Abstract: A method includes establishing a network connection with a source computing device and an application services computing device, receiving, via the network connection, a source event stream at the application services computing device, and extracting a sample of the source event stream. The method further includes partitioning the sample of the source event stream into fields, identifying a field data type of a field of the multiple fields in the sample, identifying a distribution of values of the field in the sample, and extrapolating, from the sample of the source event stream, extrapolated functions for the fields. Extrapolating an extrapolated function is dependent on the field data type and the distribution of the field. The method further includes transforming, based on the plurality of extrapolated functions in the configuration file, the source event stream to obtain a transformed event stream, and analyzing, by a target machine learning model, the transformed event stream.

Abstract: Systems and methods for identifying recommended topics are disclosed. An example method may be performed by one or more processors of a system and include identifying one or more attributes of a system user, identifying a subset of topics relevant to the system user based on analyzing the one or more attributes of the system user using an analysis model trained with a machine learning process to identify relevant topics for system users based on historical user attributes, generating, for each respective topic of the subset of topics, using the trained analysis model, a relevance score for the respective topic based at least in part on a most recent system page previously accessed by the system user and a current system page accessed by the system user, and generating one or more recommended topics for the system user based on the relevance scores.

Abstract: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Abstract: A system may include transaction storage devices. Each transaction storage device may include a data store and a registry configured to receive a request to register a universal resource identifier (URI) to a data store with a first secure identifier. The first secure identifier may be generated, using an encoding function and by a user, from a user identifier of the user. The registry may be further configured to store the URI of the data store with the first secure identifier, and receive, from an entity, a request to lookup a data store registered with a second secure identifier. The second secure identifier may be generated by the entity. The registry may be further configured to retrieve the URI of the data store in response to the request to lookup the data store, and transmit, to the entity, the URI of the data store.

Abstract: The disclosed embodiments provide a system that improves usage of an application. During operation, the system identifies, in a set of social media posts, a question related to use of the application. Next, the system responds to the set of social media posts with a set of answers to the question on one or more social media platforms and tracks social media responses to the set of answers. The system then calculates a set of relevance scores for the answers based on the tracked social media responses, wherein each relevance score represents a relevance of one of the answers to the question. Upon identifying the question in a subsequent social media post, the system selects an answer from the set of answers based on a relevance score of the answer and posts the answer in response to the subsequent social media post.

Abstract: A self-healing system configured to automatically restore non-responsive or failed applications to a normal operating state. A self-healing system may restart an application after confirming that the application itself has failed—and not an underlying dependency failure. The self-healing system may also evaluate a server hosting an application reported as being non-responsive to determine whether that server has itself failed. If an application is non-responsive or has failed on an otherwise healthy host, and the dependent service use by the application are available, the self-healing system automatically restores the application to a responsive state. To do so, the self-healing system may generate a run list specifying a sequence of scripts invoked to restore the application to the responsive state.

Abstract: Certain aspects of the present disclosure provide techniques for automating testing of applications. One example method generally includes obtaining a user interface (UI) source from an application defining pages of the application and fields associated with UI elements of each page of the pages and receiving a selection of a scenario. The method further includes generating scenario data based on a section of test data associated with the selection of the scenario and executing a test using the scenario data. To execute the test, the method includes identifying, by a runner, a first UI element of a first page of the application, identifying, by the runner, a first element of the scenario data mapped to the first UI element and entering, by the runner, the first element of the scenario data into the first UI element.

Abstract: Certain aspects of the present disclosure provide techniques for determining content quality of a set of content item based on generating a score for each content item. An example technique includes using a trained content quality model to generate the score for each content item component. In such example, the model is trained using a calculated set of features associated with text and metadata of a content item in a training data set as well as a quality label for the content item. The model is trained by associating the set of features with the quality label for each content item in the training data.

Abstract: Certain aspects of the present disclosure provide techniques for predicting a location based on transaction record data. An example technique includes obtaining a first set of transaction records and determining a merchant associated with each transaction record. The example further includes based on the merchant, determining and appending a branch identifier to each transaction record associated with the merchant to generate a first set of extended transaction records. The example further includes creating a consumption graph based on the first set of extended transaction records and determining an estimated location based on the consumption graph. The example further includes determining a precise point location based on the estimated location.

Abstract: A method including receiving events from different data sources for a service automatically executing in an enterprise system. A first event is enriched by providing the first event with first metadata that associates the first event with a first application used by the service. The first event is assigned to a time slice associated with the first application. A second event is enriched in a similar manner. A correlation graph of nodes and edges is built using the enriched events, with nodes representing the events and edges indicating relationships between the edges. A third event indicating a fault in the first application associated with the first node is received. The source of the error for the third event is identified using the second updated correlation graph and the time slice. The source of error is then mitigated.

