System

Abstract

An object of a system according to an embodiment is to identify and prevent a risk factor of an accident in a house in advance.SOLUTION: A system includes an input unit, an analysis unit, an identification unit, and a notification unit. The input unit inputs an image of a place of interest. The analysis unit analyzes the image input by the input unit. The identification unit identifies a risk factor of an accident from the image analyzed by the analysis unit. The notification unit notifies the user of the risk factor identified by the identification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to identify and prevent risk factors for accidents within the home in advance.

[0005] The system according to the embodiment aims to identify risk factors for accidents in the home in advance and prevent them. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, an identification unit, and a notification unit. The input unit inputs an image of a location of concern. The analysis unit analyzes the image input by the input unit. The identification unit identifies risk factors for an accident from the image analyzed by the analysis unit. The notification unit notifies the user of the risk factors identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify and prevent risk factors for accidents in the home in advance. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In an embodiment of the present invention, an accident prevention system allows a user to input an image of a location of concern. A generation AI analyzes the image, identifies risk factors for accidents, and notifies the user. In the accident prevention system, a user takes an image of a location of concern and inputs it into the system. For example, the user may take an image of a location where accidents are likely to occur, such as a staircase or a kitchen. The image is input into the system. The accident prevention system then analyzes the input image using a generation AI that has previously learned from a near-miss database. The generation AI identifies factors in the image that may lead to an accident. For example, it identifies factors that increase the risk of an accident, such as uneven stairs or a kitchen fire source. The risk factors for accidents identified by the generation AI are notified to the user. For example, specific risk factors, such as large staircase steps or an unprotected kitchen fire source, are notified to the user. This allows the user to understand the risk of an accident in advance and take measures. The accident prevention system aims to eliminate near-misses within the home. Users can easily identify the risk of an accident by simply taking an image of a location of concern. This improves safety within the home and prevents accidents from occurring. This allows the accident prevention system to identify and notify risk factors for accidents simply by having the user input an image of a place of concern. For example, if a user simply takes an image of a place of concern and inputs it into the system, the generation AI can analyze the image, identify risk factors for accidents, and notify the user. This allows users to understand the risk of accidents in advance and take measures. This improves safety within the home and prevents accidents from occurring.

[0029] An accident prevention system according to an embodiment includes an input unit, an analysis unit, an identification unit, and a notification unit. The input unit inputs images of places of interest to the user. Examples of images of places of interest to the user include, but are not limited to, a home, a workplace, and a public place. The input unit inputs images taken with, for example, a smartphone or a digital camera to the system. The input unit can also input images taken by the user to the system in real time. For example, the input unit instantly transmits images taken by the user to the system using the camera function of a smartphone. The analysis unit analyzes the images input by the input unit using a generation AI. The analysis unit identifies factors in the images that may lead to an accident, for example, using an image recognition algorithm. For example, the analysis unit identifies accident risk factors in the images using a generation AI that has learned from a near-miss database. The generation AI uses a deep learning model to learn past accident data and hazard prediction data and identify risk factors in the images with high accuracy. The identification unit identifies accident risk factors from the images analyzed by the generation AI. For example, the identification unit identifies specific accident factors based on the risk factors identified by the generation AI. For example, the identification unit identifies factors that pose a high risk of accidents, such as uneven stairs or a kitchen fire source. The notification unit notifies the user of the risk factors identified by the identification unit. The notification unit notifies the user of the risk factors by, for example, voice notification, text notification, alert, or other methods. For example, the notification unit notifies the user of the risk factors using a push notification function of a smartphone. The notification unit can also notify the user of the risk factors by voice using a voice assistant. As a result, the accident prevention system according to the embodiment can identify and notify risk factors for accidents simply by the user inputting an image of a place of concern. This allows the user to understand the risk of accidents in advance and take measures. This improves safety within the home and prevents accidents from occurring.

[0030] The analysis unit can analyze images using a generation AI that has learned a near-miss database. The near-miss database includes, for example, past accident data and hazard prediction data, but is not limited to these examples. The analysis unit learns the near-miss database using, for example, a deep learning model. For example, the analysis unit trains the generation AI using past accident data. The analysis unit can also train the generation AI using hazard prediction data. For example, the analysis unit inputs past accident data and hazard prediction data to the generation AI, improving the generation AI's ability to identify risk factors. This improves the accuracy of image analysis by using the generation AI that has learned the near-miss database. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input images to the generation AI that has learned the near-miss database, and the generation AI can analyze the images and identify risk factors.

[0031] The identification unit can use the generation AI to identify specific accident factors in the image. Specific accident factors include, but are not limited to, risks of falling and fire. For example, the identification unit can use the generation AI to identify risks of falling in the image. For example, the identification unit can cause the generation AI to identify slippery floors and obstacles in the image. The identification unit can also use the generation AI to identify risks of fire in the image. For example, the identification unit can cause the generation AI to identify sources of fire and flammable materials in the image. The identification unit can also use the generation AI to identify other accident risk factors in the image. For example, the identification unit can cause the generation AI to identify defects in electrical wiring and risks of gas leaks in the image. This improves the accuracy of identifying accident risk factors by using the generation AI. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or without AI. For example, the identification unit can input an image to the generation AI, which can then identify specific accident factors in the image.

[0032] The notification unit can notify the user of the identified risk factors. Notification methods include, but are not limited to, voice notification, text notification, and alerts. The notification unit can notify the user of the risk factors using, for example, a push notification function of a smartphone. For example, the notification unit can display the risk factors on the smartphone screen. The notification unit can also notify the user of the risk factors by voice using a voice assistant. For example, the notification unit can have the voice assistant read out the risk factors. The notification unit can also notify the user of the risk factors using an alert function. For example, the notification unit can notify the user of the risk factors by sounding an alert sound. By notifying the user of the identified risk factors, the user can take measures in advance. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without AI. For example, the notification unit can notify the user of the risk factors identified by the generation AI.

[0033] The input unit can input images taken by a user into the system. Examples of images taken by a user include, but are not limited to, images taken with a smartphone or a digital camera. The input unit inputs images taken by a user into the system using, for example, a smartphone's camera function. For example, the input unit immediately transmits images taken with the smartphone's camera to the system. The input unit can also input images taken with a digital camera into the system. For example, the input unit imports images taken with a digital camera into a computer and inputs them into the system. In this way, inputting images taken by a user into the system can provide information for identifying risk factors for accidents. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input images taken by a user into a generation AI, which can analyze the images and identify risk factors.

[0034] The analysis unit can identify specific accident risk factors, such as stair steps or kitchen fire sources. Specific accident risk factors include, but are not limited to, stair steps and kitchen fire sources. The analysis unit, for example, uses a generation AI to identify stair steps. For example, the analysis unit uses the generation AI to identify stair steps in an image and evaluate their height and shape. The analysis unit can also use the generation AI to identify kitchen fire sources. For example, the analysis unit uses the generation AI to identify gas stoves or electric stoves in an image and evaluate the risk associated with them. The analysis unit can also use the generation AI to identify other specific accident risk factors. For example, the analysis unit uses the generation AI to identify slippery floors or obstacles in an image and evaluate the risk associated with them. By identifying factors that increase the risk of accidents, the user can take specific measures. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input an image into the generation AI, which then identifies specific accident risk factors.

[0035] The input unit can analyze the user's past input history and select an appropriate input method. For example, the input unit preferentially suggests input methods (such as voice, text, or image) that the user has frequently used in the past. For example, the input unit analyzes the user's past input history and preferentially suggests voice input for a user who prefers voice input. The input unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, the input unit can prompt the user to input an image during a specific time period based on the user's past image input history during that time period. The input unit can also prompt the user to input images of similar locations based on images of locations the user has previously input. For example, the input unit can analyze images of locations the user has previously input and suggest image input of similar locations. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's past input history data into a generation AI, which can select the optimal input method.

