Abstract: A tax data collection system includes a navigation module configured to obtain user data. The system also includes a data graph including information relating to the user data. The system further includes a knowledge engine configured to map the user data onto a data model using the information from the data graph. Moreover, the system includes an inference engine configured to suggest a system action by analyzing at least the data model after the user data has been mapped thereon.

Abstract: Systems and methods that may be used to provide multitenant key derivation and management using a unique protocol in which key derivation may be executed between the server that holds the root key and a client that holds the derivation data and obtains an encryption key. In one or more embodiments, the derivation data may be hashed. The disclosed protocol ensures that the server does not get access to or learn anything about the client's derived key, while the client does not get access to or learn anything about the server's root key.

Abstract: A method includes training, using first real data objects, a generative adversarial network having a generator model and a discriminator model to create a trained generator model that generates realistic data, and training, using adversarial data objects and second real data objects, the discriminator model to output an authenticity binary class for the adversarial data objects and the second real data objects. The method further includes deploying the discriminator model to a production system. In the production system, the discriminator model outputs the authenticity binary class to a system classifier model.

Abstract: Systems and methods are provided for refreshing encryption and decryption keys. The disclosed techniques can improve refreshing encryption keys by allowing for the process to be automated, preventing downtime in each system and reducing developer labor in preparing and facilitating the exchange. In addition, the embodiments of the present disclosure can enable organizations to store keys (both old keys and newly generated keys) along with metadata in a known location accessible to the other organization.

Abstract: Certain aspects of the present disclosure provide techniques for identifying fraudulent user identifiers in a software application. An example method generally includes generating a vector representation of a user identifier. Using a first machine learning model and the vector representation of the user identifier, a fingerprint representative of the user identifier is generated. Using the first machine learning model and the generated fingerprint, a score is generated. The score generally describes a likelihood that the user identifier corresponds to a fraudulent user identifier. One or more similar user identifiers are identified based on the generated fingerprint and a second machine learning model. One or more actions are taken within a computing system relative to a user associated with the user identifier based on the generated score and the identified one or more similar user identifiers.

Abstract: Certain aspects of the present disclosure provide techniques for training decision trees representing users of a software application. An example method generally includes generating, from a transaction history data set for a plurality of users of a software application, a plurality of grouped data sets including transactions grouped by counterparty. A plurality of feature vectors are generated from the plurality of grouped data sets. Each feature vector generally corresponds to a user of the plurality of users and includes a plurality of features describing relationships between the user and a plurality of counterparties in a transaction history associated with the user. A decision tree is trained based on the plurality of feature vectors. The decision tree generally includes a plurality of paths terminating in a similar or different classification, and the plurality of paths distinguishes a user associated with the decision tree from other users of the software application.

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: Certain aspects of the present disclosure provide techniques for improving a user experience of an application. Embodiments include receiving, from a user and via a user interface, a request for informational content related to a step in a workflow within the application. Embodiments include determining an identifier associated with the step. Embodiments include retrieving a reference document based on the request. Embodiments include accessing metadata associated with the reference document to identify context information associated with the identifier. Embodiments include displaying a portion of the reference document to the user within the user interface based on the context information, wherein the portion of the reference document comprises the informational content.

Abstract: Certain aspects of the present disclosure provide techniques for processing natural language utterances in a knowledge graph. An example method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application. Operands and operators are extracted from the natural language utterance using a natural language model. Operands may be mapped to nodes in a knowledge graph, the nodes representing values calculated from data input into the application, and operators may be mapped to operations to be performed on data extracted from the knowledge graph. The functions associated with the operators are executed using data extracted from the nodes in the knowledge graph associated with the operands to generate a query result. The query result is returned as a response to the received long-tail query.

Abstract: Automatic keyphrase labeling and machine learning training may include a processor extracting a plurality of keywords from at least one search query that resulted in a selection of a document appearing in a search result. For each of the plurality of keywords, the processor may determine a probability that the keyword describes the document. The processor may generate one or more keyphrases by performing processing including selecting each of the plurality of keywords having a probability greater than a predetermined threshold value for insertion into at least one of the one or more keyphrases and assembling the one or more keyphrases from the selected plurality of keywords. The processor may label the document with the keyphrase.

Abstract: Techniques for detecting fraud may include obtaining a merchant's financial data; determining, via a machine learning model, a first prediction of the merchant's industry; generating a first probability matrix based on the first prediction and the declared information regarding the merchant's industry; determining, via the machine learning model, a second prediction of the merchant's industry; generating a second probability matrix based on the second prediction and the declared information regarding the merchant's industry; obtaining a declared industry of a subject merchant in a runtime environment; determining, via the machine learning model, a predicted industry for the subject merchant; obtaining, based on the declared industry and the predicted industry of the subject merchant, a first value from the first probability matrix and a second value from the second probability matrix; and labeling the subject merchant for further investigation.

