Examples of the present disclosure describe systems and methods for detecting and preventing digital media piracy. In example aspects, a machine learning model is trained on a dataset related to digital media content. Input data may then be collected by a data collection engine and provided to a multimedia processor. The multimedia processor may extract multimedia features (e.g., audio, visual, etc.) and recognized patterns from the input data and provide the extracted multimedia features to a trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a confidence value may be generated. The confidence value may be compared to a confidence threshold. If the confidence value is equal to or exceeds the confidence threshold, then the input data may be classified as pirated digital media. Remedial action response(s) may subsequently be deployed to thwart the piracy of the digital media.

An electronic device includes a camera to capture an image, and a processor to input an image acquired by photographing a detergent container into a trained model to acquire detergent information corresponding to the detergent container, and to guide an amount of detergent dispensed based on washing information corresponding to the detergent information. The trained model is a neural network trained using images of a plurality of detergent containers.

A sensor device includes an array sensor having a plurality of detection elements arrayed in one or two dimensional manner, a signal processing unit configured to acquire a detection signal by the array sensor and perform signal processing, and a calculation unit. The calculation unit detects an object from the detection signal by the array sensor, and gives an instruction, to the signal processing unit, on region information generated on the basis of the detection of the object as region information regarding the acquisition of the detection signal from the array sensor or the signal processing for the detection signal.

A sensor device includes an array sensor having a plurality of detection elements arrayed in one or two dimensional manner, a signal processing unit configured to acquire a detection signal by the array sensor and perform signal processing, and a calculation unit. The calculation unit detects an object from the detection signal by the array sensor, and gives an instruction, to the signal processing unit, on region information generated on the basis of the detection of the object as region information regarding the acquisition of the detection signal from the array sensor or the signal processing for the detection signal.

Systems as described herein may include predicting item level data based on merchant advertisement information. A transaction pattern may be detected. The merchant advertisement information may be retrieved and parsed to generate a price list. A number of transactions that each shares a common payment amount may be determined and the number may reach a threshold value. Items from the price list may be matched with the common payment amount. The transaction records may be updated to indicate likely item level transaction information. In a variety of embodiments, the likely transaction information may be presented to a user.

Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

Disclosed are a method and device for determining an icon position. The method includes: detecting a target object in a target image and determining the reference position of the target object in the target image, and detecting a salient position in the target image, thereby obtaining the reference position of a key target or object in the target image, and a salient position possibly requiring more attention in the target image; and selecting, according to the distance between the reference position or salient position and preset candidate positions, an icon position from the candidate positions.

Disclosed are a method and device for determining an icon position. The method includes: detecting a target object in a target image and determining the reference position of the target object in the target image, and detecting a salient position in the target image, thereby obtaining the reference position of a key target or object in the target image, and a salient position possibly requiring more attention in the target image; and selecting, according to the distance between the reference position or salient position and preset candidate positions, an icon position from the candidate positions.

In a creation method of a trained model, a reconstructed image (60) obtained by reconstructing three-dimensional X-ray image data (80) is generated. A projection image (61) is generated from a three-dimensional model of an image element (50) by a simulation. The projection image is superimposed on the reconstructed image to generate a superimposed image (67). A trained model (40) is created by performing machine learning using the superimposed image, and the reconstructed image or the projection image.

In a creation method of a trained model, a reconstructed image (60) obtained by reconstructing three-dimensional X-ray image data (80) is generated. A projection image (61) is generated from a three-dimensional model of an image element (50) by a simulation. The projection image is superimposed on the reconstructed image to generate a superimposed image (67). A trained model (40) is created by performing machine learning using the superimposed image, and the reconstructed image or the projection image.