[0036] When inputting images, the input unit can filter images based on specific criteria based on the user's current living situation or areas of interest. For example, if the user is a household with children, the input unit prioritizes image input of places where children are likely to touch. For example, the input unit analyzes the user's living situation and suggests image input of places where children are likely to touch. Furthermore, if the user is elderly, the input unit can prioritize image input of places where falls are likely to occur. For example, the input unit analyzes the user's age and living situation and suggests image input of places where there is a high risk of falls. Furthermore, if the user has a pet, the input unit can also prompt the user to input images of places where the pet may be in danger. For example, the input unit analyzes the type of pet and living situation of the user and suggests image input of places where the pet may be in danger. Thus, by filtering based on the user's living situation and areas of interest, more relevant images can be input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's living situation data to a generation AI, which then performs filtering.

[0037] When inputting an image, the input unit can select an appropriate input means according to the user's input method. For example, if the user prefers voice input, the input unit can preferentially provide voice image input instructions. For example, the input unit can analyze the user's past input history and provide voice input instructions to users who prefer voice input. Furthermore, if the user prefers text input, the input unit can preferentially provide text image input instructions. For example, the input unit can analyze the user's past input history and provide text input instructions to users who prefer text input. Furthermore, if the user prefers image input, the input unit can preferentially provide a camera function. For example, the input unit can analyze the user's past input history and provide a camera function to users who prefer image input. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without AI. For example, the input unit can input the user's input method data to a generation AI, which can select an appropriate input means.

[0038] When inputting images, the input unit can prioritize inputting highly relevant images based on the user's geographical location information. For example, when the user is at home, the input unit prioritizes prompting the user to input images of dangerous locations within the home. For example, the input unit analyzes the user's geographical location information and suggests inputting images of dangerous locations within the home. Furthermore, when the user is away from home, the input unit can prioritize prompting the user to input images of dangerous locations at the user's destination. For example, the input unit analyzes the user's geographical location information and suggests inputting images of dangerous locations at the user's destination. Furthermore, when the user is in a specific area, the input unit can prioritize prompting the user to input images of dangerous locations in that area. For example, the input unit analyzes the user's geographical location information and suggests inputting images of dangerous locations in that area. In this way, highly relevant images can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information data to a generation AI, which can then prioritize inputting highly relevant images.

[0039] The input unit can analyze the user's social media activity when inputting an image and input related images. The input unit, for example, prompts the user to input images of places where the user has checked in on social media. For example, the input unit analyzes the user's social media activity and suggests inputting images of checked-in places. The input unit can also analyze the content of the user's social media posts and prompt the user to input images of related places. For example, the input unit analyzes the content of the user's social media posts and suggests inputting images of related places. The input unit can also prompt the user to input images of related places based on the activity of the user's friends on social media. For example, the input unit analyzes the social media activity of the user's friends and suggests inputting images of related places. In this way, related images can be input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media activity data to the generation AI, which can input related images.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting an image. The input unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, the input unit analyzes the user's past feedback and suggests voice input for a user who prefers voice input. The input unit can also encourage the user to input images of similar locations by referring to feedback on images previously input by the user. For example, the input unit analyzes the user's past feedback and suggests image input of similar locations. The input unit can also preferentially suggest a specific input method based on the user's past feedback. For example, the input unit analyzes the user's past feedback and suggests a specific input method. This makes it possible to provide an optimal input method by reflecting the user's past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback data to a generation AI, which can customize the input method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, the analysis unit performs a detailed analysis on images with high importance. For example, the analysis unit uses a generation AI to analyze images with high importance in detail. The analysis unit can also perform a simplified analysis on images with low importance. For example, the analysis unit uses a generation AI to analyze images with low importance in a simplified manner. The analysis unit can also determine the priority of the analysis according to the importance of the image. For example, the analysis unit uses a generation AI to analyze images with high importance preferentially. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the image. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or may be performed without using an AI. For example, the analysis unit can input image importance data to the generation AI, which can adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the image. For example, the analysis unit applies a step analysis algorithm to an image of stairs. For example, the analysis unit uses a generation AI to analyze the steps of the stairs. The analysis unit can also apply a fire source analysis algorithm to an image of a kitchen. For example, the analysis unit uses a generation AI to analyze the source of a fire in the kitchen. The analysis unit can also apply a slipperiness analysis algorithm to an image of a bathroom. For example, the analysis unit uses a generation AI to analyze the slipperiness of the bathroom. In this way, by applying different analysis algorithms depending on the category of the image, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image category data to a generation AI, which can then apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the analysis accuracy of similar images based on the results of images previously analyzed by the user. For example, the analysis unit uses a generation AI to refer to the user's past analysis results and improve the analysis accuracy of similar images. The analysis unit can also prioritize analysis of specific risk factors based on the user's past analysis results. For example, the analysis unit uses a generation AI to refer to the user's past analysis results and prioritize analysis of specific risk factors. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit uses a generation AI to refer to the user's past analysis results and adjust the analysis algorithm. This improves the analysis accuracy by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can improve the analysis accuracy.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time the image was taken. For example, the analysis unit prioritizes analysis of recently taken images. For example, the analysis unit uses a generation AI to prioritize analysis of recently taken images. The analysis unit can also lower the priority of analysis for older images. For example, the analysis unit uses a generation AI to lower the priority of analysis for older images. The analysis unit can also adjust the level of detail of analysis depending on the time the image was taken. For example, the analysis unit uses a generation AI to adjust the level of detail of analysis depending on the time the image was taken. This enables efficient analysis by determining the priority of analysis based on the time the image was taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image taking time data into the generation AI, and the generation AI can determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. The analysis unit, for example, prioritizes analysis of highly relevant images. For example, the analysis unit uses a generation AI to prioritize analysis of highly relevant images. The analysis unit can also postpone the order of analysis of less relevant images. For example, the analysis unit uses a generation AI to postpone the order of analysis of less relevant images. The analysis unit can also adjust the level of detail of the analysis based on the relevance of the images. For example, the analysis unit uses a generation AI to adjust the level of detail of the analysis based on the relevance of the images. This enables efficient analysis by adjusting the order of analysis based on the relevance of the images. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image relevance data to the generation AI, which can adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology. For example, the analysis unit uses a generation AI to use detailed technical terminology if the user has technical expertise. The analysis unit can also display the analysis results in simple language if the user does not have technical expertise. For example, the analysis unit uses a generation AI to display the analysis results in simple language if the user does not have technical expertise. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit uses a generation AI to adjust the way the analysis results are presented according to the user's level of expertise. This allows the analysis results to be better understood by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0047] During identification, the identification unit can improve the accuracy of identification based on the interrelationships between images. The identification unit, for example, compares multiple images and identifies common risk factors. For example, the identification unit uses a generation AI to compare multiple images and identify common risk factors. The identification unit can also analyze the interrelationships between images and improve the accuracy of identifying risk factors. For example, the identification unit uses a generation AI to analyze the interrelationships between images and improve the accuracy of identifying risk factors. The identification unit can also determine a priority order for identification taking into account the interrelationships between images. For example, the identification unit uses a generation AI to determine a priority order for identification taking into account the interrelationships between images. In this way, the accuracy of identification is improved by taking into account the interrelationships between images. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit inputs image interrelation data into the generation AI, which can improve the accuracy of identification.

