Abstract: A system receives transaction data over time, and creates structured data based on the received transaction data. Purchase transactions that are associated with a purchase category are identified in the structured data and labeled. A recurrent neural network such as a long short-term memory (LSTM) network, in particular, a k-LSTM architecture using weighted averages to update hidden states and cell states, is trained to build a model. The model is used to predict the likelihood of a purchase transaction.

Abstract: There is provided a layered anomaly detection system. The system may perform real-time compliance anomaly detection using a plurality of anomaly-detecting machine learning (ML) models. The system includes a pre-processing subsystem which classifies population sets within a system and defines a plurality of context spaces, clusters objects and labels for each population member. The system trains a plurality of ML anomaly detection models based on received compliance events. The ML anomaly detection models may output an anomaly detection score and a confidence score. One or more ensemble ML models may be used to enhance accuracy.

Abstract: There is provided a system for performing compliance and risk assessment of a software application, such as a Software-as-a-Service (SaaS) application. The system may store historical compliance evidence data and receive updated compliance evidence data. The system may include a plurality of machine learning models which are trained using different subsets of historical compliance evidence data. When received updated compliance evidence data is incomplete, the machine learning models may be used to generate predicted compliance evidence so as to provide a full compliance evidence data set. One or more of risk and/or compliance scores may be determined based on combinations of received compliance data, predicted compliance data, and historical compliance data.

Abstract: There is provided a method for monitoring and assessing compliance of a computing system. The method may include receiving documents containing rules. Each document may be converted to a tree structure having nodes. A mapping object may be generated to map a tree structure to another tree structure. A second mapping object may be generated to relate a tree structure to compliance controls. A third mapping object may be generated to relate compliance controls to a set of compliance control evidence. A compliance score may be determined based on the controls, the evidence, and the mappings.

Abstract: There is provided a system for compliance monitoring of applications executing in cloud operating environments. The system may convert rule-containing documents to tree structures comprising nodes representing compliance rules. The system may monitor controls which provide evidence of applications' compliance when triggered by events, such as configuration changes and user interactions. The compliance evidence may be evaluated for an effect on an application's compliance score. The system may further provide a unique mapping identifier system for mappings between tree structures, controls, and compliance evidence. The system may further include a layered anomaly detection module which include a real-time processing component and a second processing component for generating and refining anomaly detection machine learning models which is de-coupled from the real-time processing component.

Abstract: There is provided an automated system for generating mappings and enumerated tree objects. Documents containing compliance rules may be converted to tree-structured documents. Mappings may be generated which link nodes within the tree-structured documents to one another. Compliance controls may be created which continuously monitor a computing environment for compliance by applications with the controls. Compliance evidence objects may be received which indicate an application's compliance or non-compliance with one or more controls.

Abstract: Embodiments relate to web applications and interfaces providing personalized access to relevant wellness resources using microservices and machine learning models. Embodiments relate to web applications and interfaces that provide recommendations based on personas computed using machine learning models. The interfaces and web applications using microservices to provide interface tools that scale to multiple users.

Abstract: Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. First and second task data are received. The task data are processed to compute a first performance metric reflective of performance of the automated agent relative to other entities in a first time interval, and a second performance metric reflective of performance of the automated agent relative to other entities in a second time interval. A reward for the reinforcement learning neural network that reflects a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent.

Abstract: Systems, methods and data structures for the processing of data for the secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of electronic payment transactions and other secure data processes. Aspects of such systems include trusted platforms by which networked communication devices and merchant systems may registered as trusted entities. Information associated with particular payment means, such as accounts or payment tokens, can be stored on device(s) secure data sets known as virtual or electronic wallets, or in the form of secure payment tokens. Among other improvements, the invention enables the use of multiple payment accounts to fund purchases and other electronic transactions.

Abstract: There is provided a computer system and method for orchestrating a user interface, the method include: obtaining a first data set representative of intercepted data communication messages between a user interface of a user and a merchant hosting server; obtaining a second data set representing an instruction set for loading visual elements on the user interface provided from the merchant hosting server; analyzing the first data set to obtain one or more user-specific characteristics; determining if the user-specific characteristics associated with the user satisfy a trigger condition associated with a current resource offering; and responsive to a positive determination: injecting, into the instruction set for loading the visual elements on the user interface provided from the merchant hosting server, code corresponding to an interactive visual element corresponding to the current resource offering.

Abstract: Systems and methods for machine learning architecture for time series data prediction. The system may include a processor and a memory storing processor-executable instructions. The processor-executable instructions, when executed, may configure the processor to: obtain time series data associated with a data query; generate a predicted value based on a sampled realization of the time series data and a continuous time generative model, the continuous time generative model trained to define an invertible mapping to maximize a log-likelihood of a set of predicted values for a time range associated with the time series data; and generate a signal providing an indication of the predicted value associated with the data query.

