Abstract: A system and method allows an offeror to identify parameters for determining whether a user could benefit from an offer, and transaction data for each of several users, and the parameters, are used to determine which of several users could benefit from the offer. The offer is presented to those users, along with the data of how the user could benefit, and the system and method allows the user to accept the offer, transmitting information to the offeror or his agent if the user accepts the offer. Payment may be made based on the offers, acceptances, or both.

Abstract: Systems and methods are disclosed for tuning a generative artificial intelligence (AI) model based on a knowledge base. Instead of manually generating questions relevant to the knowledge base, providing those questions to the generative AI model, and manually reviewing the answers generated by the generative AI model in order to tune the generative AI model over many iterations, a natural language processing model may be configured to leverage the knowledge base to automatically generate questions and answers based on the knowledge base. In this manner, the natural language processing model is able to generate tuning data that may be used to automatically tune the generative AI model. The systems and methods also disclose automatic tuning of the generative AI model, including testing and feedback that may be used to improve tuning of the generative AI model.

Abstract: A method implements a table discovery service. The method includes receiving a query string, converting the query string to a query graph, and identifying a selected graph, of a set of graphs, that matches the query graph. The method further includes transmitting a notification identifying a previously generated table corresponding to the selected graph, receiving a notification response to accept the previously generated table, and transmitting data from the previously generated table in response to the query string.

Abstract: The one or more embodiments provide for a method, system, and computer program product, an intent, generated by a large language model from a text, is received from a user device as a first input to an advice planner. A state of an account is received as a second input to the advice planner. The advice planner classifieds the intent into a domain corresponding to the intent, and generates, as output, a plan comprising a first set of action logic associated with the domain. Each action logic is a discrete step in an ordered sequence for achieving a desired state of the account. The advice planner forwards the plan to the large language model (LLM). The large language model receives the plan as input and generates advice in a natural language format as output. The advice is then forwarded to the user device.

Abstract: A method including receiving, from a server, a backoff data packet including first information and second information. The first information includes a backoff command to cease transmitting at least some requests to a service executing on the server, and The second information includes metadata. The method also includes adding the backoff data packet to cached data stored in a cache. The method also includes receiving, from a client, a request data packet including a request for the service. The method also includes comparing request information, associated with the request data packet, to the cached data, including at least comparing the request information to the metadata. The method also includes blocking, responsive to a match between the request information and the cached data, the request data packet from being transmitted to the service.

Abstract: Systems and methods are disclosed for managing a generative artificial intelligence (AI) model. Managing the generative AI model may include training or tuning the generative AI model before use or managing the operation of the generative AI model during use. Training or tuning a generative AI model typically requires manual review of outputs from the model based on the queries provided to the model to reduce hallucinations generated by the generative AI model. Once the model is in use, though, hallucinations still occur. Use of a confidence (whose generation is described herein) to train or tune the generative AI model and/or manage operation of the model reduces hallucinations, and thus improves performance, of the generative AI model.

Abstract: A method including inputting, into a phrase recognition model comprising a neural network, a vector comprising a plurality of ngrams of text. The method also includes applying, using the phrase recognition model, a filter to the plurality of ngrams during execution. The filter has a skip word setting of at least one. The method also includes determining, based on the skip word setting, at least one ngram in the vector to be skipped to form at least one skip word. The method also includes outputting an intermediate score for a set of ngrams that match the filter. The method also includes calculating a scalar number representing a semantic meaning of the at least one skip word. The method also includes generating based on the scalar number and the intermediate score, a final score for the set of ngrams. A computer action is performed using the final score.

Abstract: A content editor for generating content including root blocks and nested blocks is disclosed. The content editor can generate a deployment that includes the content. The content editor can generate user interface code configured to edit the content. The content editor can receive updates to the content and update the root blocks and nested blocks. The updated root blocks and nested blocks can be used to generate updated content for editing and/or can be deployed to end-users.

Abstract: A system receives a request for payment of a transaction between a vendor and a consumer, and sends a first request to a database associated with the online service for historical transactions and personal attributes of the vendor concurrently with sending a second request to a number of third-party services for credit information and personal attributes of the consumer. The system receives information responsive to the first and second requests from the database and the third-party services, respectively, and obtains a risk score for the transaction based on an application of one or more risk assessment rules to the received information by a machine learning model trained with at least the historical transactions and the personal attributes of the vendor. In some aspects, the system determines whether to advance funds to the vendor, prior to requesting payment from a consumer account, based at least in part on the risk score.

Abstract: Certain aspects of the present disclosure provide techniques for handling crash events in a software application using application-agnostic machine learning models. An example method generally includes receiving a data set of crash reports from a software application for analysis. Using a first neural network, a representation of each respective crash report in the data set is generated. The data set of crash reports and a mapping between functions in the software application and a multidimensional space are input into the first neural network. Each respective crash report in the data set is classified using a second neural network and the representation of each crash report in the data set. One or more actions are taken with respect to the software application based on the classifying each respective crash report in the data set.

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: 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: 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: 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: 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: 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: 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.