. For example, if there is an obstacle ahead while the user is walking, it can immediately provide that information via audio. The audio output unit can also adjust the content and timing of the audio output based on user feedback. This enables the provision of optimal information to the user. When outputting multiple pieces of information simultaneously, the audio output unit can set information priorities and prioritize the transmission of important information. This allows the user to quickly grasp the necessary information.

[0068] The identification unit identifies the color and freshness of food, the design of clothing, and other aspects of daily life. For example, the AI ​​can identify the color and freshness of food and provide advice. Specifically, the AI ​​analyzes images of food and determines freshness based on color, shape, and surface condition. For example, if an apple is a vibrant red color and has no blemishes or discoloration on its surface, it will be determined to be fresh. Based on the analysis results, the identification unit will communicate information such as, "This apple is red and fresh," via voice. The identification unit can also identify the design of clothing and provide advice. For example, the AI ​​analyzes images of clothing and identifies the color, design, and material. Based on the analysis results, it will communicate information such as, "This clothing is blue and has a simple design," via voice. Furthermore, the identification unit can provide more personalized advice based on the user's preferences and past selection history. For example, considering the colors and designs the user has previously preferred, it will provide advice such as, "This blue clothing suits your taste." The identification unit can also identify the nutritional value and allergy information of food and provide appropriate advice to the user. This allows users to obtain information to maintain a healthy diet. The identification unit can support users in various situations in daily life, providing assistance to visually impaired individuals to live their lives more freely.

[0069] The audio output unit can output sound using bone conduction earphones. The audio output unit, for example, can use bone conduction earphones to allow the user to hear ambient sounds, thus enabling safe use. The audio output unit, for example, uses bone conduction earphones to transmit sound using a specific frequency band. The audio output unit can adjust the way the bone conduction earphones are worn. This allows the user to hear ambient sounds, thus enabling safe use. Some or all of the above processing in the audio output unit may be performed using AI, for example, or without AI. For example, when the audio output unit outputs sound using bone conduction earphones, the AI ​​can adjust the type and volume of the sound.

[0070] The identification unit can identify the color and freshness of food and provide advice. For example, the identification unit uses AI to identify the color and freshness of food and provide advice. For example, the identification unit uses AI to verbally communicate information such as, "This apple is red and fresh." The identification unit can adjust the evaluation criteria for the color and freshness of food. For example, the identification unit uses criteria such as hue, saturation, and brightness to identify the color of food. The identification unit uses criteria such as shelf life and changes in appearance to identify the freshness of food. In this way, by identifying the color and freshness of food and providing advice, it is possible to support the daily lives of visually impaired people. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies the color and freshness of food, AI may use criteria such as hue, saturation, and brightness for identification.

[0071] The identification unit can identify clothing designs and provide advice. For example, the identification unit uses AI to identify clothing designs and provide advice. For example, the identification unit uses AI to verbally communicate information such as, "This clothing is blue and has a simple design." The identification unit can adjust the criteria for evaluating clothing designs. For example, the identification unit uses criteria such as color combinations, shapes, and patterns to identify clothing designs. This allows for support in the daily lives of visually impaired individuals by identifying clothing designs and providing advice. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies clothing designs, AI may use criteria such as color combinations, shapes, and patterns for identification.

[0072] The acquisition unit can acquire landscape images using smart glasses. The acquisition unit can acquire landscape images using, for example, a camera mounted on the smart glasses. The acquisition unit can adjust the resolution of the smart glasses' camera. The acquisition unit can acquire landscape images using, for example, a high-resolution camera. The acquisition unit can adjust the way the smart glasses are worn. This allows visually impaired people to acquire the surrounding scenery in real time by using smart glasses. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, when the acquisition unit acquires landscape images using the smart glasses' camera, the AI ​​can adjust the camera's resolution.

