System

Abstract

An object of a system according to an embodiment is to comprehensively determine the safety of image data.SOLUTION: A system according to an embodiment includes an image collection unit, a preprocessing unit, a classification unit, and a filtering unit. The image collection unit collects image data. The preprocessing part preprocesses the image data collected by the image collection part. The classification unit classifies the image data preprocessed by the preprocessing unit. The filtering unit filters the inappropriate image classified by the classification 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 techniques do not adequately comprehensively assess the safety of image data, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively determine the safety of image data. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, a preprocessing unit, a classification unit, and a filtering unit. The image acquisition unit acquires image data. The preprocessing unit preprocesses the image data acquired by the image acquisition unit. The classification unit classifies the image data preprocessed by the preprocessing unit. The filtering unit filters out inappropriate images classified by the classification unit. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively determine the safety of image data. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The image data safety assessment system according to an embodiment of the present invention is a system that prevents the use of inappropriate images in the training data of an image generation AI and provides a safe data set. As a result, the image data safety assessment system can prevent the use of inappropriate images in the training data of an image generation AI and provide a safe data set.

[0029] An image data safety assessment system according to an embodiment includes an image collection unit, a preprocessing unit, a classification unit, and a filtering unit. The image collection unit collects image data. For example, the image data can be collected from the Internet. The image collection unit can also preferentially collect data from reliable sources. The preprocessing unit preprocesses the collected image data. For example, the preprocessing unit resizes the image data and removes noise. The preprocessing unit can also perform color correction on the image. The classification unit classifies the image data preprocessed by the preprocessing unit. For example, the classification unit detects photographs of victims' faces using facial recognition technology. The classification unit can also identify images of minors. The filtering unit filters inappropriate images classified by the classification unit. For example, the filtering unit automatically deletes inappropriate images. The filtering unit can also keep the training dataset clean. As a result, the image data safety assessment system according to an embodiment can prevent the use of inappropriate images in the training data of an image generation AI and provide a safe dataset.

[0030] The preprocessing unit can resize the image data in bulk and remove noise. The preprocessing unit, for example, resizes the image data in bulk. For example, the preprocessing unit resizes the collected image data in bulk to improve the accuracy of analysis. The preprocessing unit also removes noise. For example, the preprocessing unit removes noise from the image data to provide clean data. This can improve the accuracy of image analysis.

[0031] The classification unit can use facial recognition technology to detect facial photographs of victims and identify images of minors. The classification unit, for example, uses facial recognition technology to detect facial photographs of victims. For example, the classification unit uses facial recognition technology to identify facial photographs of victims of crimes or accidents. The classification unit also identifies images of minors. For example, the classification unit uses facial recognition technology to identify images of minors and classify them as inappropriate images. This can improve the accuracy of detecting inappropriate images.

[0032] The filtering unit can automatically delete inappropriate images and keep the training dataset clean. The filtering unit, for example, automatically deletes inappropriate images. For example, the filtering unit automatically deletes inappropriate images classified by the classification unit and excludes them from the training dataset. The filtering unit also keeps the training dataset clean. For example, the filtering unit keeps the training dataset clean by removing inappropriate images. This makes it possible to maintain the quality of the training dataset.

[0033] Furthermore, the image data safety determination system includes an evaluation unit that evaluates the safety of the image data and generates a report. The evaluation unit evaluates the safety of the image data and generates a report. For example, the evaluation unit comprehensively evaluates the safety of the collected image data and outputs the result as a report. For example, the evaluation unit provides an evaluation result such as "This data set does not contain inappropriate images" as a report. This makes it possible to comprehensively evaluate the safety of the image data and provide the result as a report.

[0034] The image collection unit can evaluate the reliability of the collection source and automatically exclude data from unreliable sources. The image collection unit, for example, evaluates the reliability of the collection source. For example, the image collection unit analyzes the past data provision history and reputation of the collection source and preferentially collects data from reliable sources. The image collection unit also automatically excludes data from unreliable sources. For example, the image collection unit excludes data from unreliable social media and preferentially collects data from reliable news sites and official institutions. This makes it possible to exclude unreliable data and improve the quality of the collected data.

[0035] The preprocessing unit can analyze the metadata of the image and evaluate the authenticity of the image. The preprocessing unit analyzes, for example, the metadata of the image. For example, the preprocessing unit evaluates the authenticity of the image based on information about the date and time the image was taken and the location. The preprocessing unit also excludes images whose date and time of taking the image have been unnaturally altered or whose location information does not match. This allows the authenticity of the image to be evaluated and highly reliable data to be provided.

