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: A method including receiving a command to display a modal dialog. The modal dialog is displayed using both first and second scrolling frames. The first scrolling frame permits scrolling when a modal dialog height exceeds a first scrolling frame constraint. The second scrolling frame permits scrolling of a content section when a content section height exceeds a second scrolling frame constraint. The first scrolling frame constraint has a first and second priorities. The second scrolling frame constraint has a third priority. An orientation of the display screen is determined as being either in a portrait orientation or a landscape orientation. Responsive to determining the physical orientation, an applicable priority that is applicable to the first scrolling frame constraint is assigned. The applicable priority is the first priority in the portrait orientation, and is the second priority in the landscape orientation. After assigning the applicable priority, the modal dialog is displayed.

Abstract: Systems and methods are disclosed for model based document image enhancement. Instead of requiring paired dirty and clean images for training a model to clean document images (which may cause privacy concerns), two models are trained on the unpaired images such that only the dirty images are accessed or only the clean images are accessed at one time. One model is a first implicit model to translate the dirty images from a source space to a latent space, and the other model is a second implicit model to translate the images from the latent space to clean images in a target space. The second implicit model is trained based on translating electronic document images in the target space to the latent space. In some implementations, the implicit models are diffusion models, such as denoising diffusion implicit models based on solving ordinary differential equations.

Abstract: Systems and methods for inferring recommendations and experiences for anonymous users of an online website are disclosed. Anonymous users of the online website are assigned anonymous user identifiers, and the browsing activity of the anonymous users is converted into features and aggregated over time. The anonymous users' interactions are monitored and used to generate labels that are combined with the feature dataset to produce a training dataset which is used to train a machine learning model. The browsing activity of an anonymous user may be converted into features and aggregated over time and fed into the trained machine learning model from which personalized experiences and recommendations may be generated and provided to the anonymous user.

Abstract: A method including receiving a selected domain from a set of domains. The method also includes selecting, based on the selected domain, a selected machine learning model from among a set of machine learning models. Each of the machine learning models is configured to receive, as input, a dataset of past time-dependent data and generate, as output, a corresponding predicted quality measure for each of a number of time periods. The selected machine learning model is trained using training data generated for an entity corresponding to the domain. The method also includes executing the selected machine learning model on the dataset to generate predicted quality measures for the time periods. The method also includes generating, using the predicted quality measures, a schedule for executing a computer process. The method also includes presenting the schedule.

Abstract: A method and system assists train a classifier model with a machine learning process. The method and system trains the classifier with a labeled training set and with an unlabeled training set. The method and system trains the classifier model to correctly classify data items that fall within a distribution of the labeled training set. The method and system trains the classifier to indicate a lack of confidence in classification for data items that do not fall within the distribution of the labeled training set.

Abstract: Disambiguity in large language models (LLMs) includes receiving an original query in a user interface, generating an ambiguity query from the original query, and sending, via an application programming interface (API) of an LLM, the ambiguity query to the LLM. The ambiguity query includes the original query and training the LLM to recognize ambiguities. The method further includes receiving, via the API and responsive to the ambiguity query, a binary response and detecting, based at least in part on the binary response, the original query as ambiguous. Disambiguity may include detecting an ambiguity location in the original query using perturbed queries and the LLM.

Abstract: Aspects of the present disclosure provide techniques for behavior prediction. Embodiments include receiving activity data of a user, identifying user sessions comprising sets of time-stamped actions in the activity data, and segmenting the activity data into subsets corresponding to the user sessions. Embodiments include providing the subsets as inputs to a hierarchical attention time-series (HAT) model comprising: a first layer that determines attention scores for respective time-stamped actions in the subsets; and a second layer that determines attention scores for the subsets based on aggregations of the attention scores for the respective time-stamped actions.

Abstract: Certain aspects of the present disclosure provide techniques for generating a metric, include receiving a rule defining one or more text strings; determining a set of transactions based on a user attribute; determining a first subset of transactions; determining a second subset of transactions; generating a first categorical distribution based on each transaction of the first subset of transactions being associated with a transaction description containing at least one text string of the one or more text strings; calculating a first unity metric based on the first categorical distribution; generating a second categorical distribution based on each transaction of the second subset of transactions being associated with a transaction description that does not contain a text string of the one or more text strings; calculating a second unity metric based on the second categorical distribution; determining a reliability metric for the rule; and providing the reliability metric.

Abstract: A method implements efficient counterfactual search. The method includes receiving a request corresponding to an input vector, processing the input vector with a model to generate an output vector that does not correspond to a selected class, and processing the input vector using a component, of a plurality of components, to generate a counterfactual vector to the selected class. The plurality of components includes a number of dimensions that is less than a number of features of the input vector. The method further includes processing the counterfactual vector to generate a recommendation and presenting the recommendation.

Abstract: A method including receiving, at a platform and from a first user using a first user device, selection of a uniform resource indicator (URI) unique to a second user using a second user device. The method also includes generating, automatically by the platform in response to receiving the URI, a conference session unique to the first user and the second user. The method also includes transmitting, automatically by the platform, a message to the second user, the message indicating that the conference session is initiated. The method also includes receiving, by the platform, an indication from the second user device that the second user joins the conference session. The method also includes joining, automatically by the platform, the first user device and the second user device in the conference session.