[0073] The analysis unit can identify objects, colors, backgrounds, etc., contained in landscape images. For example, the analysis unit uses AI to identify objects, colors, backgrounds, etc., contained in landscape images. For example, the analysis unit uses AI to identify buildings, road signs, plants, etc., from landscape images and analyze their information. The analysis unit can adjust the identification criteria for objects, colors, and backgrounds. For example, the analysis unit identifies objects such as people, cars, and buildings. The analysis unit identifies colors using criteria such as hue, saturation, and brightness. The analysis unit identifies backgrounds such as natural landscapes and urban landscapes. By identifying objects, colors, backgrounds, etc., contained in landscape images, detailed information can be provided to visually impaired people. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit identifies objects, colors, backgrounds, etc., contained in landscape images, the AI ​​can adjust the identification criteria for objects, colors, and backgrounds.

[0074] The acquisition unit can estimate the user's emotions and adjust the timing of landscape image acquisition based on the estimated emotions. For example, if the user is excited, the acquisition unit will acquire landscape images frequently and provide detailed information. If the user is relaxed, the acquisition unit will acquire landscape images at regular intervals and provide calm information. If the user is tired, the acquisition unit will acquire only the minimum necessary landscape images and provide concise information. By adjusting the timing of landscape image acquisition according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, when the acquisition unit estimates the user's emotions and adjusts the timing of landscape image acquisition based on the estimated emotions, the AI ​​can perform the emotion estimation.

[0075] The acquisition unit can select the optimal acquisition method when acquiring landscape images by referring to the user's past travel history. The acquisition unit can adjust the frequency of landscape image acquisition based on places the user has visited in the past, for example. The acquisition unit can analyze the user's past travel patterns to determine the optimal acquisition timing, for example. The acquisition unit can prioritize acquiring landscape images of places the user has shown interest in in the past, for example. This allows the acquisition unit to select the optimal method for acquiring landscape images by referring to the user's past travel history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, when the acquisition unit selects the optimal acquisition method by referring to the user's past travel history, the AI ​​can perform the travel history analysis.

[0076] The acquisition unit can filter landscape images based on the user's current location information when acquiring them. For example, if the user is in a specific area, the acquisition unit will prioritize acquiring landscape images related to that area. For example, the acquisition unit will adjust the range of landscape images to be acquired, taking into account the distance from the user's current location. For example, if the user is moving, the acquisition unit will update the location information in real time and acquire the most suitable landscape images. This allows for the provision of highly relevant information by filtering landscape images based on the user's current location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, when the acquisition unit filters landscape images based on the user's current location information, the AI ​​may perform location information analysis.

[0077] The acquisition unit can estimate the user's emotions and determine the priority of landscape images to acquire based on the estimated user emotions. For example, if the user is excited, the acquisition unit will prioritize acquiring visually stimulating landscape images. For example, if the user is relaxed, the acquisition unit will prioritize acquiring calming landscape images. For example, if the user is tired, the acquisition unit will prioritize acquiring simple and highly visible landscape images. This allows for the provision of more appropriate information by prioritizing landscape images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, when the acquisition unit estimates the user's emotions and determines the priority of landscape images based on the estimated user emotions, the AI ​​can perform the emotion estimation.

[0078] The image acquisition unit can prioritize acquiring images that are highly relevant to the user's surrounding sound environment when acquiring landscape images. For example, if the user is in a quiet place, the acquisition unit will prioritize acquiring landscape images that evoke a sense of tranquility. For example, if the user is in a noisy place, the acquisition unit will prioritize acquiring landscape images that match the atmosphere. For example, if the user is in nature, the acquisition unit will prioritize acquiring landscape images that harmonize with the sounds of nature. In this way, by considering the user's surrounding sound environment, it is possible to acquire highly relevant landscape images. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, when the acquisition unit acquires landscape images while considering the user's surrounding sound environment, the AI ​​can perform sound environment analysis.

[0079] The acquisition unit can analyze the user's social media activity and acquire relevant images when acquiring landscape images. For example, the acquisition unit can acquire relevant landscape images based on locations shared by the user on social media. For example, the acquisition unit can analyze the content of the user's social media posts and acquire landscape images that are likely to be of interest. For example, the acquisition unit can acquire relevant landscape images by referring to locations shared by the user's followers. In this way, relevant landscape images can be acquired by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, when the acquisition unit analyzes the user's social media activity and acquires landscape images, the AI ​​can analyze the content of social media posts.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit provides visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions, the AI ​​can perform the emotion estimation.