[0036] Furthermore, the image collection unit can extract still images from the video data and perform preprocessing. The image collection unit extracts still images from the video data and performs preprocessing. For example, the image collection unit extracts specific frames from the video and performs resizing and noise removal on the still images. In this way, by extracting still images from the video data and performing preprocessing, the diversity of the dataset can be improved.

[0037] The preprocessing unit can convert the collected image data into different resolutions and formats to support display on a variety of devices. The preprocessing unit, for example, converts the collected image data into different resolutions and formats. For example, the preprocessing unit resizes high-resolution images for smartphones and tablets. The preprocessing unit can also convert JPEG format images into PNG format. This improves the versatility of the dataset by supporting display on different devices.

[0038] The classification unit analyzes background information of an image to perform classification with higher accuracy. The classification unit analyzes, for example, background information of an image. For example, the classification unit classifies an image into a specific category based on the location or time of day the image was taken. The classification unit can also evaluate the credibility of the image based on the background information. In this way, analyzing the background information can improve the accuracy of image classification.

[0039] The classification unit can analyze related text data and evaluate the relevance between the image and the text. The classification unit, for example, analyzes related text data. For example, the classification unit analyzes news articles and social media posts and evaluates the relevance between the image and the text. The classification unit can also understand the context of the image based on the text data. In this way, by analyzing the text data, the context of the image can be understood and classification accuracy can be improved.

[0040] The classification unit can analyze audio data and evaluate the relevance between images and audio to perform classification. The classification unit, for example, analyzes audio data. For example, the classification unit analyzes audio data in a video and associates it with images. The classification unit can also use voice recognition technology to convert audio data into text data and evaluate the relevance between images and text. In this way, by analyzing the audio data, the relevance between images and audio can be evaluated, and classification accuracy can be improved.

[0041] The classification unit can develop a classification algorithm customized for each region by taking into account the characteristics of different cultural spheres or regions. The classification unit, for example, takes into account the characteristics of different cultural spheres or regions. For example, the classification unit preferentially classifies images related to a specific culture or region. The classification unit also develops a classification algorithm customized for each region. For example, the classification unit uses a region-specific dataset to adjust the algorithm. This allows for improved classification accuracy by taking into account the characteristics of each region.

[0042] The detection unit can detect inappropriate images with higher accuracy by taking into account the historical background and cultural context of the image. The detection unit, for example, takes into account the historical background and cultural context of the image. For example, the detection unit refers to a related database to identify images related to historical events or cultural taboos. The detection unit can also identify inappropriate images based on the cultural context. In this way, by taking into account the historical background and cultural context, the accuracy of detecting inappropriate images can be improved.

[0043] The detection unit can analyze the details of the image and identify inappropriate elements in more detail. The detection unit analyzes the details of the image, for example. For example, the detection unit analyzes facial expressions and hand movements to identify inappropriate elements. The detection unit can also analyze the details of the background to identify inappropriate elements. In this way, by analyzing the details of the image, inappropriate elements can be identified in more detail, and detection accuracy can be improved.

[0044] The detection unit can analyze text data and evaluate the relevance between the image and the text to detect inappropriate images. The detection unit, for example, analyzes text data. For example, the detection unit analyzes captions and comments to evaluate the relevance between the image and the text. The detection unit can also identify inappropriate images based on the text data. In this way, by analyzing the text data, the relevance between the image and the text can be evaluated, and the accuracy of detecting inappropriate images can be improved.

[0045] The detection unit may develop a detection algorithm customized for each device, taking into account display on different devices and platforms. The detection unit may, for example, develop a detection algorithm optimized for smartphones and tablets. The detection unit may also perform device-specific optimization and adjust the algorithm. This allows for improved detection accuracy by taking into account display on different devices and platforms.

[0046] The filtering unit can analyze the image usage history and preferentially exclude images that have previously caused problems. The filtering unit, for example, analyzes the image usage history. For example, the filtering unit identifies images that have caused problems based on past usage status and a history of problems. The filtering unit can also determine images to be preferentially excluded based on the severity and frequency of the problems. This allows the quality of the dataset to be improved by preferentially excluding images that have previously caused problems.

