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: Systems and methods for generating a contextually adaptable classifier model are disclosed. An example method is performed by one or more processors of a system and includes obtaining a dataset, feature values, and labels, transforming each datapoint into a natural language statement (NLS) associating the datapoint's feature values and label with feature identifiers and a label identifier, generating a feature matrix for each NLS, transforming the feature matrix into a global feature vector, generating a target vector for each NLS, transforming the target vector into a global target vector having a same shape, and generating, using the vectors, a similarity measurement operation, and a loss function, a classifier model trained to generate a compatibility score predictive of an accuracy at which the classifier model can classify given data based on at least one of a different feature characterizing the given data or a different label for classifying the given data.

Abstract: A computer-implemented system and method for predicting and flagging an anomaly entered in a digital form. A server computing device classifies a plurality of data fields of the digital form to identify a set of non-zero value data fields; and obtains an anomaly detection model comprising a statistical tree structure associated with the data field of the digital form. The server computing device receives datasets including a target value of a data field and values of a set of cohorting data features; traverses a statistical tree structure of the anomaly detection model with the target dataset to form a set of target cohorts to determine a target statistic value for the data field; flags the data field value of the target dataset as an anomaly item; and generates one or more confidence scores for a runtime prediction based on one or more variance changes for the data field.

Abstract: A method including isolating a protocol language of a data set comprising a text structure representing data regarding a network communication procedure between a plurality of user devices, wherein the protocol language comprises a pattern for implementing the network communication procedure; generating a document from the data set, wherein the document includes a text structure, organizing, in light of the protocol language, the text structure into a natural language scheme; and detecting, using the natural language scheme, insights in the document.

Abstract: A method includes detecting, in a written electronic communication, an input sentence satisfying a readability metric threshold, and processing, by a sentence transformer model responsive to the input sentence satisfying the readability metric threshold, the input sentence to output a suggested set of sentences. The method further includes evaluating the first suggested set of sentences along a set of acceptability criteria, and determining, based on the evaluating, that the set of acceptability criteria is satisfied. The method further includes modifying, based on determining that the set of acceptability criteria is satisfied, the written electronic communication with the suggested set of sentences to obtain a modified written electronic communication, and storing the modified written electronic communication.

Abstract: This disclosure relates to generating a comprehensive set of synthetic utterances. An example system is configured to provide an input utterance to a plurality of synthetic utterance generation pipelines in parallel. Each of the plurality of synthetic utterance generation pipelines include one or more utterance synthesizers. For example, one or more pipelines may use a synthesizer chain that includes a plurality of synthesizers in parallel. The plurality of synthetic utterance generation pipelines generates synthetic utterances, which may be stored in a database after evaluating the similarity between the original input utterance and each resulting synthetic utterance. For example, a synthetic utterance may be retained if the cosine similarity between the input and synthetic utterances is less than a predetermined threshold.

Abstract: Systems and methods that process, classify, and provide intelligent insights related to received documents such as notice documents in real-time. The system and methods leverage a novel framework of artificial intelligence and machine learning techniques to identify a requirement in the document (e.g., a government notice) and generate actionable suggestions thereto.

Abstract: A method including training, using training data including a first ontological hierarchical level, trained machine learning models (MLMs) to predict a first output type including a second ontological hierarchical level different than the first ontological hierarchical level. The method also includes generating instances of the first output type by executing the trained MLMs on unknown data including the first ontological hierarchical level. Outputs of the trained MLMs include the instances at the second ontological hierarchical level. The method also includes training, using the instances, a voting classifier MLM to predict a selected instance from the instances. The voting classifier MLM is trained to predict the selected instance to satisfy a criterion including a third ontological hierarchical level different than the first ontological hierarchal level and the second ontological hierarchical level.

Abstract: The one or more embodiments provide for a method. The method includes receiving a digital image stored in an object notation data format. The method also includes converting the digital image into hypertext markup language (HTML) data format. The method also includes caching the HTML data format to generate cached HTML data. The method also includes receiving a first request to reload the digital image. The method also includes rendering, responsive to receiving the first request to reload, the digital image using the cached HTML data to generate a rendered digital image.

Abstract: A transcript of an audio conversation between multiple users (e.g., two users) is generated. The transcript is displayed in real time within a VR environment as the conversation takes place. A virtual selection tool is displayed within the VR environment to allow for a selection of different portions of the transcript. In addition, a virtual keyboard and or virtual panels with characters may be displayed and the virtual selection tool may be used to make selections from these displays as well. These selections are used to generate new text. The new text may form part of a user's notes of the conversation or an entry for a text field within the VR environment.

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: 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: 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: 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 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.