[0081] The analysis unit can adjust the level of detail in its analysis of landscape images based on the importance of the image. For example, it will analyze images containing important landmarks in detail. For example, it will analyze general landscape images concisely. For example, it will analyze images that the user has shown particular interest in in detail. By adjusting the level of detail in the analysis based on the importance of the image, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit analyzes landscape images, the AI ​​may evaluate the importance of the image and adjust the level of detail in the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the image category when analyzing landscape images. For example, the analysis unit applies a building recognition algorithm to images of buildings. For example, the analysis unit applies a natural object recognition algorithm to images of natural landscapes. For example, the analysis unit applies a sign recognition algorithm to images of road signs. By applying different analysis algorithms depending on the image category, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit analyzes landscape images, the AI ​​can determine the image category and apply an appropriate analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, the analysis unit will prioritize analyzing visually stimulating images. For example, if the user is relaxed, the analysis unit will prioritize analyzing calming images. For example, if the user is in a hurry, the analysis unit will prioritize analyzing images containing important information. This allows for the provision of more appropriate information by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit estimates the user's emotions and determines the priority of analysis based on the estimated emotions, the AI ​​can perform the emotion estimation.

[0084] The analysis unit can determine the priority of analysis based on when the images were taken when analyzing landscape images. For example, the analysis unit may prioritize the analysis of the most recent images. For example, the analysis unit may prioritize the analysis of images taken during a specific event period. For example, the analysis unit may prioritize the analysis of images that capture seasonal changes. By determining the priority of analysis based on when the images were taken, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit analyzes landscape images, the AI ​​may evaluate when the images were taken and determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the images when analyzing landscape images. For example, the analysis unit may prioritize the analysis of images related to the user's current location. For example, the analysis unit may prioritize the analysis of images related to the user's past travel history. For example, the analysis unit may prioritize the analysis of images that are highly relevant based on the user's interests. By adjusting the order of analysis based on the relevance of the images, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit analyzes landscape images, the AI ​​may evaluate the relevance of the images and adjust the order of analysis.

[0086] The voice output unit can estimate the user's emotions and adjust the expression of the voice output based on the estimated emotions. For example, if the user is nervous, the voice output unit will provide guidance in a calm voice. If the user is relaxed, the voice output unit will provide guidance in a cheerful voice. If the user is in a hurry, the voice output unit will provide quick and concise voice guidance. By adjusting the expression of the voice output according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, when the voice output unit estimates the user's emotions and adjusts the expression of the voice output based on the estimated emotions, the AI ​​can perform the emotion estimation.

[0087] The audio output unit can adjust the level of detail in the audio output based on the importance of the information. For example, the audio output unit will provide detailed audio guidance for important information. For example, the audio output unit will provide concise audio guidance for general information. For example, the audio output unit will provide detailed audio guidance for information that the user has shown particular interest in. By adjusting the level of detail in the audio output unit based on the importance of the information, it becomes possible to provide more appropriate information. Some or all of the above processing in the audio output unit may be performed using AI, for example, or without AI. For example, when the audio output unit outputs information, the AI ​​can evaluate the importance of the information and adjust the level of detail in the audio.

[0088] The audio output unit can apply different audio output algorithms depending on the category of information when outputting information as audio. For example, the audio output unit can apply a gentle audio output algorithm to landscape information, a clear and concise audio output algorithm to road information, and a detailed audio output algorithm to building information. By applying different audio output algorithms depending on the category of information, it becomes possible to provide more appropriate information. Some or all of the above processing in the audio output unit may be performed using AI, for example, or without AI. For example, when the audio output unit outputs information as audio, the AI ​​can determine the category of the information and apply an appropriate audio output algorithm.

[0089] The voice output unit can estimate the user's emotions and determine the priority of voice output based on the estimated emotions. For example, if the user is excited, the voice output unit will prioritize providing visually stimulating information via voice. For example, if the user is relaxed, the voice output unit will prioritize providing calming information via voice. For example, if the user is in a hurry, the voice output unit will prioritize providing important information via voice. This allows for more appropriate information to be provided by determining the priority of voice output according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, when the voice output unit estimates the user's emotions and determines the priority of voice output based on the estimated emotions, the AI ​​can perform the emotion estimation.