[0047] The filtering unit can self-learn filtering criteria and continuously improve filtering accuracy. The filtering unit, for example, self-learns filtering criteria. For example, the filtering unit develops a self-learning algorithm based on past filtering results. The filtering unit can also adjust the method of updating the dataset and continuously improve filtering accuracy. In this way, filtering accuracy can be continuously improved by self-learning filtering criteria.

[0048] The filtering unit can perform more accurate filtering by taking into account image metadata. The filtering unit, for example, takes into account image metadata. For example, the filtering unit preferentially excludes images taken at a specific date and time or location based on information about the date and time of the image capture or the location. The filtering unit can also evaluate the authenticity of the image based on camera setting information. In this way, by taking into account metadata, the accuracy of filtering can be improved.

[0049] The filtering unit can develop customized filtering algorithms for different industries and applications. For example, the filtering unit develops customized filtering algorithms for different industries and applications. For example, the filtering unit sets filtering criteria specialized for medical images for the medical industry, and sets criteria specialized for advertising images for the advertising industry. The filtering unit can also adjust the algorithm using industry-specific data sets. This allows for the development of filtering algorithms tailored to different industries and applications, thereby improving the accuracy of filtering.

[0050] The evaluation unit can perform a more accurate safety evaluation by taking into account the intended use and context of the image. The evaluation unit, for example, considers the intended use and context of the image. For example, the evaluation unit analyzes related metadata and sets evaluation criteria based on the intended use and context of the image. The evaluation unit can also evaluate safety based on usage scenarios and related information. In this way, by taking into account the intended use and context, the accuracy of the safety evaluation can be improved.

[0051] The evaluation unit can self-learn the evaluation criteria and continuously improve the accuracy of the evaluation. The evaluation unit, for example, self-learns the evaluation criteria. For example, the evaluation unit develops a self-learning algorithm based on past evaluation results. The evaluation unit can also adjust the method of updating the dataset and continuously improve the accuracy of the evaluation. In this way, the evaluation criteria can be self-learned, thereby continuously improving the accuracy of the evaluation.

[0052] The evaluation unit can develop evaluation criteria customized for different industries or applications. For example, the evaluation unit develops evaluation criteria customized for different industries or applications. For example, the evaluation unit sets evaluation criteria specialized for medical images for the medical industry, and sets criteria specialized for advertising images for the advertising industry. The evaluation unit can also adjust the evaluation criteria using industry-specific datasets. In this way, the evaluation accuracy can be improved by developing evaluation criteria tailored to different industries or applications.

[0053] The evaluation unit can visualize the evaluation results and make them intuitively understandable using graphs and charts. The evaluation unit can visualize the evaluation results and make them intuitively understandable using graphs and charts. For example, the evaluation unit displays the evaluation scores in a bar graph or a pie chart. The evaluation unit can also display the evaluation results as infographics. In this way, by visualizing the evaluation results, they can be intuitively understood.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The image collection unit can evaluate the reliability of the collection source and automatically exclude data from unreliable sources. For example, the image collection unit analyzes the past data provision history and reputation of the collection source and preferentially collects data from highly reliable sources. The image collection unit also automatically excludes data from unreliable sources. For example, the image collection unit excludes data from unreliable social media and preferentially collects data from highly reliable news sites and official institutions. This makes it possible to exclude unreliable data and improve the quality of collected data.

[0056] The preprocessing unit can analyze image metadata and evaluate the authenticity of the image. For example, the preprocessing unit evaluates the authenticity of the image based on the image's capture date and time and location information. The preprocessing unit also excludes images whose capture date and time have been unnaturally altered or whose location information does not match. This allows the authenticity of the image to be evaluated and highly reliable data to be provided.

[0057] The image collection unit can extract still images from video data and perform preprocessing. For example, the image collection unit extracts specific frames from a video and resizes or removes noise from them as still images. This allows the diversity of the dataset to be improved by extracting still images from video data and performing preprocessing.

[0058] The preprocessing unit can convert the collected image data into different resolutions and formats to support display on various devices. For example, the preprocessing unit resizes high-resolution images for smartphones and tablets. The preprocessing unit can also convert JPEG format images to PNG format. This improves the versatility of the dataset by supporting display on different devices.

[0059] The classification unit can analyze background information of an image to perform classification with higher accuracy. For example, the classification unit classifies an image into a specific category based on the location or time of day the image was taken. The classification unit can also evaluate the credibility of the image based on the background information. In this way, analyzing the background information can improve the accuracy of image classification.

