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: A visual-based classification model influenced by text features as a result of the outputs of a text-based classification model is disclosed. A system receives one or more documents to be classified based on one or more visual features and provides the one or more documents to a student classification model, which is a visual-based classification model. The system also classifies, by the student classification model, the one or more documents into one or more document types based on one or more visual features. The one or more visual features are generated by the student classification model that is trained based on important text identified by a teacher classification model for the one or more document types, with the teacher classification model being a text-based classification model. Generating training data and training the student classification model based on the training data are also described.

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 transforming a data visualization are disclosed. An example method includes presenting the data visualization on a first page of a display of the computing device, the data visualization representing at least a first portion of a data set, receiving a visualization transformation command from a user, in response to receiving the visualization transformation command, navigating to a second page of the display, and, during the navigation, transforming the data visualization based at least in part on the first portion of the data set, wherein the transformation includes at least enlarging the data visualization and rotating the data visualization from a first orientation to a second orientation.

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.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

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: 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.