[0090] The audio output unit can determine the priority of audio output based on the timing of information acquisition. For example, the audio output unit may prioritize the latest information in audio announcements. For example, the audio output unit may prioritize the information during a specific event period in audio announcements. For example, the audio output unit may prioritize the information that captures seasonal changes in audio announcements. By determining the priority of audio based on the timing of information acquisition, it becomes possible to provide more appropriate information. Some or all of the above processing in the audio output unit may be performed using AI, for example, or without AI. For example, when the audio output unit outputs information in audio, the AI ​​may evaluate the timing of information acquisition and determine the priority of audio.

[0091] The voice output unit can adjust the order of the audio based on the relevance of the information when outputting information by voice. For example, the voice output unit may prioritize providing information related to the user's current location. For example, the voice output unit may prioritize providing information related to the user's past travel history. For example, the voice output unit may prioritize providing information that is highly relevant based on the user's interests. By adjusting the order of the audio based on the relevance of the information, it becomes possible to provide more appropriate information. Some or all of the above processing in the voice output unit may be performed using AI, for example, or without AI. For example, when the voice output unit outputs information by voice, the AI ​​may evaluate the relevance of the information and adjust the order of the audio.

[0092] The identification unit can estimate the user's emotions and adjust the method of identification based on the estimated user emotions. For example, if the user is relaxed, the identification unit provides detailed identification results. For example, if the user is in a hurry, the identification unit provides concise identification results that get straight to the point. For example, if the user is excited, the identification unit provides visually stimulating identification results. This allows for more appropriate information to be provided by adjusting the method of identification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit estimates the user's emotions and adjusts the method of identification based on the estimated user emotions, the AI ​​can perform the emotion estimation.

[0093] The identification unit can adjust the level of detail of identification based on the importance of the information when identifying the color and freshness of food, the design of clothing, etc. For example, the identification unit can identify and provide important food freshness information in detail. For example, the identification unit can identify and provide general food color information in a concise manner. For example, the identification unit can identify and provide clothing designs that the user is particularly interested in in detail. By adjusting the level of detail of identification based on the importance of the information, it becomes possible to provide more appropriate information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies the color and freshness of food, the design of clothing, etc., the AI ​​can evaluate the importance of the information and adjust the level of detail of identification.

[0094] The identification unit can apply different identification algorithms depending on the category of information when identifying the color and freshness of food, the design of clothing, etc. For example, the identification unit can apply a color recognition algorithm to the color of food. For example, the identification unit can apply a freshness evaluation algorithm to the freshness of food. For example, the identification unit can apply a design recognition algorithm to the design of clothing. By applying different identification algorithms depending on the category of information, it becomes possible to provide more appropriate information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies the color and freshness of food, the design of clothing, etc., the AI ​​can determine the category of information and apply an appropriate identification algorithm.

[0095] The identification unit can estimate the user's emotions and determine the priority of identification based on the estimated user emotions. For example, if the user is excited, the identification unit will prioritize identifying visually stimulating information. For example, if the user is relaxed, the identification unit will prioritize identifying calming information. For example, if the user is in a hurry, the identification unit will prioritize identifying important information. This allows for the provision of more appropriate information by determining the priority of identification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, or not using AI. For example, when the identification unit estimates the user's emotions and determines the priority of identification based on the estimated user emotions, the AI ​​can perform the emotion estimation.

[0096] The identification unit can determine the priority of identification based on the timing of information acquisition when identifying the color and freshness of food, clothing design, etc. For example, the identification unit may prioritize the identification of the most recent food information. For example, the identification unit may prioritize the identification of clothing designs during a specific event period. For example, the identification unit may prioritize the identification of food information that captures seasonal changes. By determining the priority of identification based on the timing of information acquisition, it becomes possible to provide more appropriate information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies the color and freshness of food, clothing design, etc., the AI ​​may evaluate the timing of information acquisition and determine the priority of identification.