[0060] The classification unit can analyze related text data and evaluate the relevance between images and text. For example, the classification unit can analyze news articles and social media posts and evaluate the relevance between images and text. The classification unit can also understand the context of images based on the text data. This allows the system to understand the context of images by analyzing the text data, thereby improving classification accuracy.

[0061] The classification unit can analyze audio data and evaluate the relevance between images and audio to perform classification. For example, the classification unit can analyze audio data in a video and associate it with images. The classification unit can also use speech recognition technology to convert audio data into text data and evaluate the relevance between images and text. This allows the classification accuracy to be improved by analyzing the audio data and evaluating the relevance between images and audio.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The image collection unit collects image data. For example, image data can be collected from the Internet. Alternatively, data can be collected preferentially from reliable sources. Step 2: The preprocessing section preprocesses the collected image data. For example, it resizes the image data and removes noise. It can also perform color correction on the image. Step 3: The classification unit classifies the image data preprocessed by the preprocessing unit. For example, it uses facial recognition technology to detect the victim's face. It can also identify images of minors. Step 4: The filtering unit filters out inappropriate images classified by the classification unit. For example, it can automatically delete inappropriate images. It can also keep the training dataset clean.

[0064] (Example 2) The image data safety assessment system according to an embodiment of the present invention is a system that prevents the use of inappropriate images in the training data of an image generation AI and provides a safe data set. As a result, the image data safety assessment system can prevent the use of inappropriate images in the training data of an image generation AI and provide a safe data set.

[0065] An image data safety assessment system according to an embodiment includes an image collection unit, a preprocessing unit, a classification unit, and a filtering unit. The image collection unit collects image data. For example, the image data can be collected from the Internet. The image collection unit can also preferentially collect data from reliable sources. The preprocessing unit preprocesses the collected image data. For example, the preprocessing unit resizes the image data and removes noise. The preprocessing unit can also perform color correction on the image. The classification unit classifies the image data preprocessed by the preprocessing unit. For example, the classification unit detects photographs of victims' faces using facial recognition technology. The classification unit can also identify images of minors. The filtering unit filters inappropriate images classified by the classification unit. For example, the filtering unit automatically deletes inappropriate images. The filtering unit can also keep the training dataset clean. As a result, the image data safety assessment system according to an embodiment can prevent the use of inappropriate images in the training data of an image generation AI and provide a safe dataset.

[0066] The preprocessing unit can resize the image data in bulk and remove noise. The preprocessing unit, for example, resizes the image data in bulk. For example, the preprocessing unit resizes the collected image data in bulk to improve the accuracy of analysis. The preprocessing unit also removes noise. For example, the preprocessing unit removes noise from the image data to provide clean data. This can improve the accuracy of image analysis.

[0067] The classification unit can use facial recognition technology to detect facial photographs of victims and identify images of minors. The classification unit, for example, uses facial recognition technology to detect facial photographs of victims. For example, the classification unit uses facial recognition technology to identify facial photographs of victims of crimes or accidents. The classification unit also identifies images of minors. For example, the classification unit uses facial recognition technology to identify images of minors and classify them as inappropriate images. This can improve the accuracy of detecting inappropriate images.

[0068] The filtering unit can automatically delete inappropriate images and keep the training dataset clean. The filtering unit, for example, automatically deletes inappropriate images. For example, the filtering unit automatically deletes inappropriate images classified by the classification unit and excludes them from the training dataset. The filtering unit also keeps the training dataset clean. For example, the filtering unit keeps the training dataset clean by removing inappropriate images. This makes it possible to maintain the quality of the training dataset.

[0069] Furthermore, the image data safety determination system includes an evaluation unit that evaluates the safety of the image data and generates a report. The evaluation unit evaluates the safety of the image data and generates a report. For example, the evaluation unit comprehensively evaluates the safety of the collected image data and outputs the result as a report. For example, the evaluation unit provides an evaluation result such as "This data set does not contain inappropriate images" as a report. This makes it possible to comprehensively evaluate the safety of the image data and provide the result as a report.

[0070] The image collection unit can evaluate the reliability of the collection source and automatically exclude data from unreliable sources. The image collection unit, for example, evaluates the reliability of the collection source. For example, the image collection unit analyzes the past data provision history and reputation of the collection source and preferentially collects data from reliable sources. The image collection unit also automatically excludes data from unreliable sources. For example, the image collection unit excludes data from unreliable social media and preferentially collects data from reliable news sites and official institutions. This makes it possible to exclude unreliable data and improve the quality of the collected data.