Abstract: A method may include executing a baseline classifier on unreviewed transaction features of an unreviewed transaction record to obtain a baseline account identifier, and executing a comparison model on (i) an unreviewed transaction vector of the unreviewed transaction record and (ii) reviewed transaction vectors to obtain comparison scores. The reviewed transaction vectors may correspond to reviewed transaction records each having a user-approved account identifier. The method may further include selecting, using the comparison scores, a reviewed transaction record. The reviewed transaction record may correspond to a comparison score. The comparison score may correspond to a user-approved account identifier of the reviewed transaction record.

Abstract: Techniques are disclosed for determining application experience of a user. One embodiment presented herein includes a computer-implemented method, which includes receiving, at a computing device, eye tracking data of a user interacting with at least a first page of an application. The computer-implemented method further includes determining, based at least on the eye tracking data, at least a current user experience regarding the first page. The computer-implemented method further includes predicting, based on evaluating the current user experience, that the user is likely to discontinue use of the application. The computer-implemented method further includes determining, based at least on the prediction, an intervention that reduces a likelihood of the user discontinuing use of the application, and interacting with the user according to the intervention.

Abstract: Described herein are example implementations for generating a perturbation seed for the perturbation of electronic data. A system obtains a plurality of datapoints (with one or more statistics calculated from the plurality of datapoints to be perturbed based on a perturbation seed). The system calculates one or more metrics from the plurality of datapoints. The system also generates, for each of the one or more metrics, a rounded metric by rounding the metric. The system further generates the perturbation seed. Generating the perturbation seed includes hashing the one or more rounded metrics. Rounding a metric may be to a defined place value (such as the second most significant place value), and a binary output of hashing the one or more rounded metrics may be converted to a number. The system may perturb one or more statistics based on the perturbation seed and output the one or more perturbed statistics.

Abstract: In general, the invention relates to a method involving allowing access to a financial application by a third-party extension based on a single license to use the financial application, where the third-party extension was developed by a third-party developer using one or more tools in a software development kit (SDK) for the financial application and where the financial application stores first transaction data obtained for a first user of the financial application, monitoring operations performed on the financial application by the third-party extension to detect operations that migrate transaction data to a competitive application, using distributed computing software adjust a risk index that is associated with the third-party extension, determining that the risk index exceeds a pre-defined threshold, and controlling future access to the financial application by the third-party extension.

Abstract: In a collection technique, a user (such as a taxpayer) provides information (such as income-tax information) by submitting an image of a document, such as an income-tax summary or form. In particular, the user may provide a description of the document. In response, the user is prompted for the information associated with the field in the document. Then, the user provides the image of a region in the document that includes the field. Based on the image, the information is extracted, and the field in the form is populated using the extracted information. The prompting, receiving, extracting and populating operations may be repeated for one or more additional fields in the document.

Abstract: A method including setting an initial lookback path length for a current path in a directed acyclic graph. The current path includes a subset of the nodes connected by a sequence of the edges. The method also includes querying, for a current lookback path length, whether a matching key is present in a transition probability dictionary (TPD). The method also includes querying, responsive to the matching key being present in the TPD for the current lookback path length, whether a matching value is present for the matching key. The matching value includes a sample path in the TPD that matches the current path. Responsive to the matching value being present in the TPD for the matching key, a next node associated with the matching value is returned. The next node is connectable in a valid operational relationship to a last node in the current path.

Abstract: A method implements image background removal. The method includes receiving an image and processing the image using a first color distance threshold to generate a first alpha mask. The method further includes processing the first alpha mask using a cartesian edge threshold and a second color distance threshold to generate a second alpha mask. The method further includes presenting the image combined with the second alpha mask in a combined image.

Abstract: Systems and methods for lean parsing are disclosed. An example method is performed by one or more processors of a system and includes retrieving form data including first sentence segments and second sentence segments, determining a first predicate structure for each of the sentence segments based on a set of operators within the first set of sentence segments, identifying known tokens within the second set of sentence segments, each of the known tokens appearing on a list of predetermined tokens, identifying new tokens within the second set of sentence segments, each of the new tokens not on the list, mapping each known and new token to at least one operator, determining a second predicate structure for each sentence segment based on the mapping, and generating a predicate argument structure incorporating the first and second predicate structures, the predicate argument structure ready for mapping to at least one machine executable function.

Abstract: A system for explaining tax questions for an electronic tax return preparation program includes a computing device having a tax logic agent and a user interface controller. The computing device executes the tax logic agent, which analyzes a completeness graph to identify a required variable. The tax logic agent also determines whether an explanation asset is indexed to the required variable in the completeness graph, and sends an identity of the required variable to a user interface controller. The user interface controller generates a tax question corresponding to the required variable. When the tax logic agent determines that an explanation asset is indexed to the required variable in the completeness graph, the computing device presents the tax question and the explanation asset. When an explanation asset is not indexed to the required variable in the completeness graph, the computing device presents the tax question without the explanation asset.