Canva Pty Ltd

Described herein is a computer implemented method for generating an alert based on a product update for a software system, the product update including a first product update variant of the software system and a product update control of the software system, the method including: receiving user interaction event data based on one or more user interaction events from a plurality of users of the software system, the plurality of users including a first subset of users using the first product update variant and a second subset of users using the product update control; processing the user interaction event data to determine if any of the one or more user interaction events is an exposure event; based on the user interaction event data of any determined exposure events, determining, for each of the first product update variant and product update control, a respective user metric dataset for a user metric; calculating, for the first product update variant and product update control, respective probability input data in respect of the user metric based on the respective user metric datasets; based on the probability input data, calculating, for the first product update variant and product update control, a first variant estimation model and a control estimation model respectively; deriving a first change value distribution based on the first variant estimation model and the control estimation model such that the first change value distribution defines probability of relative uplift or downlift of user interactions; calculating a first impact value based on first change value distribution and a predefined time value t; comparing the calculated first impact value with a predefined impact value threshold; and based on this comparison, generating a first alert condition.

Computer processing systems and computer implemented methods for determining an arrangement of elements of a design are described. The arrangement may be into rows or columns. Collections of elements for rows and/or columns are determined based on overlap of the elements along a dimension prior to arrangement. Each collection may be arranged within an area of the design into a row or column. In some embodiments an initial arrangement into both rows and columns is determined and a final arrangement is determined based on which arrangement results in a least total overlap area of the elements.

A method, system and computer-readable medium for training a multi-label classification model to learn a new entity added to a dictionary of the multi-label classification model are disclosed. The method includes: identifying candidate training instances based on the new entity; generating a training record for each of the candidate training instances, where the training record includes the candidate training instance and the new entity as a new label; for each training record: adding existing entities from the dictionary, such that the training record includes a set of labels including the new label; and refining the set of labels to remove labels that are not related to the corresponding training instance. The method further includes training the multi-label classification model using the refined training records.

Methods of training a machine learning model for image processing are described, as well as methods for image processing to determine a predicted rotation value for an image based on the trained machine learning model. A method of training includes utilising as a learning objective a reduction or minimisation of a classification loss. Training image pairs may be associated with classifications corresponding to rotation values for the purpose of determining the classification loss. An image may be divided into a plurality of images for determining an input to the machine learning model, for training or for image processing.

Described herein is a computer implemented method. The method includes determining a first set of objects, wherein each object in the first set of objects is associated with an object-image and a position and processing the first set of objects to generate a first image-raster. The first image-raster incorporates each object-image that is associated with an object in the first set of objects, and each object-image is positioned in the first image-raster based on the position of the object that the object-image is associated with. The method further includes generating a first digital image by processing the first image-raster using a trained image generation model.

Described embodiments generally relate to a method of generating an image editing tool recommendation. The method includes receiving a prompt entered by a user; processing the prompt using a tool class prediction model to predict a tool class associated with the prompt, wherein the tool class is associated with at least one image editing tool that is configured to perform an image editing function described by the prompt; determining an image editing tool associated with the predicted tool class; and outputting a recommendation to the user directing the user to the determined image editing tool.

Described herein is a computer implemented method for generating a plurality of synthesised designs based on a selected design that includes a first element. The method includes accessing design generation data that includes source data and field association data. The source data defines a first field and a first design generation record that defines a first content item that is an image-type content item and that is associated with the first field. The field association data defines a first association between the first field and the first element. The method further includes generating a first synthesised design that is based on the selected design and that is generated such that the first element of the selected design is updated in the first synthesised design using the first content item.

Described embodiments generally relate to a method for extracting a portion of an image. The method includes accessing an image for editing; receiving at least one user input related to the accessed image, the at least one user input corresponding to at least one image element of the accessed image; based on the at least one user input, generating at least one new image mask; receiving a user input corresponding to at least one selected image mask; and generating an extraction mask based on each of the at least one selected image masks, to allow the portion of the image defined by the extraction mask to be extracted.

Described herein is a computer implemented method. The method includes a client application generating a document request and communicating the document request to a server system. The client application receives: document data including page data in respect of a plurality of pages of the document; recorded interaction data defining one or more interactive events that occurred during a recording of a presentation associated with the document; and media data including audio and/or video data recorded during the recording of the presentation. The client application replays the presentation by using the recorded interaction data to synchronise display of the pages with playback of the media data.