[0071] The preprocessing unit can analyze the metadata of the image and evaluate the authenticity of the image. The preprocessing unit analyzes, for example, the metadata of the image. For example, the preprocessing unit evaluates the authenticity of the image based on information about the date and time the image was taken and the location. The preprocessing unit also excludes images whose date and time of taking the image have been unnaturally altered or whose location information does not match. This allows the authenticity of the image to be evaluated and highly reliable data to be provided.

[0072] Furthermore, the preprocessing unit can use an emotion estimation function to evaluate the emotional impact of the collected images and pre-filter images that may evoke negative emotions. The preprocessing unit uses the emotion estimation function to evaluate the emotional impact of the collected images and pre-filter images that may evoke negative emotions. For example, the preprocessing unit uses the emotion estimation function to evaluate the emotional impact of the collected images and filter out images with high emotion scores of sadness or anger. This makes it possible to improve the quality of the dataset by pre-filtering out images that may evoke negative emotions.

[0073] Furthermore, the image collection unit can extract still images from the video data and perform preprocessing. The image collection unit extracts still images from the video data and performs preprocessing. For example, the image collection unit extracts specific frames from the video and performs resizing and noise removal on the still images. In this way, by extracting still images from the video data and performing preprocessing, the diversity of the dataset can be improved.

[0074] The preprocessing unit can convert the collected image data into different resolutions and formats to support display on a variety of devices. The preprocessing unit, for example, converts the collected image data into different resolutions and formats. For example, the preprocessing unit resizes high-resolution images for smartphones and tablets. The preprocessing unit can also convert JPEG format images into PNG format. This improves the versatility of the dataset by supporting display on different devices.

[0075] Furthermore, the preprocessing unit can use an emotion estimation function to evaluate the emotional impact of the collected images and preferentially collect images that evoke positive emotions. The preprocessing unit uses the emotion estimation function to evaluate the emotional impact of the collected images and preferentially collect images that evoke positive emotions. For example, the preprocessing unit uses the emotion estimation function to evaluate the emotional impact of the collected images and preferentially use images with high emotion scores for joy or surprise. This allows the quality of the dataset to be improved by preferentially collecting images that evoke positive emotions.

[0076] The classification unit analyzes background information of an image to perform classification with higher accuracy. The classification unit analyzes, for example, background information of an image. For example, the classification unit classifies an image into a specific category based on the location or time of day the image was taken. The classification unit can also evaluate the credibility of the image based on the background information. In this way, analyzing the background information can improve the accuracy of image classification.

[0077] The classification unit can analyze related text data and evaluate the relevance between the image and the text. The classification unit, for example, analyzes related text data. For example, the classification unit analyzes news articles and social media posts and evaluates the relevance between the image and the text. The classification unit can also understand the context of the image based on the text data. In this way, by analyzing the text data, the context of the image can be understood and classification accuracy can be improved.

[0078] The classification unit can use the emotion estimation function to evaluate the emotional elements of the image and perform categorization based on the emotion. The classification unit, for example, uses the emotion estimation function to evaluate the emotional elements of the image. For example, the classification unit uses the emotion estimation function to evaluate the emotional elements of the image and classify the image based on an emotion score of joy or sadness. In this way, by evaluating the emotional elements, categorization based on emotion can be performed, and classification accuracy can be improved.

[0079] The classification unit can analyze audio data and evaluate the relevance between images and audio to perform classification. The classification unit, for example, analyzes audio data. For example, the classification unit analyzes audio data in a video and associates it with images. The classification unit can also use voice recognition technology to convert audio data into text data and evaluate the relevance between images and text. In this way, by analyzing the audio data, the relevance between images and audio can be evaluated, and classification accuracy can be improved.

[0080] The classification unit can develop a classification algorithm customized for each region by taking into account the characteristics of different cultural spheres or regions. The classification unit, for example, takes into account the characteristics of different cultural spheres or regions. For example, the classification unit preferentially classifies images related to a specific culture or region. The classification unit also develops a classification algorithm customized for each region. For example, the classification unit uses a region-specific dataset to adjust the algorithm. This allows for improved classification accuracy by taking into account the characteristics of each region.