[0048] The identification unit can perform identification by taking into consideration attribute information of the photographer of the image. For example, if the photographer is elderly, the identification unit prioritizes identifying factors that increase the risk of falling. For example, the identification unit uses a generation AI to prioritize identifying factors that increase the risk of falling if the photographer is elderly. Furthermore, if the photographer is a child, the identification unit can prioritize identifying risk factors in places that are easily touched by children. For example, the identification unit uses a generation AI to prioritize identifying risk factors in places that are easily touched by children if the photographer is a child. Furthermore, the identification unit can also identify factors that may put the pet at risk if the photographer has a pet. For example, the identification unit uses a generation AI to identify factors that may put the pet at risk if the photographer has a pet. This allows for more appropriate risk factors to be identified by taking into consideration the attribute information of the photographer of the image. Some or all of the above-described processing by the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input attribute information data of the photographer of the image into the generation AI, which can then perform identification.

[0049] During identification, the identification unit can assign specific weights based on the frequency of image capture. For example, the identification unit prioritizes identifying risk factors in locations that are frequently photographed. For example, the identification unit uses a generation AI to prioritize identifying risk factors in locations that are frequently photographed. The identification unit can also lower the priority of identifying locations that are less frequently photographed. For example, the identification unit uses a generation AI to lower the priority of identifying locations that are less frequently photographed. The identification unit can also adjust the specific weights according to the frequency of image capture. For example, the identification unit uses a generation AI to adjust the specific weights according to the frequency of image capture. In this way, by assigning specific weights based on the frequency of image capture, important risk factors can be prioritized. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input image capture frequency data to the generation AI, which can then assign specific weights.

[0050] The identification unit can perform identification by taking into account the geographic distribution of the images. For example, the identification unit analyzes the geographic distribution of locations where the images were taken and identifies risk factors. For example, the identification unit uses a generation AI to analyze the geographic distribution of locations where the images were taken and identifies risk factors. The identification unit can also compare images from geographically close locations and identify common risk factors. For example, the identification unit uses a generation AI to compare images from geographically close locations and identify common risk factors. The identification unit can also determine a priority for identification based on the geographic distribution. For example, the identification unit uses a generation AI to determine a priority for identification based on the geographic distribution. This improves the accuracy of identifying risk factors by taking the geographic distribution of the images into consideration. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input geographic distribution data of the images to the generation AI, and the generation AI can perform identification.

[0051] The identification unit can improve the accuracy of identification by referring to literature related to the image during identification. The identification unit, for example, refers to literature related to the image to improve the accuracy of identifying risk factors. For example, the identification unit uses a generation AI to refer to literature related to the image to improve the accuracy of identifying risk factors. The identification unit can also identify risk factors based on information obtained from related literature. For example, the identification unit uses a generation AI to identify risk factors based on information obtained from related literature. The identification unit can also analyze literature related to the image and determine the priority of identification. For example, the identification unit uses a generation AI to analyze literature related to the image and determine the priority of identification. As a result, the accuracy of identifying risk factors is improved by referring to literature related to the image. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input literature data related to the image into the generation AI, which can improve the accuracy of identification.

[0052] The identification unit can perform identification taking into consideration the market value of the image. For example, the identification unit prioritizes identifying risk factors in locations with high market value. For example, the identification unit uses a generation AI to prioritize identifying risk factors in locations with high market value. The identification unit can also lower the priority of identification for locations with low market value. For example, the identification unit uses a generation AI to lower the priority of identification for locations with low market value. The identification unit can also adjust the weighting of identification according to the market value of the image. For example, the identification unit uses a generation AI to adjust the weighting of identification according to the market value of the image. This allows important risk factors to be prioritized by taking the market value of the image into consideration. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input market value data of the image into the generation AI, and the generation AI can perform identification.

[0053] The notification unit can select the optimal notification method by referring to the user's past notification history when providing a notification. The notification unit, for example, prioritizes providing a notification method (audio, text, etc.) that the user previously preferred. For example, the notification unit analyzes the user's past notification history and prioritizes providing audio notification to a user who prefers audio notification. The notification unit can also suggest the optimal notification method for a specific time period based on the user's past notification history. For example, the notification unit analyzes the user's past notification history and suggests the optimal notification method for a specific time period. The notification unit can also analyze the user's past notification history and select the most effective notification method. For example, the notification unit analyzes the user's past notification history and selects the most effective notification method. In this way, the optimal notification method can be provided by referring to the user's past notification history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history data into a generation AI, which can select the optimal notification method.

[0054] The notification unit can customize the notification content according to the user's current task when providing a notification. For example, when the user is working, the notification unit provides a concise and to-the-point notification. For example, the notification unit analyzes the user's current task and provides a concise and to-the-point notification when the user is working. The notification unit can also provide a detailed notification when the user is taking a break. For example, the notification unit analyzes the user's current task and provides a detailed notification when the user is taking a break. The notification unit can also prioritize audio notifications when the user is exercising. For example, the notification unit analyzes the user's current task and prioritizes audio notifications when the user is exercising. This improves the effectiveness of notifications by customizing the notification content according to the user's current task. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's current task data into a generation AI, which can customize the notification content.

[0055] The notification unit can select the optimal notification method by taking into consideration the user's device information when providing a notification. For example, if the user is using a smartphone, the notification unit prioritizes push notification. For example, the notification unit analyzes the user's device information and prioritizes push notification when the user is using a smartphone. Furthermore, if the user is using a tablet, the notification unit can provide a notification optimized for a large screen. For example, the notification unit analyzes the user's device information and provides a notification optimized for a large screen when the user is using a tablet. Furthermore, the notification unit can provide a concise and highly visible notification when the user is using a smartwatch. For example, the notification unit analyzes the user's device information and provides a concise and highly visible notification when the user is using a smartwatch. This makes it possible to provide the optimal notification method by taking into consideration the user's device information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's device information data into a generation AI, which can select the optimal notification method.

[0056] The notification unit can make the notification content multilingual according to the user's language setting when providing a notification. The notification unit, for example, automatically sets the notification language based on the language setting of the user's device. For example, the notification unit analyzes the language setting of the user's device and automatically sets the notification language. The notification unit can also provide a language switching function when the user uses multiple languages. For example, the notification unit analyzes the user's language setting and provides the language switching function when the user uses multiple languages. The notification unit can also provide notifications in a specific language when the user selects that language. For example, the notification unit analyzes the user's language setting and provides notifications in a specific language when the user selects that language. This makes the notification content multilingual according to the user's language setting, thereby improving understanding of the notification. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input the user's language setting data into a generation AI, which can then make the notification content multilingual.

[0057] At the time of notification, the notification unit can analyze the user's social media activity and provide related information. The notification unit can provide, for example, information about places where the user has checked in on social media. For example, the notification unit can analyze the user's social media activity and provide information about places where the user has checked in. The notification unit can also analyze the content of the user's social media posts and provide information about related risk factors. For example, the notification unit can analyze the content of the user's social media posts and provide information about related risk factors. The notification unit can also provide information about related places and events by referring to the activities of the user's friends on social media. For example, the notification unit can analyze the social media activity of the user's friends and provide information about related places and events. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's social media activity data to a generation AI, which can provide related information.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The analysis unit can learn the user's past behavioral patterns and improve the accuracy of identifying risk factors. For example, the analysis unit can learn what locations the user has experienced accidents in the past and prioritize identifying risk factors in similar locations. The analysis unit can also learn what measures the user has taken in the past and evaluate whether similar measures are effective. Furthermore, the analysis unit can automatically collect data necessary to identify risk factors based on the user's past behavioral patterns. In this way, by taking the user's past behavioral patterns into consideration, the accuracy of identifying risk factors can be improved and more effective measures can be taken.

[0060] The notification unit can monitor the user's current activity status in real time and notify the user of risk factors at the optimal timing. For example, if the user is driving, the notification unit can delay notification until the user finishes driving. Also, if the user is in a meeting, the notification unit can postpone notification until the meeting ends. Furthermore, if the user is relaxed, the notification unit can immediately notify the user and encourage the user to take measures against risk factors. In this way, by taking the user's current activity status into consideration, risk factors can be notified at the optimal timing, ensuring the user's safety.

