Abstract: A computer-implemented method for determining an Application Quality Index (AQI) Score of a software-as-a-service (SAAS) multi-tenant application to a SAAS system is provided. The method may be performed on a computing device executing a processor. The method may include accessing data at a data repository. The accessed data includes at least connection/disconnection data and API error data of the SAAS multi-tenant application. The method also includes processing the accessed data to generate two or more sub-indexes. The two or more sub-indexes includes at least a first sub-index based on the connection/disconnection data and a second sub-index based on the API error data. The first sub-index is assigned a weighted value based on content of the connection/disconnection data and the second sub-index is assigned a weighted value based on content of the API error data.

Abstract: An integrated development environment (IDE) system that may provide runtime validation, and incremental compilation and linking of multiple schema resources for a query language such as e.g., GraphQL. The runtime validation may be performed during the incremental compilation and linking process. If errors in the source code are detected during the runtime validation process, the compilation and linking process may be stopped without generating runtime objects.

Abstract: A method for computer estimations based on statistical tree structures involves obtaining a statistical tree structure for reference elements. The statistical tree structure includes leaf nodes segmenting a statistic for a data label according to data features in the reference elements, and intermediate nodes connecting a first node to the leaf nodes. Each of the first node and the intermediate nodes provide a branching based on one of the data features. The method further includes obtaining target data, including values for the data features, and a value for the data label. The method also includes selecting the first node, associated with a first data feature, traversing the statistical tree structure to a leaf node by matching the values of the data features to the branching of the intermediate nodes, and assessing the value for the data label in the target data based on the statistic associated with the leaf node.

Abstract: The invention relates to a method for mapping topics. The method includes obtaining terms, obtaining tokens from each term, and identifying a first and a second set of topics. Each of the topics represents one or more of the terms. The method further includes identifying first and second topic names for the first and the second sets of topics. For each topic, the tokens associated with the terms assigned to the topic are analyzed for relevance, and a token with a high relevance is selected as the topic name. The method also includes selecting one of the first and one of the second sets of topics to obtain first and second selected topics, determining, based on the one or more terms, a similarity value between each of the first and the second selected topics, and establishing a mapping between similar first and second selected topics.

Abstract: A method may include obtaining a knowledge graph including entities, and determining, for the knowledge graph, a first state including a first selectable entity subset of the entities that are selectable by a user. The first selectable entity subset may include an entity. The method may further include receiving, from the user and via a graphical user interface (GUI), a selection of the entity from the first selectable entity subset, and responsive to the selection, adding the entity to a report schema. The method further includes restricting, by the GUI, a space of selectable entities addable to the knowledge graph to the first selectable entity subset. The report schema may be used to populate a report. The method may further include, responsive to the selection, transitioning the knowledge graph to a second state including a second selectable entity subset of the entities that are selectable by the user.

Abstract: A computer-implemented method and system are provided to utilize machine learning technology to process user financial transaction data to predict a personalized payment screen architecture. A plurality of feature datasets associated with transaction data of a plurality of electronic invoices are obtained by a computing device. Each feature dataset comprises a plurality of features, a payment screen and a payment method configured to be presented on at least one payment screen. The computing device is configured to train a machine learning model with the feature datasets to produce a probability matrix with probabilities of each payment method used to pay the invoices through each payment screen. The computing device may weigh the probability matrix to generate a recommendation matrix and determine a prediction of a payment screen based on the recommendation matrix.

Abstract: A method includes receiving first metrics describing first behaviors of applications having a fault, and second metrics describing second behaviors of an enterprise system in which the applications are executing. The first and second metrics are ingested. Ingesting includes assigning a first cardinality score to first metrics related to the fault. Ingesting also includes assigning a second, lower cardinality score to second metrics that are unrelated to the fault. Tumbling window processing is performed on the ingested metrics by sorting the first metrics and the second metrics into time slices. Each of the time slices includes a corresponding data structure for storing the data in the different time periods. Tumbling window processing is also performed on the ingested metrics by storing more data in the time slices for the first metrics having the first cardinality score relative to the second metrics having the second cardinality score.

Abstract: Aspects of using augment reality for service projects are described. In some implementations, an augmented reality (AR) system receives one or more images of a real-world space, detects a physical object in the real-world space from the one or more images, and identifies the physical object for a service project. The AR system also generates a virtual object associated with the identified physical object and outputs the virtual object for display in an AR environment. The AR system further receives a selection of one or more portions of the virtual object associated with one or more portions of the physical object to be serviced and generates information for performing the service project based on the one or more portions of the physical object.

Abstract: A method secures hash chains via hybrid consensus. A proximate payload for a proximate chain block for a proximate chain is obtained. A first identifier of the proximate chain and the proximate payload are hashed with a hash function to generate a second identifier of the proximate chain. The proximate chain block is added to the proximate chain. The proximate chain block includes the first identifier, the second identifier, and the proximate payload. A request to add the second identifier to a reporting chain is transmitted. A response indicating that the second identifier is incorporated into the reporting chain using the consensus mechanism is received.