[0081] The classification unit can use the emotion estimation function to evaluate the emotional elements of images and preferentially classify images that evoke positive emotions. The classification unit, for example, uses the emotion estimation function to evaluate the emotional elements of images. For example, the classification unit uses the emotion estimation function to evaluate the emotional elements of images and preferentially classify images with high emotion scores of joy or surprise. This allows the quality of the dataset to be improved by preferentially classifying images that evoke positive emotions.

[0082] The detection unit can detect inappropriate images with higher accuracy by taking into account the historical background and cultural context of the image. The detection unit, for example, takes into account the historical background and cultural context of the image. For example, the detection unit refers to a related database to identify images related to historical events or cultural taboos. The detection unit can also identify inappropriate images based on the cultural context. In this way, by taking into account the historical background and cultural context, the accuracy of detecting inappropriate images can be improved.

[0083] The detection unit can analyze the details of the image and identify inappropriate elements in more detail. The detection unit analyzes the details of the image, for example. For example, the detection unit analyzes facial expressions and hand movements to identify inappropriate elements. The detection unit can also analyze the details of the background to identify inappropriate elements. In this way, by analyzing the details of the image, inappropriate elements can be identified in more detail, and detection accuracy can be improved.

[0084] The detection unit can use the emotion estimation function to evaluate the emotional impact of images and preferentially detect images that are likely to evoke negative emotions. The detection unit uses the emotion estimation function to evaluate the emotional impact of images and preferentially detect images that are likely to evoke negative emotions. For example, the detection unit uses the emotion estimation function to evaluate the emotional impact of images and identify images with high emotion scores for sadness or anger. This allows the quality of the dataset to be improved by preferentially detecting images that are likely to evoke negative emotions.

[0085] The detection unit can analyze text data and evaluate the relevance between the image and the text to detect inappropriate images. The detection unit, for example, analyzes text data. For example, the detection unit analyzes captions and comments to evaluate the relevance between the image and the text. The detection unit can also identify inappropriate images based on the text data. In this way, by analyzing the text data, the relevance between the image and the text can be evaluated, and the accuracy of detecting inappropriate images can be improved.

[0086] The detection unit may develop a detection algorithm customized for each device, taking into account display on different devices and platforms. The detection unit may, for example, develop a detection algorithm optimized for smartphones and tablets. The detection unit may also perform device-specific optimization and adjust the algorithm. This allows for improved detection accuracy by taking into account display on different devices and platforms.

[0087] The detection unit can use the emotion estimation function to evaluate the emotional impact of images and preferentially detect images that evoke positive emotions. The detection unit uses the emotion estimation function to evaluate the emotional impact of images and preferentially detect images that evoke positive emotions. For example, the detection unit uses the emotion estimation function to evaluate the emotional impact of images and identify images with high emotion scores of joy or surprise. This allows the quality of the dataset to be improved by preferentially detecting images that evoke positive emotions.

[0088] The filtering unit can analyze the image usage history and preferentially exclude images that have previously caused problems. The filtering unit, for example, analyzes the image usage history. For example, the filtering unit identifies images that have caused problems based on past usage status and a history of problems. The filtering unit can also determine images to be preferentially excluded based on the severity and frequency of the problems. This allows the quality of the dataset to be improved by preferentially excluding images that have previously caused problems.

[0089] The filtering unit can self-learn filtering criteria and continuously improve filtering accuracy. The filtering unit, for example, self-learns filtering criteria. For example, the filtering unit develops a self-learning algorithm based on past filtering results. The filtering unit can also adjust the method of updating the dataset and continuously improve filtering accuracy. In this way, filtering accuracy can be continuously improved by self-learning filtering criteria.

[0090] The filtering unit can use the emotion estimation function to evaluate the emotional impact of the filtered images and preferentially filter out images that are likely to evoke negative emotions. The filtering unit uses the emotion estimation function to evaluate the emotional impact of the filtered images and preferentially filter out images that are likely to evoke negative emotions. For example, the filtering unit uses the emotion estimation function to evaluate the emotional impact of the filtered images and filter out images with high emotion scores of sadness or anger. This allows the quality of the dataset to be improved by preferentially filtering out images that are likely to evoke negative emotions.

[0091] The filtering unit can perform more accurate filtering by taking into account image metadata. The filtering unit, for example, takes into account image metadata. For example, the filtering unit preferentially excludes images taken at a specific date and time or location based on information about the date and time of the image capture or the location. The filtering unit can also evaluate the authenticity of the image based on camera setting information. In this way, by taking into account metadata, the accuracy of filtering can be improved.