[0061] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, the notification unit can prioritize the notification method (audio, text, etc.) that the user has previously preferred. The notification unit can also suggest the optimal notification method for a specific time period based on the user's past notification history. Furthermore, the notification unit can select the most effective notification method based on the user's past notification history and quickly notify the user of risk factors. This allows the optimal notification method to be provided by taking the user's past notification history into consideration, enabling a quick response to risk factors.

[0062] The identification unit can customize the risk factor identification method by reflecting the user's past feedback. For example, the identification unit can suggest an optimal identification method based on feedback provided by the user in the past. The identification unit can also prioritize identification of similar risk factors by referring to the user's past feedback. Furthermore, the identification unit can adjust the identification priority based on the user's past feedback. In this way, by reflecting the user's past feedback, an optimal identification method can be provided and the accuracy of risk factor identification can be improved.

[0063] The analysis unit can improve the accuracy of identifying risk factors based on the user's current geographic location information. For example, the analysis unit prioritizes analysis of specific risk factors in the user's current location. The analysis unit can also identify risk factors by referring to data on accidents that have occurred in similar locations in the past based on the user's geographic location information. Furthermore, the analysis unit can identify region-specific risk factors and propose countermeasures based on the user's geographic location information. In this way, by taking the user's current geographic location information into consideration, the accuracy of identifying risk factors can be improved, and more effective countermeasures can be taken.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The input unit inputs images of places of interest to the user. Images of places of interest to the user include within the home, workplace, public places, etc. The input unit inputs images taken with a smartphone or digital camera into the system. Images taken by the user can also be input to the system in real time. For example, the user can use the camera function of a smartphone to instantly send images taken by the user to the system. Step 2: The analysis unit uses the generation AI to analyze the image input by the input unit. The analysis unit uses an image recognition algorithm to identify factors in the image that could lead to an accident. For example, the generation AI, which has learned from a near-miss database, can be used to identify accident risk factors in the image. The generation AI uses a deep learning model to learn from past accident data and hazard prediction data, and can identify risk factors in the image with high accuracy. Step 3: The identification unit identifies risk factors for accidents from the images analyzed by the generation AI. Based on the risk factors identified by the generation AI, the identification unit identifies specific accident factors. For example, it identifies factors that pose a high risk of accidents, such as uneven stairs or the source of a fire in the kitchen. Step 4: The notification unit notifies the user of the risk factors identified by the identification unit. The notification unit notifies the user of the risk factors by voice notification, text notification, alert, or other methods. For example, the notification unit notifies the user of the risk factors by using a push notification function of a smartphone. Alternatively, the notification unit can notify the user of the risk factors by voice using a voice assistant.

[0066] (Example 2) In an embodiment of the present invention, an accident prevention system allows a user to input an image of a location of concern. A generation AI analyzes the image, identifies risk factors for accidents, and notifies the user. In the accident prevention system, a user takes an image of a location of concern and inputs it into the system. For example, the user may take an image of a location where accidents are likely to occur, such as a staircase or a kitchen. The image is input into the system. The accident prevention system then analyzes the input image using a generation AI that has previously learned from a near-miss database. The generation AI identifies factors in the image that may lead to an accident. For example, it identifies factors that increase the risk of an accident, such as uneven stairs or a kitchen fire source. The risk factors for accidents identified by the generation AI are notified to the user. For example, specific risk factors, such as large staircase steps or an unprotected kitchen fire source, are notified to the user. This allows the user to understand the risk of an accident in advance and take measures. The accident prevention system aims to eliminate near-misses within the home. Users can easily identify the risk of an accident by simply taking an image of a location of concern. This improves safety within the home and prevents accidents from occurring. This allows the accident prevention system to identify and notify risk factors for accidents simply by having the user input an image of a place of concern. For example, if a user simply takes an image of a place of concern and inputs it into the system, the generation AI can analyze the image, identify risk factors for accidents, and notify the user. This allows users to understand the risk of accidents in advance and take measures. This improves safety within the home and prevents accidents from occurring.

[0067] An accident prevention system according to an embodiment includes an input unit, an analysis unit, an identification unit, and a notification unit. The input unit inputs images of places of interest to the user. Examples of images of places of interest to the user include, but are not limited to, a home, a workplace, and a public place. The input unit inputs images taken with, for example, a smartphone or a digital camera to the system. The input unit can also input images taken by the user to the system in real time. For example, the input unit instantly transmits images taken by the user to the system using the camera function of a smartphone. The analysis unit analyzes the images input by the input unit using a generation AI. The analysis unit identifies factors in the images that may lead to an accident, for example, using an image recognition algorithm. For example, the analysis unit identifies accident risk factors in the images using a generation AI that has learned from a near-miss database. The generation AI uses a deep learning model to learn past accident data and hazard prediction data and identify risk factors in the images with high accuracy. The identification unit identifies accident risk factors from the images analyzed by the generation AI. For example, the identification unit identifies specific accident factors based on the risk factors identified by the generation AI. For example, the identification unit identifies factors that pose a high risk of accidents, such as uneven stairs or a kitchen fire source. The notification unit notifies the user of the risk factors identified by the identification unit. The notification unit notifies the user of the risk factors by, for example, voice notification, text notification, alert, or other methods. For example, the notification unit notifies the user of the risk factors using a push notification function of a smartphone. The notification unit can also notify the user of the risk factors by voice using a voice assistant. As a result, the accident prevention system according to the embodiment can identify and notify risk factors for accidents simply by the user inputting an image of a place of concern. This allows the user to understand the risk of accidents in advance and take measures. This improves safety within the home and prevents accidents from occurring.

[0068] The analysis unit can analyze images using a generation AI that has learned a near-miss database. The near-miss database includes, for example, past accident data and hazard prediction data, but is not limited to these examples. The analysis unit learns the near-miss database using, for example, a deep learning model. For example, the analysis unit trains the generation AI using past accident data. The analysis unit can also train the generation AI using hazard prediction data. For example, the analysis unit inputs past accident data and hazard prediction data to the generation AI, improving the generation AI's ability to identify risk factors. This improves the accuracy of image analysis by using the generation AI that has learned the near-miss database. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input images to the generation AI that has learned the near-miss database, and the generation AI can analyze the images and identify risk factors.

[0069] The identification unit can use the generation AI to identify specific accident factors in the image. Specific accident factors include, but are not limited to, risks of falling and fire. For example, the identification unit can use the generation AI to identify risks of falling in the image. For example, the identification unit can cause the generation AI to identify slippery floors and obstacles in the image. The identification unit can also use the generation AI to identify risks of fire in the image. For example, the identification unit can cause the generation AI to identify sources of fire and flammable materials in the image. The identification unit can also use the generation AI to identify other accident risk factors in the image. For example, the identification unit can cause the generation AI to identify defects in electrical wiring and risks of gas leaks in the image. This improves the accuracy of identifying accident risk factors by using the generation AI. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or without AI. For example, the identification unit can input an image to the generation AI, which can then identify specific accident factors in the image.

[0070] The notification unit can notify the user of the identified risk factors. Notification methods include, but are not limited to, voice notification, text notification, and alerts. The notification unit can notify the user of the risk factors using, for example, a push notification function of a smartphone. For example, the notification unit can display the risk factors on the smartphone screen. The notification unit can also notify the user of the risk factors by voice using a voice assistant. For example, the notification unit can have the voice assistant read out the risk factors. The notification unit can also notify the user of the risk factors using an alert function. For example, the notification unit can notify the user of the risk factors by sounding an alert sound. By notifying the user of the identified risk factors, the user can take measures in advance. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without AI. For example, the notification unit can notify the user of the risk factors identified by the generation AI.