Abstract: A tax return preparation application generates a first interview screen displayed to a subject taxpayer and requests first input of an image or entry of pre-determined data. Intermediate computer storing electronic tax data of multiple taxpayers determines whether electronic tax data provided or derived based on first input includes a first set for certain fields of an electronic tax form. If so, certain fields are automatically populated and a second, larger set of electronic data is identified and imported from data store to populate additional fields. If not, tax return preparation application generates a second interview screen requesting second input. Additional fields are not populated with data imported from data store when second input confirms that electronic tax data does not include the first set, but may import from data store when second input makes modifications or corrections such that electronic tax data includes the first set.

Abstract: Systems and methods that may be used to implement overlapping data caching for object application program interfaces (APIs). A unique identifier may be assigned to each sub-component of an object API request. The unique identifiers may be used to determine if sub-components of one object API request overlaps with a sub-component of a prior different request such that a data response for the prior different request can be retrieved from the cache and used as part of the data response for the current object API request.

Abstract: Certain aspects of the present disclosure provide methods and systems for determining that a user has been presented an element in a user interface. In certain embodiments, a certain dataset of past user-inputs may be de-duplicated. The de-duplicated dataset may then be reduced to a set of data structures by identifying all the data fields of the dataset that correspond to questions that were presented to certain users but were not required to be answered.

Abstract: During operation of a system, a financial transaction of an individual is associated with one or more predefined categories based on scores that indicate the likelihood of association. For example, a given predefined category may include a merchant name (such as the name of a potential counterparty in the financial transaction) and/or an attribute associated with one or more merchants. The score for a given predefined category may be determined based on a number of occurrences of the given predefined category in financial-transaction histories of a subset of a group of individuals who have common spending patterns in their financial-transaction histories. Moreover, the spending pattern may be based on values of financial transactions in the financial-transaction histories.

Abstract: Computer-implemented systems, methods and articles for generating an error report listing errors identified in the computerized preparation of a payroll tax form. The system includes a computing device, a data store, and a payroll tax form software application executable by the computing device. The payroll tax form software includes a payroll calculation engine, an error check engine, and one or more error graph(s). Each error graph includes a plurality of interconnected nodes configured to utilize tax data and calculated payroll data to identify errors. The calculation engine is configured to read tax data from the shared data store, perform payroll calculation operations, and write calculated payroll data to the shared data store. The error check engine is configured to read the tax data and payroll data and process the error graph(s) to identify errors and generate an error report listing the errors and/or explanations of the errors.

Abstract: Systems and methods for recognizing domain specific named entities are disclosed. An example method may be performed by one or more processors of a text incorporation system and include extracting a number of terms from a text under consideration, identifying, among the number of terms, a set of unmatched terms that do not match any of a plurality of known terms, passing each respective unmatched term to a vectorization module, embedding a vectorized version of each respective unmatched term in a vector space, comparing each vectorized version to known term vectors, passing, to a machine learning model, candidate terms corresponding to known term vectors closest to the vectorized versions, identifying, using the machine learning model, a best candidate term for each respective unmatched term, mapping the best candidate terms to unmatched terms in the text under consideration, and incorporating the text under consideration into the system based on the mappings.

Abstract: Systems and methods are disclosed. An example method may be performed by one or more processors of a system and include retrieving case data indicating, for each respective case of a number of cases, one or more documents retrieved to assist a system user associated with the respective case, generating, from the case data, a case matrix including a plurality of rows each corresponding to a respective case of the number of cases and a plurality of columns each corresponding to the documents retrieved to assist the system user associated with the respective case, and identifying groups of similar cases among the plurality of cases based on a clustering process performed on at least a portion of the case matrix.

Abstract: Runtime computer software product management systems that operate in a networked computing environment. A computer database or computer software product configuration/access management system includes a runtime subscription model including a hybrid entitlement-service state model that defines a hybrid state including first or entitlement state data specifying access to the computer software product by the customer and second or service state data specifying permissible customer actions given a pre-determined combination of entitlement state and service state data. Subscription model state changes may be executed in runtime in response to events published by an event generation system and processed by the configuration/access management system, and modifications to the hybrid state are communicated to an access control system. The access control system responds to customer requests based at least in part upon the received hybrid state data.

Abstract: A method for improving a transcription may include identifying, in the transcription, reliable channel tokens of an utterance of a reliable channel and an unreliable channel token of an utterance of an unreliable channel, and generating, using a machine learning model, a vector embedding for the unreliable channel token and vector embeddings for the reliable channel tokens. The method may further include calculating vector distances between the vector embedding and the vector embeddings, and generating, for the unreliable channel token and using the vector distances, a score corresponding to a reliable channel token. The method may further include determining that the score is within a threshold score, and in response to determining that the score is within the threshold score, replacing, in the transcription, the unreliable channel token with the reliable channel token.