Abstract: Certain aspects of the present disclosure provide techniques for managing a search engine based on search performance metrics. An example method generally includes dividing a set of search history data into a first subset of search history data and a second subset of search history data. The first subset of data is associated with interaction with search results, and the second subset of data is associated with non-interaction with search results. A first quality score is generated for searches in the first subset of data. A second quality score is generated for searches in the second subset of data based on different search intents identified for each temporally related group in the second subset of data. An overall quality score is generated for a search engine, and one or more actions with respect to the search engine are taken based on the overall quality score.

Abstract: Certain aspects of the present disclosure provide techniques for generating a user interface to prompt users of a software application to perform an action in the software application. The method generally includes generating historical transaction time gap data for transactions in the account. A probability distribution is generated based on the historical time gap data. The probability distribution represents a probability that a transaction related to the account has been performed after an elapsed time from a previous transaction. A probability that an unrecorded transaction exists for an account based on the probability distribution and a time difference between a most recent transaction and a current time. The probability that an unrecorded transaction exists is determined to exceed a threshold probability, and a user interface is generated and displayed to a user of the software application including a prompt for the user to enter new transactions for the account.

Abstract: Certain aspects of the present disclosure provide techniques for enumerating relationships in an object graph using traversal context objects that are decoupled from the implementation details of an object graph and the objects stored therein. An example method generally includes receiving a request to traverse an object graph, the object graph including a plurality of objects. A root context object is generated, and a traversal context object associated with a root node of the object graph is generated. The traversal context object is linked to the root context object and added to a traversal data structure. The graph is traversed by adding and removing traversal context objects to and from the traversal data structure until a terminating event occurs.

Abstract: Incoming data requests from the perspective of the data lake, are gathered and analyzed to determine the usage of the data. Using the perspective of the data lake avoids the technical challenge of analyzing data usage by different computation points, which are at different locations, perform hard-to-track different operations, and are often reachable only through complicated access protocols. Another technical challenge of mapping between an object path and a table path is solved by generating object path datasets and table path datasets at different levels of abstractions. A comparison is performed, iteratively, from a lower level of granularity and the granularity is increased in the progressive steps. Matches from iterations are unionized to generate a final matching data. Observability metrics are generated using final matching data and are used to perform downstream operations such as controlling data table access, moving data tables to cold storage, decommissioning unused pipelines, etc.

Abstract: A system and method for extracting data from a piece of content using spatial information about the piece of content. The system and method may use a conditional random fields process or a bidirectional long short term memory and conditional random fields process to extract structured data using the spatial information.

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: The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes receiving electronic transaction data indicative of one or more transactions, identifying, from the one or more transactions, a subset of transactions that are associated with for known attribute values with respect to one or more unique recipients, computing, for each unique provider of the one or more unique providers, a provider feature based on the known attribute values with respect to a subset of the one or more associated unique recipients, computing, for a given recipient indicated in one or more given transactions that are not included in the subset of transactions, a recipient feature based on the provider feature of each unique provider of the one or more associated unique providers, and predicting, based on the recipient feature, a value for the attribute with respect to the given recipient.

Abstract: Certain aspects of the present disclosure provide techniques for providing an augmented reality user interface, including: receiving, by an image sensor of an electronic device, an image of a physical document; determining a document type associated with the physical document by performing image recognition on the image of the physical document; determining an augmented reality template to display on a display of the electronic device; displaying the augmented reality template on the display of the electronic device, wherein the augmented reality template is aligned in three dimensions with the physical document; determining a distance between the physical document and the electronic device; and enabling one or more interactive user interface elements within the augmented reality template displayed on the display of the electronic device if the determined distance between the physical document and the electronic device is less than a threshold distance.

Abstract: A computer-implemented method includes receiving training data including a plurality of API requests from a plurality of client devices. The method includes generating a plurality of permissible API sessions based on the training data. The method includes applying a sequence embedding technique to the plurality of permissible API sessions to generate a plurality of embeddings. The method includes applying a dimensionality reduction technique to the plurality of embeddings to generate a plurality of compact embeddings. The method includes applying a clustering technique to the plurality of compact embeddings to determine a plurality of different clusters of the compact embeddings. The method includes generating a plurality of patterns based on the plurality of different clusters. Each of the plurality of patterns is descriptive of permissible API sessions associated with a corresponding cluster of the plurality of different clusters.

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: Systems and methods for labeling data are disclosed. An example method may be performed by one or more processors of a labeling system and include retrieving labeled data, identifying characteristics predictive of labels that would be entered for unlabeled data items having the respective characteristics based on the labeled data, training an analysis model to predict labels that would be entered for unlabeled data items, generating, for unlabeled data items, using the trained analysis model, a prediction of a label that will be entered for the respective unlabeled data item if the respective unlabeled data item is presented for labeling, selecting, based on the generated predictions, a subset of unlabeled data items to be presented for labeling, receiving labels for the subset of unlabeled data items, determining that a completion criteria associated with the trained analysis model is met, and generating labels for remaining unlabeled data items.