[0071] The input unit can input images taken by a user into the system. Examples of images taken by a user include, but are not limited to, images taken with a smartphone or a digital camera. The input unit inputs images taken by a user into the system using, for example, a smartphone's camera function. For example, the input unit immediately transmits images taken with the smartphone's camera to the system. The input unit can also input images taken with a digital camera into the system. For example, the input unit imports images taken with a digital camera into a computer and inputs them into the system. In this way, inputting images taken by a user into the system can provide information for identifying risk factors for accidents. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input images taken by a user into a generation AI, which can analyze the images and identify risk factors.

[0072] The analysis unit can identify specific accident risk factors, such as stair steps or kitchen fire sources. Specific accident risk factors include, but are not limited to, stair steps and kitchen fire sources. The analysis unit, for example, uses a generation AI to identify stair steps. For example, the analysis unit uses the generation AI to identify stair steps in an image and evaluate their height and shape. The analysis unit can also use the generation AI to identify kitchen fire sources. For example, the analysis unit uses the generation AI to identify gas stoves or electric stoves in an image and evaluate the risk associated with them. The analysis unit can also use the generation AI to identify other specific accident risk factors. For example, the analysis unit uses the generation AI to identify slippery floors or obstacles in an image and evaluate the risk associated with them. By identifying factors that increase the risk of accidents, the user can take specific measures. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input an image into the generation AI, which then identifies specific accident risk factors.

[0073] The accident prevention system includes an input unit that estimates a user's emotions and adjusts the timing of image input based on the estimated user emotions. For example, if the user is feeling anxious, the input unit accelerates the timing at which the system prompts the user to input an image. For example, the input unit infers that the user is feeling anxious from the user's facial expression or voice and prompts the user to input an image immediately. The input unit can also delay the timing at which the system prompts the user to input an image if the user is relaxed. For example, the input unit infers that the user is relaxed from the user's facial expression or voice and postpones the image input. The input unit can also prompt the user to input an image immediately if the user is in a hurry. For example, the input unit infers that the user is in a hurry from the user's movements or voice and prompts the user to input an image quickly. This allows the image input timing to be adjusted according to the user's emotions, thereby enabling the image to be input at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input the user's facial expression data to the generation AI, which may then estimate the user's emotions and adjust the timing of image input.

[0074] The input unit can analyze the user's past input history and select an appropriate input method. For example, the input unit preferentially suggests input methods (such as voice, text, or image) that the user has frequently used in the past. For example, the input unit analyzes the user's past input history and preferentially suggests voice input for a user who prefers voice input. The input unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, the input unit can prompt the user to input an image during a specific time period based on the user's past image input history during that time period. The input unit can also prompt the user to input images of similar locations based on images of locations the user has previously input. For example, the input unit can analyze images of locations the user has previously input and suggest image input of similar locations. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's past input history data into a generation AI, which can select the optimal input method.

[0075] When inputting images, the input unit can filter images based on specific criteria based on the user's current living situation or areas of interest. For example, if the user is a household with children, the input unit prioritizes image input of places where children are likely to touch. For example, the input unit analyzes the user's living situation and suggests image input of places where children are likely to touch. Furthermore, if the user is elderly, the input unit can prioritize image input of places where falls are likely to occur. For example, the input unit analyzes the user's age and living situation and suggests image input of places where there is a high risk of falls. Furthermore, if the user has a pet, the input unit can also prompt the user to input images of places where the pet may be in danger. For example, the input unit analyzes the type of pet and living situation of the user and suggests image input of places where the pet may be in danger. Thus, by filtering based on the user's living situation and areas of interest, more relevant images can be input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's living situation data to a generation AI, which then performs filtering.

[0076] When inputting an image, the input unit can select an appropriate input means according to the user's input method. For example, if the user prefers voice input, the input unit can preferentially provide voice image input instructions. For example, the input unit can analyze the user's past input history and provide voice input instructions to users who prefer voice input. Furthermore, if the user prefers text input, the input unit can preferentially provide text image input instructions. For example, the input unit can analyze the user's past input history and provide text input instructions to users who prefer text input. Furthermore, if the user prefers image input, the input unit can preferentially provide a camera function. For example, the input unit can analyze the user's past input history and provide a camera function to users who prefer image input. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without AI. For example, the input unit can input the user's input method data to a generation AI, which can select an appropriate input means.

[0077] The input unit can estimate the user's emotions and determine the priority of images to be input based on the estimated user emotions. For example, if the user is feeling anxious, the input unit prioritizes image input of high-risk locations. For example, the input unit estimates that the user is feeling anxious from the user's facial expressions and voice, and suggests image input of high-risk locations. Furthermore, if the user is relaxed, the input unit can postpone image input of low-risk locations. For example, the input unit estimates that the user is relaxed from the user's facial expressions and voice, and postpones image input of low-risk locations. Furthermore, if the user is in a hurry, the input unit can prioritize image input of the most important locations. For example, the input unit estimates that the user is in a hurry from the user's movements and voice, and suggests image input of the most important locations. In this way, by determining the priority of images to be input according to the user's emotions, important images can be preferentially input. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input the user's facial expression data to the generation AI, which may then estimate the user's emotions and determine the priority of the images to be input.

[0078] When inputting images, the input unit can prioritize inputting highly relevant images based on the user's geographical location information. For example, when the user is at home, the input unit prioritizes prompting the user to input images of dangerous locations within the home. For example, the input unit analyzes the user's geographical location information and suggests inputting images of dangerous locations within the home. Furthermore, when the user is away from home, the input unit can prioritize prompting the user to input images of dangerous locations at the user's destination. For example, the input unit analyzes the user's geographical location information and suggests inputting images of dangerous locations at the user's destination. Furthermore, when the user is in a specific area, the input unit can prioritize prompting the user to input images of dangerous locations in that area. For example, the input unit analyzes the user's geographical location information and suggests inputting images of dangerous locations in that area. In this way, highly relevant images can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information data to a generation AI, which can then prioritize inputting highly relevant images.

[0079] The input unit can analyze the user's social media activity when inputting an image and input related images. The input unit, for example, prompts the user to input images of places where the user has checked in on social media. For example, the input unit analyzes the user's social media activity and suggests inputting images of checked-in places. The input unit can also analyze the content of the user's social media posts and prompt the user to input images of related places. For example, the input unit analyzes the content of the user's social media posts and suggests inputting images of related places. The input unit can also prompt the user to input images of related places based on the activity of the user's friends on social media. For example, the input unit analyzes the social media activity of the user's friends and suggests inputting images of related places. In this way, related images can be input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media activity data to the generation AI, which can input related images.