Abstract: A method utilizes a framework for transaction categorization personalization. A transaction record is received. a baseline model is selected from a plurality of machine learning models. An account identifier, corresponding to the transaction record using the baseline model, is selected. The account identifier for the transaction record is presented.

Abstract: Systems and methods for predicting multi-currency goals are disclosed. An example method may include receiving a multi-currency goal, the multi-currency goal including at least a current value of an account in a first currency, a target value, and one or more target currencies, predicting one or more future values of the account using a first machine learning model, predicting an inflation-adjusted future currency exchange rate (IAFCER) for each predicted future value of the account for each of the one or more target currencies using one or more second machine learning models, determining, for each predicted IAFCER, an equivalent target amount in the corresponding target currency, and predicting whether the multi-currency goal will succeed or fail based at least in part on the predicted future values of the account and the equivalent target amounts.

Abstract: A method may include registering, with an offline job to be executed by a computer processor, an application programming interface (API) and an operation, obtaining, from a repository, a user consent of a user for the operation, and in response to obtaining the user consent, creating, for the user, an access token including the operation and the API. The user consent may be stored external to the access token. The method may further include transmitting the access token to the offline job, and calling, by the offline job, the API using the access token.

Abstract: An orchestration layer for execution user defined automation workflows. The orchestration layer may include multiple process instances that host user defined automation workflows that automate processes or tasks. To improve system performance and reduce operating costs, the user defined automation workflows are deployed to the orchestration layer in a standard format that standardizes the user defined workflow configurations. The orchestration layer may also dynamically scale the computational resources allocated to teach process instance based on the properties of each user defined automation workflow.

Abstract: A method categorizes transaction records. A transaction record is received by a server application. The transaction record is encoded with a first machine learning model to obtain a transaction vector, wherein the transaction vector is in a same vector space as multiple account vectors. A second machine learning model executing in the server application, selects an account vector, from the multiple account vectors, corresponding to the transaction vector. An account identifier, corresponding to the account vector, is presented for the transaction record.

Abstract: A method may include receiving a first definition of an object type from a first software component and a second definition of the object type from a second software component. The object type may be labeled by an ID. The method may further include storing, in a dynamic graph, a node labeled by the ID, and storing, in a type definition repository external to the dynamic graph, the first definition of the object type and the second definition of the object type. The method may further include receiving, from the first software component, a modified first definition of the object type. The method may further include replacing, in the type definition repository and using the ID, the first definition of the object type with the modified first definition, and transmitting, to the second software component, a message indicating a need to lookup, by the ID, the modified first definition.

Abstract: A method services competing updates from multiple servicing instances. An update message is received by a class of a plurality of classes that service an object. The update message includes an update to the object and an expected version for the object. A version match is determined between the expected version and a current version of the object. When the version match is successful, the update is applied to the object. A response message including the current version of the object is generated. The response message is transmitted. When the update is successfully applied, the response message is transmitted as a success message. When the update is not successfully applied, the response message is transmitted as a failure message.

Abstract: This disclosure provides systems, methods and apparatuses for scheduling tasks in an electronic system. In some implementations, a dynamic scheduling system allows performance-based access to a task schedule. In distributing tasks to be completed, the dynamic scheduling system prioritizes resources that are more proficient at completing the tasks over resources that are less proficient. For example, resources that are more proficient may receive higher-priority access to the task schedule than resources that are less proficient. Each resource may be assigned a proficiency score based on quantitative or qualitative performance indicators associated with tasks previously completed by the resource. Each resource is dynamically provided access to the task schedule based on its proficiency score.

Abstract: Certain aspects of the present disclosure provide techniques for generating a compliance graph based on a compliance rule to implement in a software program product for determining user compliance. To generate a compliance graph, an encoder receives a compliance rule in a source language and generates a set of corresponding vectors. The decoder, which has been trained using verified training pairs and synthetic data, generates a sequence of operations based on the vectors from the encoder. The sequence of operations is the used to build a graph in which each operation is a node in the graph and each node is connected to at least one other node in the same graph or a separate graph.

Abstract: The disclosed embodiments provide a system that facilitates use of an electronic device. During operation, the system displays, by an application on the electronic device, a user interface comprising a first set of user-interface elements for obtaining data from a user. Upon detecting a first swiping gesture through the user interface, the system submits the data for processing by the application. The system then updates the user interface with a second set of user-interface elements based on the submitted data.

Abstract: A method and system provide a data management system that provides data management services and products to users. The method and system provide a self-help service including an assistance documents database including a large number of assistance documents. The data management system includes a database of messaging content including a large number of messages that can be provided with assistance documents accessed by users. The data management system includes a predictive model that has been trained with a machine learning process to match messages from the messaging content database to assistance documents. When a user accesses an assistance document, the predictive model matches the assistance document to a message from the messaging content database in real time. The data management system provides personalized messaging content data including the matched message in real time with the assistance documents accessed by the user.