Abstract: A method may be used to predict a business' category by analyzing the business' vendors. A neural network architecture may be trained via supervised learning to predict categories for businesses based on listed vendors. The neural network may be used to classify uncategorized businesses within an accounting software database. A list of factors associated with a business' success may be generated by analyzing, aggregating and ranking factors determined to be relevant to a business based on its categorization. The factors associated with the business' success may be related to the products and/or services offered by the business and the format of which those products and/or services are offered by the business. The factors may also be related to the products and/or services purchased by the business from a vendor and the format of which those products and/or services are purchased from the vendor.

Abstract: Systems and methods for user authentication are disclosed. An example method includes receiving a request for access to a first secured service, the request corresponding to a first user, determining whether or not the request for access is valid, in response to determining that the request for access is valid, determining whether or not the first user has successfully performed a secondary authentication within a predetermined time period of the request for access, and in response to determining that the first user has successfully performed the secondary authentication within the predetermined time period of the valid request for access, providing the first user with access to the secured service.

Abstract: Augmented Denoising Diffusion Implicit Models (“DDIMs”) using a latent trajectory optimization process can be used for image generation and manipulation using text input and one or more source images to create an output image. Noise bias and textual bias inherent in the model representing the image and text input is corrected by correcting trajectories previously determined by the model at each step of a diffusion inversion process by iterating multiple starts the trajectories to find determine augmented trajectories that minimizes loss at each step. The trajectories can be used to determine an augmented noise vector, enabling use of an augmented DDIM and resulting in more accurate, stable, and responsive text-based image manipulation.

Abstract: An apparatus may include a database and a processor in communication with at least one network. The processor may be configured to instantiate an application stack comprising a data missing detector, a data source router, and a response handler. The data missing detector may be configured to receive a request for data and determine whether the data is available from the database. The data source router may be configured to identify an alternate database from which to obtain the data when the data is not available from the database and route the request for data through the at least one network to an alternate processor associated with the alternate database. The response handler may be configured identify responsive data to a routed response received from another processor in the database and send the responsive data to a requesting device. Multiple apparatuses may form a distributed system.

Abstract: A method including receiving, in a machine learning model (MLM), a corpus including words. The MLM includes layers configured to extract keywords from the corpus, plus a retrospective layer. A first keyword and a second keyword from the corpus are identified in the layers. The first and second keywords are assigned first and second probabilities. Each probability is a likelihood that a keyword is to be included in a key phrase. A determination is made, in the retrospective layer, of a first probability modifier that modifies the first probability based on a first dependence relationship between the second keyword being placed after the first keyword. The first probability is modified using the first probability modifier. The first modified probability is used to determine whether the first keyword and the second keyword together form the key phrase. The key phrase is stored in a non-transitory computer readable storage medium.

Abstract: Systems and methods may predict whether a user will abandon an application. Initially, different features are extracted from a time series of numerical values rendered by the application. A machine learning model is trained using a supervised approach on the extracted features to map the known and labeled outputs. In this supervised approach, the output may be binary with a “0”-label for a user that has left the application in the middle of a task and a “1”-label for the user who has used the application to finish the task. During the deployment, the trained model may be called to predict whether the user will abandon the application based on time series of numerical values retrieved in real time. If an abandonment is predicted, a customized message is generated and presented on the user's device.

Abstract: A method, computer program product, and system are provided. A first natural language text is received via a user interface. A generative pretrained transformer machine learning model processes the first natural language text and a context to identify a first intent. The processing is based in part on a syntax determined from a set of natural language completion paradigms. The generative transformer machine learning model maps the first set of parameters to a first query. The mapping is associated with a first confidence. The generative transformer machine learning model processes the first set of parameters and the first query to generate a set of execution steps. The processing is performed when the first confidence satisfies a threshold. The set of execution steps is parsed into a query object that is forwarded to a reporting service.

Abstract: Systems and methods for automated techniques that generate queryable database table ownership attribution information in real-time. In addition to generating ownership attribution information, system and methods provide a novel framework for creating bi-partite graphs and generating insightful graph data.

Abstract: A method implements anonymous uncensorable cryptographic chains. The method includes receiving, from a first application, verifiable data for a current record and unverified data for the current record. The unverified data for the current record was received by the first application from a second application. The method further includes verifying the verifiable data for the current record with unverified data from a previous record. The method further includes recording the verifiable data for the current record and the unverified data for the current record to the current record responsive to verifying the verifiable data for the current record. The method further includes presenting the current record to one or more of the first application and to the second application.