[0097] The identification unit can adjust the order of identification based on the relevance of the information when identifying the color and freshness of food, the design of clothing, etc. For example, the identification unit may prioritize identifying food information related to the user's current meal. For example, the identification unit may prioritize identifying food information related to the user's past purchase history. For example, the identification unit may prioritize identifying clothing designs that are highly relevant based on the user's interests. By adjusting the order of identification based on the relevance of the information, it becomes possible to provide more appropriate information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit identifies the color and freshness of food, the design of clothing, etc., the AI ​​may evaluate the relevance of the information and adjust the order of identification.

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

[0099] The acquisition unit can acquire biometric information such as the user's heart rate and body temperature, and adjust the timing of landscape image acquisition based on this information. For example, if the user's heart rate is elevated, the acquisition unit can acquire landscape images more frequently to provide detailed information. Also, if the user's body temperature is high, the acquisition unit can prioritize acquiring calm landscape images to encourage calm judgment. Furthermore, if the user's biometric information is stable, the acquisition unit can acquire landscape images at normal intervals to provide standard information. In this way, by adjusting the timing of landscape image acquisition based on the user's biometric information, it becomes possible to provide more appropriate information.

[0100] The analysis unit can analyze audio information contained in landscape images and provide it to the user via audio. For example, it can analyze car sounds and human voices contained in landscape images and provide the user with information such as "A car is passing ahead" or "There is a person talking on the right." It can also analyze natural sounds contained in landscape images and provide the user with information such as "There is a river flowing on the left" or "You can hear birds chirping." Furthermore, it can analyze audio information contained in landscape images in real time and provide information to the user immediately. As a result, by analyzing audio information contained in landscape images, more detailed information can be provided to visually impaired people.

[0101] The identification unit can estimate the user's emotions and adjust the way the identification results are presented based on the estimated emotions. For example, if the user is relaxed, the identification unit can provide detailed identification results. If the user is in a hurry, the identification unit can provide concise identification results that get straight to the point. Furthermore, if the user is excited, the identification unit can provide visually stimulating identification results. By adjusting the way the identification results are presented according to the user's emotions, it becomes possible to provide more appropriate information.

[0102] The voice output unit can refer to the user's past voice command history and provide voice output tailored to the user's preferences. For example, if the user has frequently used a particular voice command in the past, the voice output unit can provide information based on that command. Also, if the user has preferred a particular voice output in the past, the voice output unit can prioritize using that voice output. Furthermore, by analyzing the user's past voice command history, it is possible to provide voice output tailored to the user's preferences. This allows for more appropriate voice output by referring to the user's past voice command history.

[0103] The image acquisition unit can estimate the user's emotions and adjust the range of landscape images acquired based on those emotions. For example, if the user is excited, the unit can acquire a wide-angle landscape image to provide detailed information. If the user is relaxed, the unit can acquire a narrow-angle landscape image to provide calm information. Furthermore, if the user is tired, the unit can acquire only the minimum necessary landscape images to provide concise information. By adjusting the range of landscape images acquired according to the user's emotions, more appropriate information can be provided.

[0104] The analysis unit can analyze temperature information contained in landscape images and provide users with information about the temperature. For example, by analyzing the temperature information contained in a landscape image, it can provide users with voice messages such as "The area in front is hot" or "The area on the right is cool." It can also analyze the temperature information contained in landscape images in real time and provide users with immediate information about the temperature. Furthermore, it can analyze the temperature information contained in landscape images and prompt users to take appropriate action. In this way, by analyzing the temperature information contained in landscape images, more detailed information can be provided to visually impaired people.

[0105] The voice output unit can estimate the user's emotions and adjust the tone of the voice output based on those emotions. For example, if the user is nervous, the voice output unit can provide guidance in a calm tone. If the user is relaxed, the voice output unit can provide guidance in a bright tone. Furthermore, if the user is in a hurry, the voice output unit can provide guidance in a quick and concise tone. By adjusting the tone of the voice output according to the user's emotions, it becomes possible to provide more appropriate information.