Abstract: Systems and methods here may be used for pre-processing images, including using a computer for receiving a pixelated image of a paper document of an original size, downscaling the received pixelated image, employing a neural network algorithm to the downscaled image to identify four corners of the paper document in the received pixelated image, re-enlarging the downscaled image to the original size, identifying each of four corners of the paper document in the pixelated image, determining a quadrilateral composed of lines that intersect at four angles at the four corners of the paper document in the pixelated image, defining a projective plane of the pixelated image, and determining an inverse transformation of the pixelated image to transform the projective plane quadrilateral into a right angled rectangle.

Abstract: A system deploying a machine learning technique that utilizes known code graph and abstract syntax tree pairs for known JSON objects to learn a function for predicting a corresponding abstract syntax tree from a new JSON object. The predicted abstract syntax tree is used to generate code for formatting the new JSON object into a standardized data structure.

Abstract: Certain aspects of the disclosure provide systems and methods for training an information extraction transformer model architecture directed to pre-training a first multimodal transformer model on an unlabeled dataset, training a second multimodal transformer model on a first labeled dataset to perform a key information extraction task processing the unlabeled dataset with the second multimodal transformer model to generate pseudo-labels for the unlabeled dataset, training the first multimodal transformer model based on a second labeled dataset comprising one or more labels, the pseudo-labels generated, or combinations thereof to generate a third multimodal transformer model, generating updated pseudo-labels based on label completion predictions from the third multimodal transformer model, and training the third multimodal transformer model using a noise-aware loss function and the updated pseudo-labels to generate an updated third multimodal transformer model.

Abstract: Methods, systems and articles of manufacture for efficiently calculating an electronic tax return, such as within a tax return preparation system. A computerized tax return preparation system accesses taxpayer-specific tax data from a shared data store. The system executes a tax calculation engine configured to perform a plurality of tax calculations based on a tax calculation graph and the taxpayer-specific tax data from the shared data store. The system is configured to perform only the calculations in the tax calculation graph which are changed by new taxpayer-specific tax data received since the preceding tax calculation executed by the tax calculation engine. The system may also determine whether the new taxpayer-specific tax data does, or does not change the calculated tax return and the reason why.

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: Methods and systems for assisting entities with improving the effectiveness of their profiles are disclosed. An example method is performed by one or more processors of a system and includes storing profile data including profiles identifying attributes associated with respective entities, obtaining a selection data vector including values each indicating a selection rate for a respective entity, generating, using a trained analysis model, selection prediction data predicting, for each respective change of a set of possible changes to a selected entity's profile, how the selection rate for the selected entity will change if the selected entity's profile is adjusted in accordance with the respective change, selecting, from the selection prediction data, one or more recommended changes likely to result in an increase in the selection rate for the selected entity, and outputting a prompt recommending that the selected entity make one or more recommended changes to the selected entity's profile.

Abstract: Systems and methods for assessment of user price sensitivity using a predictive model are disclosed.

Abstract: Aspects of the present disclosure provide techniques for automated text amendment. Embodiments include identifying a first plurality of n-grams in first text associated with a domain. Embodiments include identifying a second plurality of n-grams in second text associated with the domain. Embodiments include identifying a third plurality of n-grams in third text that is not associated with the domain. Embodiments include determining candidate n-grams that are overexpressed in the second plurality of n-grams compared to the third plurality of n-grams. Embodiments include determining a match between a candidate n-gram of the candidate n-grams and a given n-gram of the first plurality of n-grams based on one or more matching factors. Embodiments include amending the first text based on the match between the candidate n-gram and the given n-gram.

Abstract: Aspects of the present disclosure provide techniques for efficient location tracking. Embodiments include receiving a device location from a mobile device. Embodiments include identifying a plurality of region definitions and selecting a set of region definitions from the plurality of region definitions based on a proximity of a location of each region definition of the plurality of region definitions to the device location. Embodiments include generating a provisional region definition based on a location of a region definition of the set of region definitions that is farthest from the device location and including the provisional region definition in the set of region definitions. Embodiments include providing the set of region definitions to the mobile device for provisioning and refraining from requesting device locations from the mobile device until receiving a notification from the mobile device that the mobile device has exited a provisional region defined by the provisional region definition.