According to a first aspect, it is provided a method for processing an input media feed for monitoring a person. The method is performed by a media processing device comprising a media capturing device. The method comprises the steps of: obtaining an input media feed using the media capturing device; providing the input media feed to a local artificial, AI, intelligence engine, to extract at least one feature of the input media feed; and transmitting intermediate results comprising the extracted at least one feature to train a central AI model, while refraining from transmitting the input media feed. The local AI engine forms part of the media processing device. The intermediate results comprise a label of the extracted at least one feature. The label is obtained from an end result of another local AI engine.

Computer-implemented methods, systems and computer-readable media for building and using an artificial intelligence model for secure biometric authentication. Utilizing difference vectors, the model securely relates output vectors generated from noisy biometric data of a plurality of enrolled users to pre-defined fixed points in a vector space.

A method may include obtaining a cluster. The cluster may include a subset of reference entities. The method may further include calculating distances between features of a target entity and features of the subset of reference entities, selecting, based on the distances, peer entities from the subset, and generating an estimated value of a metric. The generating may include applying, to the features of the target entity, a machine learning model trained using training data including values of the features for the peer entities labeled with a value of the metric. The method may further include presenting the estimated value of the metric.

A deep learning-based digital/virtual staining method and system enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally/virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital/virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally/virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

Data segmentation, analysis, and security is provided. An analysis of a set of generated data evolution paths corresponding to a set of data collected over a defined span of time is performed. A behavior trend is determined based on analysis of the set of generated data evolution paths. A set of action steps is performed automatically based on the determined behavior trend.

Methods, systems, and apparatus, including computer programs encoded on computer storage medium, for training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels. The method includes actions of obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with a respective label from the predetermined set of multiple labels; and modifying the training data to generate regularizing training data, comprising: for each training item, determining whether to modify the label associated with the training item, and changing the label associated with the training item to a different label from the predetermined set of labels, and training the neural network on the regularizing data.

Disclosed are computer-implemented methods which encompass determining whether two medical images were taken of the same patient. In a first aspect, this is done by analysing a registration of the two images with one another. The registration may be a direct registration between the two images or an indirect registration, for example via an atlas to which each image is registered. In other aspects, a machine learning algorithm is trained on the basis of image registrations to determine whether the two images were taken of the same patient. The disclosed methods serve the purpose of being able to group medical images together which were taken of the same patient without having to provide or otherwise process data about the identity of the patient.

In one implementation, a method includes identifying a first content-dependent feature associated with a data sector. The method further includes determining a baseline data sector associated with the data sector. The method further includes determining, by a processing device, a content-dependent delta between the first content-dependent feature and a second content-dependent feature of the baseline data sector. The method further includes providing the content-dependent delta and an indicator to the baseline data sector for storage on a plurality of storage devices.

In some embodiments, there is provided a system. The system may include at least one data processor and at least one memory storing instructions which, when executed by the at least one data processor, cause the apparatus to at least: determine, for a received document including at least one item, that the received document likely includes at least one missing item, the determination based on at least a machine learning model and the at least one item; and provide an indication of the at least one missing item. Related systems and articles of manufacture are also provided.

Aspects of the invention include determining an event associated with a computing system, the event occurring at a first time, obtaining system data associated with the computing system, determining a system state of the computing system at the first time based on the system data, determining, based on the system state, two or more system data clusters comprising clustered system data associated with the system state of the computing system, determining, via an interpolation algorithm, an interpolated data value for the first time based on the system data, and adjusting the interpolated data value based on a determination that the interpolate data value is outside the two or more system data clusters.