[0106] The identification unit can refer to the user's past identification history and provide identification results tailored to the user's preferences. For example, if the user has previously preferred the color or freshness of a particular food item, the identification unit can prioritize providing that information. Similarly, if the user has previously preferred a particular clothing design, the identification unit can prioritize providing that information. Furthermore, by analyzing the user's past identification history, it is possible to provide identification results tailored to the user's preferences. This allows for the provision of more appropriate identification results by referring to the user's past identification history.

[0107] The analysis unit can analyze humidity information contained in landscape images and provide users with information about humidity. For example, by analyzing the humidity information contained in landscape images, it can provide users with information such as "The area in front has high humidity" or "The area on the right has low humidity" via voice. It can also analyze the humidity information contained in landscape images in real time and provide users with information about humidity immediately. Furthermore, it can analyze the humidity information contained in landscape images and prompt users to take appropriate action. In this way, by analyzing the humidity information contained in landscape images, more detailed information can be provided to visually impaired people.

[0108] The identification unit can estimate the user's emotions and determine the priority of identification based on those emotions. For example, if the user is excited, it can prioritize identifying visually stimulating information. If the user is relaxed, it can prioritize identifying calming information. Furthermore, if the user is in a hurry, it can prioritize identifying important information. By determining the priority of identification according to the user's emotions, it becomes possible to provide more appropriate information.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The acquisition unit acquires landscape images. The acquisition unit acquires landscape images using, for example, a camera mounted on smart glasses. The acquisition unit can acquire still images and videos. The acquisition unit, for example, uses the smart glasses' camera to acquire high-resolution images and transmits them to the AI ​​in real time. The acquisition unit can adjust the resolution and frame rate of the landscape images. Step 2: The analysis unit analyzes the landscape image acquired by the acquisition unit. The analysis unit identifies objects, colors, backgrounds, etc., contained in the landscape image using, for example, an image recognition algorithm. The analysis unit uses, for example, AI to identify buildings, road signs, plants, etc., from the landscape image and analyzes that information. The analysis unit may use deep learning technology to improve the accuracy of image recognition. Step 3: The audio output unit outputs the information analyzed by the analysis unit as audio. The audio output unit outputs the analysis results as audio, for example, using speech synthesis technology. The audio output unit can output audio using bone conduction earphones. The audio output unit can, for example, have the AI ​​communicate information such as, "There is a building ahead. There is a road sign on the right." The audio output unit can adjust the type and volume of the audio. Step 4: The identification unit identifies things like the color and freshness of food, and the design of clothing in everyday life. For example, the AI ​​in the identification unit can identify the color and freshness of food and provide advice. For example, the AI ​​in the identification unit can verbally convey information such as, "This apple is red and fresh." The identification unit can also identify the design of clothing and provide advice. For example, the AI ​​in the identification unit can verbally convey information such as, "This clothing is blue and has a simple design."

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

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the acquisition unit, analysis unit, audio output unit, and identification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires a landscape image using the camera 42 of the smart device 14 and transmits it to the identification processing unit 290 of the data processing device 12. The analysis unit is implemented in the identification processing unit 290 of the data processing device 12 and identifies objects, colors, backgrounds, etc., contained in the landscape image. The audio output unit is implemented in the control unit 46A of the smart device 14 and outputs the analysis results as audio using bone conduction earphones. The identification unit is implemented in the identification processing unit 290 of the data processing device 12 and identifies the color and freshness of food, the design of clothing, etc., and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0116] As shown in Figure 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.

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

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0130] Each of the multiple elements described above, including the acquisition unit, analysis unit, audio output unit, and identification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires a landscape image using the camera 42 of the smart glasses 214 and transmits it to the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies objects, colors, backgrounds, etc., contained in the landscape image. The audio output unit is implemented in the control unit 46A of the smart glasses 214 and outputs the analysis results as audio using bone conduction earphones. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies the color and freshness of food, the design of clothing, etc., and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0146] Each of the multiple elements described above, including the acquisition unit, analysis unit, audio output unit, and identification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires a landscape image using the camera 42 of the headset terminal 314 and transmits it to the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies objects, colors, backgrounds, etc., contained in the landscape image. The audio output unit is implemented in the control unit 46A of the headset terminal 314 and outputs the analysis results as audio using bone conduction earphones. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies the color and freshness of food, the design of clothing, etc., and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0148] As shown in Figure 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.