[0092] The filtering unit can develop customized filtering algorithms for different industries and applications. For example, the filtering unit develops customized filtering algorithms for different industries and applications. For example, the filtering unit sets filtering criteria specialized for medical images for the medical industry, and sets criteria specialized for advertising images for the advertising industry. The filtering unit can also adjust the algorithm using industry-specific data sets. This allows for the development of filtering algorithms tailored to different industries and applications, thereby improving the accuracy of filtering.

[0093] The filtering unit can use the emotion estimation function to evaluate the emotional impact of the filtered images and preferentially retain images that evoke positive emotions. The filtering unit uses the emotion estimation function to evaluate the emotional impact of the filtered images and preferentially retain images that evoke positive emotions. For example, the filtering unit uses the emotion estimation function to evaluate the emotional impact of the filtered images and preferentially use images with high emotion scores for joy or surprise. This allows the quality of the dataset to be improved by preferentially retaining images that evoke positive emotions.

[0094] The evaluation unit can perform a more accurate safety evaluation by taking into account the intended use and context of the image. The evaluation unit, for example, considers the intended use and context of the image. For example, the evaluation unit analyzes related metadata and sets evaluation criteria based on the intended use and context of the image. The evaluation unit can also evaluate safety based on usage scenarios and related information. In this way, by taking into account the intended use and context, the accuracy of the safety evaluation can be improved.

[0095] The evaluation unit can self-learn the evaluation criteria and continuously improve the accuracy of the evaluation. The evaluation unit, for example, self-learns the evaluation criteria. For example, the evaluation unit develops a self-learning algorithm based on past evaluation results. The evaluation unit can also adjust the method of updating the dataset and continuously improve the accuracy of the evaluation. In this way, the evaluation criteria can be self-learned, thereby continuously improving the accuracy of the evaluation.

[0096] The evaluation unit can use the emotion estimation function to evaluate the emotional impact of the evaluation results and prioritize improving evaluation results that are likely to cause negative emotions. The evaluation unit uses the emotion estimation function to evaluate the emotional impact of the evaluation results and prioritize improving evaluation results that are likely to cause negative emotions. For example, the evaluation unit uses the emotion estimation function to evaluate the emotional impact of the evaluation results and improve evaluation results with high emotion scores for sadness or anger. This allows the quality of the evaluation to be improved by prioritized improvement of evaluation results that are likely to cause negative emotions.

[0097] The evaluation unit can develop evaluation criteria customized for different industries or applications. For example, the evaluation unit develops evaluation criteria customized for different industries or applications. For example, the evaluation unit sets evaluation criteria specialized for medical images for the medical industry, and sets criteria specialized for advertising images for the advertising industry. The evaluation unit can also adjust the evaluation criteria using industry-specific datasets. In this way, the evaluation accuracy can be improved by developing evaluation criteria tailored to different industries or applications.

[0098] The evaluation unit can visualize the evaluation results and make them intuitively understandable using graphs and charts. The evaluation unit can visualize the evaluation results and make them intuitively understandable using graphs and charts. For example, the evaluation unit displays the evaluation scores in a bar graph or a pie chart. The evaluation unit can also display the evaluation results as infographics. In this way, by visualizing the evaluation results, they can be intuitively understood.

[0099] The evaluation unit can use the emotion estimation function to evaluate the emotional impact of the evaluation results and preferentially provide evaluation results that evoke positive emotions. The evaluation unit uses the emotion estimation function to evaluate the emotional impact of the evaluation results and preferentially provide evaluation results that evoke positive emotions. For example, the evaluation unit uses the emotion estimation function to evaluate the emotional impact of the evaluation results and preferentially use evaluation results with high emotion scores for joy and surprise. This allows the quality of the evaluations to be improved by preferentially providing evaluation results that evoke positive emotions.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The image collection unit can evaluate the reliability of the collection source and automatically exclude data from unreliable sources. For example, the image collection unit analyzes the past data provision history and reputation of the collection source and preferentially collects data from highly reliable sources. The image collection unit also automatically excludes data from unreliable sources. For example, the image collection unit excludes data from unreliable social media and preferentially collects data from highly reliable news sites and official institutions. This makes it possible to exclude unreliable data and improve the quality of collected data.

[0102] The preprocessing unit can analyze image metadata and evaluate the authenticity of the image. For example, the preprocessing unit evaluates the authenticity of the image based on the image's capture date and time and location information. The preprocessing unit also excludes images whose capture date and time have been unnaturally altered or whose location information does not match. This allows the authenticity of the image to be evaluated and highly reliable data to be provided.