[0080] The input unit can customize the input method by reflecting the user's past feedback when inputting an image. The input unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, the input unit analyzes the user's past feedback and suggests voice input for a user who prefers voice input. The input unit can also encourage the user to input images of similar locations by referring to feedback on images previously input by the user. For example, the input unit analyzes the user's past feedback and suggests image input of similar locations. The input unit can also preferentially suggest a specific input method based on the user's past feedback. For example, the input unit analyzes the user's past feedback and suggests a specific input method. This makes it possible to provide an optimal input method by reflecting the user's past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback data to a generation AI, which can customize the input method.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit displays the analysis results in a simple and easy-to-understand manner. For example, the analysis unit infers that the user is feeling anxious from the user's facial expressions and voice and displays the analysis results concisely. The analysis unit can also display detailed analysis results if the user is relaxed. For example, the analysis unit infers that the user is relaxed from the user's facial expressions and voice and displays detailed analysis results. The analysis unit can also display analysis results that emphasize the main points if the user is in a hurry. For example, the analysis unit infers that the user is in a hurry from the user's movements and voice and displays analysis results that emphasize the main points. This allows the analysis results to be better understood by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI, which may infer the user's emotions and adjust the expression method of the analysis.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, the analysis unit performs a detailed analysis on images with high importance. For example, the analysis unit uses a generation AI to analyze images with high importance in detail. The analysis unit can also perform a simplified analysis on images with low importance. For example, the analysis unit uses a generation AI to analyze images with low importance in a simplified manner. The analysis unit can also determine the priority of the analysis according to the importance of the image. For example, the analysis unit uses a generation AI to analyze images with high importance preferentially. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the image. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or may be performed without using an AI. For example, the analysis unit can input image importance data to the generation AI, which can adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the image. For example, the analysis unit applies a step analysis algorithm to an image of stairs. For example, the analysis unit uses a generation AI to analyze the steps of the stairs. The analysis unit can also apply a fire source analysis algorithm to an image of a kitchen. For example, the analysis unit uses a generation AI to analyze the source of a fire in the kitchen. The analysis unit can also apply a slipperiness analysis algorithm to an image of a bathroom. For example, the analysis unit uses a generation AI to analyze the slipperiness of the bathroom. In this way, by applying different analysis algorithms depending on the category of the image, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image category data to a generation AI, which can then apply different analysis algorithms.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the analysis accuracy of similar images based on the results of images previously analyzed by the user. For example, the analysis unit uses a generation AI to refer to the user's past analysis results and improve the analysis accuracy of similar images. The analysis unit can also prioritize analysis of specific risk factors based on the user's past analysis results. For example, the analysis unit uses a generation AI to refer to the user's past analysis results and prioritize analysis of specific risk factors. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit uses a generation AI to refer to the user's past analysis results and adjust the analysis algorithm. This improves the analysis accuracy by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can improve the analysis accuracy.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit displays the analysis results in a short, concise manner. For example, the analysis unit infers that the user is feeling anxious from the user's facial expressions and voice, and displays the analysis results concisely. The analysis unit can also display detailed analysis results if the user is relaxed. For example, the analysis unit infers that the user is relaxed from the user's facial expressions and voice, and displays detailed analysis results. The analysis unit can also display concise analysis results if the user is in a hurry. For example, the analysis unit infers that the user is in a hurry from the user's movements and voice, and displays concise analysis results. This allows the length of the analysis to be adjusted according to the user's emotions, thereby deepening understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then estimate the user's emotions and adjust the length of the analysis.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time the image was taken. For example, the analysis unit prioritizes analysis of recently taken images. For example, the analysis unit uses a generation AI to prioritize analysis of recently taken images. The analysis unit can also lower the priority of analysis for older images. For example, the analysis unit uses a generation AI to lower the priority of analysis for older images. The analysis unit can also adjust the level of detail of analysis depending on the time the image was taken. For example, the analysis unit uses a generation AI to adjust the level of detail of analysis depending on the time the image was taken. This enables efficient analysis by determining the priority of analysis based on the time the image was taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image taking time data into the generation AI, and the generation AI can determine the priority of analysis.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. The analysis unit, for example, prioritizes analysis of highly relevant images. For example, the analysis unit uses a generation AI to prioritize analysis of highly relevant images. The analysis unit can also postpone the order of analysis of less relevant images. For example, the analysis unit uses a generation AI to postpone the order of analysis of less relevant images. The analysis unit can also adjust the level of detail of the analysis based on the relevance of the images. For example, the analysis unit uses a generation AI to adjust the level of detail of the analysis based on the relevance of the images. This enables efficient analysis by adjusting the order of analysis based on the relevance of the images. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image relevance data to the generation AI, which can adjust the order of analysis.

[0088] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology. For example, the analysis unit uses a generation AI to use detailed technical terminology if the user has technical expertise. The analysis unit can also display the analysis results in simple language if the user does not have technical expertise. For example, the analysis unit uses a generation AI to display the analysis results in simple language if the user does not have technical expertise. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit uses a generation AI to adjust the way the analysis results are presented according to the user's level of expertise. This allows the analysis results to be better understood by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0089] The identification unit can estimate the user's emotions and prioritize the identified risk factors based on the estimated user emotions. For example, if the user is feeling anxious, the identification unit prioritizes identifying serious risk factors. For example, the identification unit estimates that the user is feeling anxious from the user's facial expressions and voice, and prioritizes identifying serious risk factors. Furthermore, if the user is relaxed, the identification unit can also identify minor risk factors. For example, the identification unit estimates that the user is relaxed from the user's facial expressions and voice, and prioritizes identifying minor risk factors. Furthermore, if the user is in a hurry, the identification unit can also prioritize identifying the most important risk factors. For example, the identification unit estimates that the user is in a hurry from the user's movements and voice, and prioritizes identifying the most important risk factors. In this way, by prioritizing the risk factors according to the user's emotions, it is possible to prioritize identifying important risk factors. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit may input the user's facial expression data to the generation AI, which may then estimate the user's emotions and determine the priority of risk factors.

[0090] During identification, the identification unit can improve the accuracy of identification based on the interrelationships between images. The identification unit, for example, compares multiple images and identifies common risk factors. For example, the identification unit uses a generation AI to compare multiple images and identify common risk factors. The identification unit can also analyze the interrelationships between images and improve the accuracy of identifying risk factors. For example, the identification unit uses a generation AI to analyze the interrelationships between images and improve the accuracy of identifying risk factors. The identification unit can also determine a priority order for identification taking into account the interrelationships between images. For example, the identification unit uses a generation AI to determine a priority order for identification taking into account the interrelationships between images. In this way, the accuracy of identification is improved by taking into account the interrelationships between images. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit inputs image interrelation data into the generation AI, which can improve the accuracy of identification.

[0091] The identification unit can perform identification by taking into consideration attribute information of the photographer of the image. For example, if the photographer is elderly, the identification unit prioritizes identifying factors that increase the risk of falling. For example, the identification unit uses a generation AI to prioritize identifying factors that increase the risk of falling if the photographer is elderly. Furthermore, if the photographer is a child, the identification unit can prioritize identifying risk factors in places that are easily touched by children. For example, the identification unit uses a generation AI to prioritize identifying risk factors in places that are easily touched by children if the photographer is a child. Furthermore, the identification unit can also identify factors that may put the pet at risk if the photographer has a pet. For example, the identification unit uses a generation AI to identify factors that may put the pet at risk if the photographer has a pet. This allows for more appropriate risk factors to be identified by taking into consideration the attribute information of the photographer of the image. Some or all of the above-described processing by the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input attribute information data of the photographer of the image into the generation AI, which can then perform identification.

[0092] During identification, the identification unit can assign specific weights based on the frequency of image capture. For example, the identification unit prioritizes identifying risk factors in locations that are frequently photographed. For example, the identification unit uses a generation AI to prioritize identifying risk factors in locations that are frequently photographed. The identification unit can also lower the priority of identifying locations that are less frequently photographed. For example, the identification unit uses a generation AI to lower the priority of identifying locations that are less frequently photographed. The identification unit can also adjust the specific weights according to the frequency of image capture. For example, the identification unit uses a generation AI to adjust the specific weights according to the frequency of image capture. In this way, by assigning specific weights based on the frequency of image capture, important risk factors can be prioritized. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input image capture frequency data to the generation AI, which can then assign specific weights.

