Abstract: Disclosed dynamic schema mapping systems and methods monitor network traffic between different microservices and train mapping models based on the monitored network traffic using unsupervised training. This training of the mapping models generates a probability distribution tensor that shows the probabilistic associations of different key-value pairs of the schemas of different microservices. The trained mapping models are used to map a schema from a source microservice to another schema at a destination microservice. Should the translated schema be incompatible with the destination microservice, a semi-supervised approach is taken to make the translated schema compatible. The trained models may be reinforced (e.g., the probability distribution tensor may be updated) as more network traffic is collected and analyzed.

Abstract: The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes generating a dependency graph based on one or more microservices, computing, for each microservice of the one or more microservices, a complexity score using the dependency graph, identifying a subset of the one or more microservices, wherein each microservice in the subset of the one or more microservices has a complexity score meeting a threshold value, and applying a transactional lock on each microservice in the subset of the one or more microservices.

Abstract: At least one processor may capture a plurality of image snapshots containing information about a monitored system at a plurality of sequential times, each snapshot having the same vertical and horizontal dimensions. The processor may label the plurality of image snapshots as indicative of an event that took place in the monitored system, may receive additional data describing the event, may cluster the labeled plurality of image snapshots and the additional data using at least one machine learning clustering algorithm, and may merge the clustered plurality of image snapshots and the clustered additional data into merged data. The processors may create a model by processing the merged data using at least one neural network, the model being configured to detect future events of a same type as the event in the monitored system. The processor may store the model in a memory in communication with the processor.

Abstract: The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes generating a dependency graph based on one or more microservices in a transaction, wherein each node of the dependency graph corresponds to each microservice of the one or more microservices in the transaction, respectively, generating a first set of features using the dependency graph, predicting a first set of nodes that are likely to fail using a first machine learning model based on the first set of features, generating a second set of features using the dependency graph, predicting a second set of nodes that are likely to fail using a second machine learning model based on the second set of features, and applying one or more fixes to one or more microservices based on a combination of the first set of nodes and the second set of nodes.

Abstract: At least one processor may capture a plurality of image snapshots containing information about a monitored system at a plurality of sequential times, each snapshot having the same vertical and horizontal dimensions. The processor may label the plurality of image snapshots as indicative of an event that took place in the monitored system, may receive additional data describing the event, may cluster the labeled plurality of image snapshots and the additional data using at least one machine learning clustering algorithm, and may merge the clustered plurality of image snapshots and the clustered additional data into merged data. The processors may create a model by processing the merged data using at least one neural network, the model being configured to detect future events of a same type as the event in the monitored system. The processor may store the model in a memory in communication with the processor.

Abstract: Systems and apparatuses are disclosed for extracting information from document images. An example method includes segmenting a document image into multiple segments and determining formatting information for each segment. Determining formatting information for a segment includes determining one or more features of the segment and comparing the one or more features of the segment to one or more clusters of features associated with different document types. The formatting information for the segment is based on the comparison. The method also includes, for each segment, storing the formatting information in a data structure associated with the segment. The method further includes, for each segment including text to be identified during information extraction, applying OCR to the segment to generate machine-encoded text and storing the machine-encoded text in the associated data structure.

Abstract: A method ranks image brands. An image brand model is trained to generate an image brand rank from image features. An augmented image brand model is trained to generate an augmented image brand rank from the image brand rank. Predicted financial features are generated from the augmented image brand rank using a feature generation model. A neural network model is trained to generate a predicted augmented image brand rank from the predicted financial features.

Abstract: A method ranks image brands. An image brand model is trained to generate an image brand rank from image features. An augmented image brand model is trained to generate an augmented image brand rank from the image brand rank. Predicted financial features are generated from the augmented image brand rank using a feature generation model. A neural network model is trained to generate a predicted augmented image brand rank from the predicted financial features.

Abstract: Systems and apparatuses are disclosed for extracting information from document images. An example method includes segmenting a document image into multiple segments and determining formatting information for each segment. Determining formatting information for a segment includes determining one or more features of the segment and comparing the one or more features of the segment to one or more clusters of features associated with different document types. The formatting information for the segment is based on the comparison. The method also includes, for each segment, storing the formatting information in a data structure associated with the segment. The method further includes, for each segment including text to be identified during information extraction, applying OCR to the segment to generate machine-encoded text and storing the machine-encoded text in the associated data structure.

Abstract: Displaying a template of a document in a GUI. The template includes displayed sections. The sections includes at least one configuration defined by conditions, which are defined by rules. The sections are defined by a distinct set of conditions defined by a distinct set of rules. The configuration, conditions, and rules are stored separately from the template and from each other. When modifying the template, at least one of a condition change and a rule change required to implement the change to the template is identified. At least one of a modified condition and rule is retrieved. The first configuration is updated by modifying at least one of the conditions and the rules using at least one of the revised condition and the revised rule. The second configuration for the GUI is rendered to form a modified template. The modified template is displayed on the graphical user interface.

Abstract: Displaying a template of a document in a GUI. The template includes displayed sections. The sections includes at least one configuration defined by conditions, which are defined by rules. The sections are defined by a distinct set of conditions defined by a distinct set of rules. The configuration, conditions, and rules are stored separately from the template and from each other. When modifying the template, at least one of a condition change and a rule change required to implement the change to the template is identified. At least one of a modified condition and rule is retrieved. The first configuration is updated by modifying at least one of the conditions and the rules using at least one of the revised condition and the revised rule. The second configuration for the GUI is rendered to form a modified template. The modified template is displayed on the graphical user interface.