[0103] The preprocessing unit can use the emotion estimation function to evaluate the emotional impact of the collected images and pre-filter images that may evoke negative emotions. For example, the preprocessing unit can use the emotion estimation function to evaluate the emotional impact of the collected images and filter out images with high emotion scores of sadness or anger. This can improve the quality of the dataset by pre-filtering out images that may evoke negative emotions.

[0104] The image collection unit can extract still images from video data and perform preprocessing. For example, the image collection unit extracts specific frames from a video and resizes or removes noise from them as still images. This allows the diversity of the dataset to be improved by extracting still images from video data and performing preprocessing.

[0105] The preprocessing unit can convert the collected image data into different resolutions and formats to support display on various devices. For example, the preprocessing unit resizes high-resolution images for smartphones and tablets. The preprocessing unit can also convert JPEG format images to PNG format. This improves the versatility of the dataset by supporting display on different devices.

[0106] The preprocessing unit can use the emotion estimation function to evaluate the emotional impact of the collected images and preferentially collect images that evoke positive emotions. For example, the preprocessing unit can use the emotion estimation function to evaluate the emotional impact of the collected images and preferentially use images with high emotion scores of joy or surprise. This allows the quality of the dataset to be improved by preferentially collecting images that evoke positive emotions.

[0107] The classification unit can analyze background information of an image to perform classification with higher accuracy. For example, the classification unit classifies an image into a specific category based on the location or time of day the image was taken. The classification unit can also evaluate the credibility of the image based on the background information. In this way, analyzing the background information can improve the accuracy of image classification.

[0108] The classification unit can analyze related text data and evaluate the relevance between images and text. For example, the classification unit can analyze news articles and social media posts and evaluate the relevance between images and text. The classification unit can also understand the context of images based on the text data. This allows the system to understand the context of images by analyzing the text data, thereby improving classification accuracy.

[0109] The classification unit can use the emotion estimation function to evaluate the emotional elements of the image and perform categorization based on the emotion. For example, the classification unit can use the emotion estimation function to evaluate the emotional elements of the image and classify the image based on an emotion score of joy or sadness. In this way, by evaluating the emotional elements, categorization based on the emotion can be performed, and classification accuracy can be improved.

[0110] The classification unit can analyze audio data and evaluate the relevance between images and audio to perform classification. For example, the classification unit can analyze audio data in a video and associate it with images. The classification unit can also use speech recognition technology to convert audio data into text data and evaluate the relevance between images and text. This allows the classification accuracy to be improved by analyzing the audio data and evaluating the relevance between images and audio.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The image collection unit collects image data. For example, image data can be collected from the Internet. Alternatively, data can be collected preferentially from reliable sources. Step 2: The preprocessing section preprocesses the collected image data. For example, it resizes the image data and removes noise. It can also perform color correction on the image. Step 3: The classification unit classifies the image data preprocessed by the preprocessing unit. For example, it uses facial recognition technology to detect the victim's face. It can also identify images of minors. Step 4: The filtering unit filters out inappropriate images classified by the classification unit. For example, it can automatically delete inappropriate images. It can also keep the training dataset clean.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

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

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

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0157] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0180] 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 image collection unit that collects image data; a preprocessing unit that preprocesses the image data collected by the image collecting unit; a classification unit that classifies the image data preprocessed by the preprocessing unit; a filtering unit that filters out inappropriate images classified by the classification unit. A system characterized by:

2. The classification unit Uses facial recognition technology to detect photographs of victims' faces and identify images of minors 2. The system of claim 1.

3. The evaluation section Evaluating the security of the image data and generating a report 2. The system of claim 1.

4. The classification unit Analyze the background information of the image to perform more accurate classification 2. The system of claim 1.

5. The detection unit is It takes into account the historical background and cultural context of the image and detects inappropriate images with greater accuracy.

2. The system of claim 1.

6. The filtering unit Analyze the usage history of the image and prioritize excluding images that have caused problems in the past.

2. The system of claim 1.

7. The evaluation section Evaluating the emotional impact of evaluation results and prioritizing improvements to said evaluation results that may cause negative emotions 2. The system of claim 1.

8. The pre-treatment unit Evaluating the emotional impact of the collected images and pre-filtering the images that may evoke negative emotions.

2. The system of claim 1.

Images