[0093] The identification unit can estimate the user's emotions and adjust the display method of the identified risk factors based on the estimated user emotions. For example, if the user is feeling anxious, the identification unit displays the risk factors in a simple and easy-to-understand manner. For example, the identification unit infers that the user is feeling anxious from the user's facial expressions and voice and displays the risk factors concisely. The identification unit can also display detailed risk factors if the user is relaxed. For example, the identification unit infers that the user is relaxed from the user's facial expressions and voice and displays detailed risk factors. The identification unit can also display risk factors that are summarized briefly if the user is in a hurry. For example, the identification unit infers that the user is in a hurry from the user's movements and voice and displays risk factors that are summarized briefly. This allows the user to better understand the risk factors by adjusting the display method of the risk factors according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit may input the user's facial expression data to the generation AI, which may estimate the user's emotions and adjust the display method of risk factors.

[0094] The identification unit can perform identification by taking into account the geographic distribution of the images. For example, the identification unit analyzes the geographic distribution of locations where the images were taken and identifies risk factors. For example, the identification unit uses a generation AI to analyze the geographic distribution of locations where the images were taken and identifies risk factors. The identification unit can also compare images from geographically close locations and identify common risk factors. For example, the identification unit uses a generation AI to compare images from geographically close locations and identify common risk factors. The identification unit can also determine a priority for identification based on the geographic distribution. For example, the identification unit uses a generation AI to determine a priority for identification based on the geographic distribution. This improves the accuracy of identifying risk factors by taking the geographic distribution of the images into consideration. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input geographic distribution data of the images to the generation AI, and the generation AI can perform identification.

[0095] The identification unit can improve the accuracy of identification by referring to literature related to the image during identification. The identification unit, for example, refers to literature related to the image to improve the accuracy of identifying risk factors. For example, the identification unit uses a generation AI to refer to literature related to the image to improve the accuracy of identifying risk factors. The identification unit can also identify risk factors based on information obtained from related literature. For example, the identification unit uses a generation AI to identify risk factors based on information obtained from related literature. The identification unit can also analyze literature related to the image and determine the priority of identification. For example, the identification unit uses a generation AI to analyze literature related to the image and determine the priority of identification. As a result, the accuracy of identifying risk factors is improved by referring to literature related to the image. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input literature data related to the image into the generation AI, which can improve the accuracy of identification.

[0096] The identification unit can perform identification taking into consideration the market value of the image. For example, the identification unit prioritizes identifying risk factors in locations with high market value. For example, the identification unit uses a generation AI to prioritize identifying risk factors in locations with high market value. The identification unit can also lower the priority of identification for locations with low market value. For example, the identification unit uses a generation AI to lower the priority of identification for locations with low market value. The identification unit can also adjust the weighting of identification according to the market value of the image. For example, the identification unit uses a generation AI to adjust the weighting of identification according to the market value of the image. This allows important risk factors to be prioritized by taking the market value of the image into consideration. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input market value data of the image into the generation AI, and the generation AI can perform identification.

[0097] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit can provide a notification in a calm tone. For example, the notification unit can estimate that the user is feeling anxious from the user's facial expression and voice and provide a notification in a calm tone. The notification unit can also provide a notification in a bright tone if the user is relaxed. For example, the notification unit can estimate that the user is relaxed from the user's facial expression and voice and provide a notification in a bright tone. The notification unit can also provide a quick and concise notification if the user is in a hurry. For example, the notification unit can estimate that the user is in a hurry from the user's movements and voice and provide a quick and concise notification. This improves the effectiveness of the notification by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI or without AI. For example, the notification unit can input the user's facial expression data into the generation AI, which can then estimate the user's emotions and adjust the notification method.

[0098] The notification unit can select the optimal notification method by referring to the user's past notification history when providing a notification. The notification unit, for example, prioritizes providing a notification method (audio, text, etc.) that the user previously preferred. For example, the notification unit analyzes the user's past notification history and prioritizes providing audio notification to a user who prefers audio notification. The notification unit can also suggest the optimal notification method for a specific time period based on the user's past notification history. For example, the notification unit analyzes the user's past notification history and suggests the optimal notification method for a specific time period. The notification unit can also analyze the user's past notification history and select the most effective notification method. For example, the notification unit analyzes the user's past notification history and selects the most effective notification method. In this way, the optimal notification method can be provided by referring to the user's past notification history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history data into a generation AI, which can select the optimal notification method.

[0099] The notification unit can customize the notification content according to the user's current task when providing a notification. For example, when the user is working, the notification unit provides a concise and to-the-point notification. For example, the notification unit analyzes the user's current task and provides a concise and to-the-point notification when the user is working. The notification unit can also provide a detailed notification when the user is taking a break. For example, the notification unit analyzes the user's current task and provides a detailed notification when the user is taking a break. The notification unit can also prioritize audio notifications when the user is exercising. For example, the notification unit analyzes the user's current task and prioritizes audio notifications when the user is exercising. This improves the effectiveness of notifications by customizing the notification content according to the user's current task. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's current task data into a generation AI, which can customize the notification content.

[0100] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit prioritizes important notifications. For example, the notification unit infers that the user is feeling anxious from the user's facial expressions and voice, and prioritizes important notifications. The notification unit can also postpone less important notifications if the user is relaxed. For example, the notification unit infers that the user is relaxed from the user's facial expressions and voice, and postpones less important notifications. The notification unit can also prioritize the most important notifications if the user is in a hurry. For example, the notification unit infers that the user is in a hurry from the user's movements and voice, and prioritizes the most important notifications. In this way, by determining the priority of notifications according to the user's emotions, important notifications can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input the user's facial expression data into a generation AI, which may then estimate the user's emotions and determine the priority of notifications.

[0101] The notification unit can select the optimal notification method by taking into consideration the user's device information when providing a notification. For example, if the user is using a smartphone, the notification unit prioritizes push notification. For example, the notification unit analyzes the user's device information and prioritizes push notification when the user is using a smartphone. Furthermore, if the user is using a tablet, the notification unit can provide a notification optimized for a large screen. For example, the notification unit analyzes the user's device information and provides a notification optimized for a large screen when the user is using a tablet. Furthermore, the notification unit can provide a concise and highly visible notification when the user is using a smartwatch. For example, the notification unit analyzes the user's device information and provides a concise and highly visible notification when the user is using a smartwatch. This makes it possible to provide the optimal notification method by taking into consideration the user's device information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's device information data into a generation AI, which can select the optimal notification method.

[0102] The notification unit can make the notification content multilingual according to the user's language setting when providing a notification. The notification unit, for example, automatically sets the notification language based on the language setting of the user's device. For example, the notification unit analyzes the language setting of the user's device and automatically sets the notification language. The notification unit can also provide a language switching function when the user uses multiple languages. For example, the notification unit analyzes the user's language setting and provides the language switching function when the user uses multiple languages. The notification unit can also provide notifications in a specific language when the user selects that language. For example, the notification unit analyzes the user's language setting and provides notifications in a specific language when the user selects that language. This makes the notification content multilingual according to the user's language setting, thereby improving understanding of the notification. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input the user's language setting data into a generation AI, which can then make the notification content multilingual.