Abstract: A method for detecting network anomalies comprises monitoring a network that provides public-facing application services and monitoring at least one external public Internet platform outside of the network to obtain volumetric problem report data about the application services. The external public Internet platform is nonspecific to the application services. Responsive to the volumetric problem report data from the external public Internet platform(s) exceeding a threshold, at least one internal network event logging tool is queried for alerts, and from the alerts, at least one anomaly associated with the volumetric problem report data is identified and an anomaly report about the at least one anomaly is generated. Responsive to generating the anomaly report, it may be determined whether the at least one anomaly has a known remediation, and if so, the known remediation may be initiated automatically. Network administrator(s) may also be automatically notified.

Abstract: Systems and methods for managing electronic resource access are disclosed. An example computer system may include: a processor; and a non-transitory memory storing one or more sets of instructions that when executed by the processor, causes the system to: receive a user request for accessing or modifying an electronic resource; process the user request to obtain text data; apply feature engineering to the text data to output a feature matrix, the feature engineering comprising application of natural language processing to the text data; use a trained machine learning model to determine a probability score indicating a likelihood of incident occurrence as a result of the user request; and generate signals for displaying a decision granting or denying the user request based on the probability score.

Abstract: A de-coupled computing infrastructure is described that is adapted to provide domain specific contextual engines based on conversational flow. The computing infrastructure further includes, in some embodiments, a mechanism for directing conversational flow in respect of a backend natural language processing engine. The computing infrastructure is adapted to control or manage conversational flows using a plurality of natural language processing agents.

Abstract: Systems, methods, and machine-executable data structures for the processing of data for the secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of electronic payment transactions. Aspects of such methods, systems, and data structures include providing at an electronic device, an output indicating that a dynamically-configured electronic token is in a transaction-ready state, where the dynamically-configured electronic token is associated with a plurality of loyalty accounts; in response to one or more signals providing information regarding a location of the electronic device, obtaining token data associated with a loyalty account of the plurality of loyalty accounts corresponding to the location of the electronic device; and via a data communication interface, route a token, generated from the token data, for processing at a transaction processing system.

Abstract: A system for computational estimation sampling from non-trivial probability distributions. The system comprises a processor, operating in conjunction with computer memory. The processor is configured to conduct importance sampling using normalizing flows where a base distribution has a set of parameters that can be adjusted to account for heavy-tailed distributions.

Abstract: Described in various embodiments herein is a technical solution directed to decomposition of time as an input for machine learning, and various related mechanisms and data structures. In particular, specific machines, computer-readable media, computer processes, and methods are described that are utilized to improve machine learning outcomes, including, improving accuracy, convergence speed (e.g., reduced epochs for training), and reduced overall computational resource requirements. A vector representation of continuous time containing a periodic function with frequency and phase-shift learnable parameters is used to decompose time into output dimensions for improved tracking of periodic behavior of a feature. The vector representation is used to modify time inputs in machine learning architectures.

Abstract: An approach for increasing security of biometric templates is described. An improved system is adapted to split a full set of features or representations of a trained model into a first partial template and a second partial template, the second partial template being stored on a secure enclave accessible only through zero-knowledge proof based interfaces. During verification using the template, a new full set of features is received for comparison, and a model is loaded based on the available portions of the model. Comparison utilizing the second partial template requires the computation of zero-knowledge proofs as direct access to the underlying second partial template is prohibited by the secure enclave.

Abstract: Systems and methods for maintaining IT assets are disclosed. An example computer system may include: a processor; and a non-transitory memory storing one or more sets of instructions that when executed by the processor, causes the system to: access or receive asset data for a plurality of IT assets located in a plurality of physical locations; determine, based on the asset data, a plurality of assets due for maintenance; access or receive, in real time, map data for physical locations associated with the plurality of assets; determine a cost matrix for scheduling a route plan for visiting the physical locations associated with the plurality of assets; compute the route plan based on the cost matrix and the map data; generate signals for displaying, at a display of a user device, the route plan; and transmit said signals to the user device for displaying the route plan.

Abstract: Systems and methods for adaptively identifying anomalous network communication traffic. The system includes a processor and a memory coupled to the processor. The memory includes processor-executable instructions that configure the processor to: obtain data associated with a sequence of network communication events; determine that the sequence of communication events is generated by a computing agent based on a symmetricity measure associated with the sequence of network communication events; generate a threat prediction value for the sequence of network communication events prior-generated by the computing agent based on a combination of the symmetricity measure and a randomness measure associated with the network communication events; and transmit a signal for communicating that the sequence of network communication events is a potential malicious sequence of network communication events based on the threat prediction value.