Abstract: A method for improving the quality of a machine-learning based model includes generating a first query requesting a description of a change proposed to a system and an intended outcome of the change proposed; receiving a first response; generating a second query providing a risk of an incident associated with the change proposed and requesting justification of the change proposed in view of the risk; receiving a second response; generating a third query requesting an implementation plan for the change proposed; receiving a third response; generating an alert to an incident owner providing the description, intended outcome, risk, justification, and implementation plan of the change proposed; receiving a risk confirmation or rejection from the incident owner confirming or rejecting a relationship between the change proposed and the risk; and updating the machine-learning based model to learn an association between extracted features of the change and extracted features of the incident.

Abstract: A method for improving the quality of a machine-learning based model includes generating a first query requesting a description of a change proposed to a system and an intended outcome of the change proposed; receiving a first response; generating a second query providing a risk of an incident associated with the change proposed and requesting justification of the change proposed in view of the risk; receiving a second response; generating a third query requesting an implementation plan for the change proposed; receiving a third response; generating an alert to an incident owner providing the description, intended outcome, risk, justification, and implementation plan of the change proposed; receiving a risk confirmation or rejection from the incident owner confirming or rejecting a relationship between the change proposed and the risk; and updating the machine-learning based model to learn an association between extracted features of the change and extracted features of the incident.

Abstract: A method for generating a user-designed image for applying to a payment card includes receiving at least one image criterion of a target image associated with a user associated with a payment card, receiving a plurality of preliminary images generated by an artificial intelligence engine based on the at least one image criterion, receiving an image selection by the user, the image selection including an image selected from the plurality of preliminary images, displaying on a user interface the selected image superimposed on a virtual representation of the payment card, and setting at least one dimensional parameter of the selected image to suit a size of the payment card.

Abstract: A method for identifying and handling related incidents using a machine-learning based model, includes, performing by one or more processors, operations including: clustering a sub-group of incident records from among a plurality of incident records using the machine-learning based model based on a rolling time window and a number of records in the sub-group of incident records; creating a problem record based on the clustered incident records; populating the problem record with information related to the clustered incident records; linking the clustered incident records to the problem record; providing a notification that the problem record has been created; receiving a resolution for the problem record; and updating, based on the resolution, the machine-learning based model to learn an association between extracted features of the resolution and extracted features of the clustered incident records.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing sequences of multi-modal entity data using convolutional neural networks. One of the methods includes receiving an input sequence of multi-modal feature vectors characterizing an entity over a time window, wherein each multi-modal feature vector in the input sequence corresponds to a different time interval during the time window; processing the input sequence of multi-modal feature vectors using a convolutional neural network to generate a latent sequence that comprises a plurality of latent feature vectors; processing the latent sequence of latent feature vectors using an aggregation neural network to generate an aggregated feature vector; and processing the aggregated feature vector using an output neural network to generate a prediction that characterizes the entity after the time window.

Abstract: A method for improving the quality of a machine-learning based model includes generating a first query requesting a description of a change proposed to a system and an intended outcome of the change proposed; receiving a first response; generating a second query providing a risk of an incident associated with the change proposed and requesting justification of the change proposed in view of the risk; receiving a second response; generating a third query requesting an implementation plan for the change proposed; receiving a third response; generating an alert to an incident owner providing the description, intended outcome, risk, justification, and implementation plan of the change proposed; receiving a risk confirmation or rejection from the incident owner confirming or rejecting a relationship between the change proposed and the risk; and updating the machine-learning based model to learn an association between extracted features of the change and extracted features of the incident.