[0103] At the time of notification, the notification unit can analyze the user's social media activity and provide related information. The notification unit can provide, for example, information about places where the user has checked in on social media. For example, the notification unit can analyze the user's social media activity and provide information about places where the user has checked in. The notification unit can also analyze the content of the user's social media posts and provide information about related risk factors. For example, the notification unit can analyze the content of the user's social media posts and provide information about related risk factors. The notification unit can also provide information about related places and events by referring to the activities of the user's friends on social media. For example, the notification unit can analyze the social media activity of the user's friends and provide information about related places and events. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's social media activity data to a generation AI, which can provide related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned input unit, analysis unit, identification unit, and notification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit uses the camera 42 of the smart device 14 to capture an image of a place of interest to the user and transmits the image to the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the input image using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies specific accident factors based on the risk factors identified by the generation AI. The notification unit is realized by the control unit 46A of the smart device 14 and notifies the user of the risk factors. For example, the push notification function of the smart device 14 is used to notify the user of the risk factors. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, identification unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit uses the camera 42 of the smart glasses 214 to capture an image of a place of interest to the user and transmits the image to the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the input image using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies specific accident factors based on the risk factors identified by the generation AI. The notification unit is realized by the control unit 46A of the smart glasses 214 and notifies the user of the risk factors. For example, the push notification function of the smart glasses 214 is used to notify the user of the risk factors. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, identification unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit uses the camera 42 of the headset type terminal 314 to capture an image of a place of interest to the user and transmits the image to the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the input image using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies specific accident factors based on the risk factors identified by the generation AI. The notification unit is realized by the control unit 46A of the headset type terminal 314 and notifies the user of the risk factors. For example, the risk factors are notified to the user using a push notification function of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, identification unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit uses the camera 42 of the robot 414 to capture an image of a place of interest to the user and transmits the image to the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the input image using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies specific accident factors based on the risk factors identified by the generation AI. The notification unit is realized by the control unit 46A of the robot 414 and notifies the user of the risk factors. For example, the push notification function of the robot 414 is used to notify the user of the risk factors.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The analysis unit can learn the user's past behavioral patterns and improve the accuracy of identifying risk factors. For example, the analysis unit can learn what locations the user has experienced accidents in the past and prioritize identifying risk factors in similar locations. The analysis unit can also learn what measures the user has taken in the past and evaluate whether similar measures are effective. Furthermore, the analysis unit can automatically collect data necessary to identify risk factors based on the user's past behavioral patterns. In this way, by taking the user's past behavioral patterns into consideration, the accuracy of identifying risk factors can be improved and more effective measures can be taken.

[0106] The identification unit can estimate the user's emotions and adjust the method for identifying risk factors based on the estimated user emotions. For example, if the user feels anxious, the identification unit can quickly identify risk factors and immediately notify the user. If the user feels relaxed, the identification unit can also identify detailed risk factors and suggest specific countermeasures. Furthermore, if the user is in a hurry, the identification unit can prioritize identifying the most important risk factors and briefly notify the user. In this way, by adjusting the method for identifying risk factors according to the user's emotions, it is possible to respond appropriately to the user's needs.

[0107] The notification unit can monitor the user's current activity status in real time and notify the user of risk factors at the optimal timing. For example, if the user is driving, the notification unit can delay notification until the user finishes driving. Also, if the user is in a meeting, the notification unit can postpone notification until the meeting ends. Furthermore, if the user is relaxed, the notification unit can immediately notify the user and encourage the user to take measures against risk factors. In this way, by taking the user's current activity status into consideration, risk factors can be notified at the optimal timing, ensuring the user's safety.

[0108] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user emotions. For example, if the user feels anxious, the analysis unit can provide detailed analysis results and suggest specific measures for risk factors. If the user feels relaxed, the analysis unit can also provide concise analysis results and explain the outline of risk factors. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points and encourage quick measures. In this way, adjusting the level of detail of the analysis according to the user's emotions can deepen the user's understanding and enable them to take appropriate measures.

[0109] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, the notification unit can prioritize the notification method (audio, text, etc.) that the user has previously preferred. The notification unit can also suggest the optimal notification method for a specific time period based on the user's past notification history. Furthermore, the notification unit can select the most effective notification method based on the user's past notification history and quickly notify the user of risk factors. This allows the optimal notification method to be provided by taking the user's past notification history into consideration, enabling a quick response to risk factors.

[0110] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user feels anxious, the analysis unit prioritizes analysis of important risk factors. Also, if the user feels relaxed, the analysis unit can analyze minor risk factors. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis of the most important risk factors and provide results quickly. In this way, by determining the priority of analysis according to the user's emotions, important risk factors can be quickly identified and appropriate measures can be taken.

[0111] The identification unit can customize the risk factor identification method by reflecting the user's past feedback. For example, the identification unit can suggest an optimal identification method based on feedback provided by the user in the past. The identification unit can also prioritize identification of similar risk factors by referring to the user's past feedback. Furthermore, the identification unit can adjust the identification priority based on the user's past feedback. In this way, by reflecting the user's past feedback, an optimal identification method can be provided and the accuracy of risk factor identification can be improved.

[0112] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user's emotions. For example, if the user feels anxious, the notification unit can provide a notification that includes detailed countermeasures. If the user feels relaxed, the notification unit can provide a concise notification that outlines risk factors. Furthermore, if the user is in a hurry, the notification unit can provide a notification that focuses on the main points and encourages the user to take prompt countermeasures. In this way, adjusting the content of the notification according to the user's emotions can deepen the user's understanding and enable them to take appropriate countermeasures.

[0113] The analysis unit can improve the accuracy of identifying risk factors based on the user's current geographic location information. For example, the analysis unit prioritizes analysis of specific risk factors in the user's current location. The analysis unit can also identify risk factors by referring to data on accidents that have occurred in similar locations in the past based on the user's geographic location information. Furthermore, the analysis unit can identify region-specific risk factors and propose countermeasures based on the user's geographic location information. In this way, by taking the user's current geographic location information into consideration, the accuracy of identifying risk factors can be improved, and more effective countermeasures can be taken.

[0114] The identification unit can estimate the user's emotions and adjust the display method of risk factors based on the estimated user's emotions. For example, if the user is feeling anxious, the identification unit can display risk factors in a simple and easy-to-understand manner. If the user is relaxed, the identification unit can also display detailed risk factors. Furthermore, if the user is in a hurry, the identification unit can display risk factors that are summarized to the basics. In this way, adjusting the display method of risk factors according to the user's emotions allows the user to better understand the risk factors and take appropriate measures.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The input unit inputs images of places of interest to the user. Images of places of interest to the user include within the home, workplace, public places, etc. The input unit inputs images taken with a smartphone or digital camera into the system. Images taken by the user can also be input to the system in real time. For example, the user can use the camera function of a smartphone to instantly send images taken by the user to the system. Step 2: The analysis unit uses the generation AI to analyze the image input by the input unit. The analysis unit uses an image recognition algorithm to identify factors in the image that could lead to an accident. For example, the generation AI, which has learned from a near-miss database, can be used to identify accident risk factors in the image. The generation AI uses a deep learning model to learn from past accident data and hazard prediction data, and can identify risk factors in the image with high accuracy. Step 3: The identification unit identifies risk factors for accidents from the images analyzed by the generation AI. Based on the risk factors identified by the generation AI, the identification unit identifies specific accident factors. For example, it identifies factors that pose a high risk of accidents, such as uneven stairs or the source of a fire in the kitchen. Step 4: The notification unit notifies the user of the risk factors identified by the identification unit. The notification unit notifies the user of the risk factors by voice notification, text notification, alert, or other methods. For example, the notification unit notifies the user of the risk factors by using a push notification function of a smartphone. Alternatively, the notification unit can notify the user of the risk factors by voice using a voice assistant.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting images of places of interest; an analysis unit that analyzes the image input by the input unit; an identification unit that identifies risk factors for an accident from the image analyzed by the analysis unit; a notification unit that notifies a user of the risk factor identified by the identification unit; Equipped with A system characterized by:

2. The analysis unit Analyze images using generative AI trained on a near-miss database 2. The system of claim 1.

3. The identification unit Generative AI identifies specific causes of accidents in images 2. The system of claim 1.

4. The notification unit Inform users of identified risk factors 2. The system of claim 1.

5. The input unit The user takes a photo and inputs it into the system.

2. The system of claim 1.

6. The analysis unit Identify specific accident risk factors, such as uneven stairs or kitchen fires 2. The system of claim 1.

7. The input unit Estimate the user's emotions and adjust the timing of image input based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Analyze the user's past input history and select the appropriate input method 2. The system of claim 1.

Images