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

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0163] Each of the multiple elements described above, including the acquisition unit, analysis unit, audio output unit, and identification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires a landscape image using the camera 42 of the robot 414 and transmits it to the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies objects, colors, backgrounds, etc., contained in the landscape image. The audio output unit is implemented in the control unit 46A of the robot 414 and outputs the analysis results as audio using bone conduction earphones. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies the color and freshness of food, the design of clothing, etc., and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) An acquisition unit that acquires landscape images, An analysis unit analyzes the landscape image acquired by the acquisition unit, A voice output unit that outputs the information analyzed by the aforementioned analysis unit as sound, It includes an identification unit that identifies the color and freshness of food, the design of clothing, etc., in daily life. A system characterized by the following features. (Note 2) The aforementioned audio output unit is Outputting sound using bone conduction earphones The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned identification unit is Identifying the color and freshness of food and providing advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned identification unit is Identify clothing designs and provide advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, Acquiring landscape images using smart glasses The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Identifying objects, colors, backgrounds, etc., contained in landscape images. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of landscape image acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring landscape images, the system selects the optimal acquisition method by referring to the user's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring landscape images, filtering is performed based on the user's current location information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the user's emotions and determines the priority of landscape images to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring landscape images, the system prioritizes acquiring images that are highly relevant to the user's surrounding sound environment, taking into account the user's surrounding sound environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring landscape images, the system analyzes the user's social media activity and retrieves relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing landscape images, adjust the level of detail of the analysis based on the importance of the image. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing landscape images, different analysis algorithms are applied depending on the image category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing landscape images, the priority of the analysis is determined based on when the image was taken. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing landscape images, the order of analysis is adjusted based on the relevance of the images. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned audio output unit is It estimates the user's emotions and adjusts the way the voice output is expressed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned audio output unit is When outputting information via audio, the level of detail in the audio is adjusted based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned audio output unit is When outputting information as audio, different audio output algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned audio output unit is It estimates the user's emotions and determines the priority of voice output based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned audio output unit is When outputting information via audio, the priority of the audio is determined based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned audio output unit is When outputting information via audio, the order of the audio is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned identification unit is It estimates the user's emotions and adjusts the way identification is represented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned identification unit is When identifying the color and freshness of food, or the design of clothing, the level of detail of the identification is adjusted based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned identification unit is When identifying the color and freshness of food, or the design of clothing, different identification algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned identification unit is It estimates the user's emotions and determines the priority of identification based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned identification unit is When identifying the color and freshness of food, or the design of clothing, the priority of identification is determined based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned identification unit is When identifying the color and freshness of food, or the design of clothing, the order of identification is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires landscape images, An analysis unit analyzes the landscape image acquired by the acquisition unit, A voice output unit that outputs the information analyzed by the aforementioned analysis unit as sound, It includes an identification unit that identifies the color and freshness of food, the design of clothing, etc., in daily life. A system characterized by the following features.

2. The aforementioned audio output unit is Outputting sound using bone conduction earphones The system according to feature 1.

3. The aforementioned identification unit is Identifying the color and freshness of food and providing advice. The system according to feature 1.

4. The aforementioned identification unit is Identify clothing designs and provide advice. The system according to feature 1.

5. The acquisition unit is, Acquiring landscape images using smart glasses The system according to feature 1.

6. The aforementioned analysis unit, Identifying objects, colors, backgrounds, etc., contained in landscape images. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of landscape image acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, When acquiring landscape images, the system selects the optimal acquisition method by referring to the user's past movement history. The system according to feature 1.

9. The acquisition unit is, When acquiring landscape images, filtering is performed based on the user's current location information. The system according to feature 1.

10. The acquisition unit is, The system estimates the user's emotions and determines the priority of landscape images to acquire based on the estimated user emotions. The system according to feature 1.

Images