Abstract: Certain aspects of the present disclosure provide techniques and systems for automatically detecting, tracking, and processing certain information content, based on voice input from a user. A voice enabled content tracking system receives natural language content corresponding to audio input from a user. A determination is made as to whether the natural language content includes a first type of information, based on evaluating the natural language content with a first machine learning model. In response to determining the natural language content comprises the first type of information, a temporal association of the first type of information is determined, based on evaluating the natural language content with a second machine learning model, and a message including an indication of the temporal association of the first type of information is transmitted to the user.

Abstract: Transactions include text fields, such as description fields. Transactions are extracted from financial institutions using web-scraping extraction. In the process of extracting transactions, errors can be introduced into text fields, such as the inclusion of a space within a word or the removal of a space between words. A statistical approach is applied to the text fields. When two alternative text fields are presented, the alternative that statistically includes more common tokens, such as unigrams and bigrams, is chosen as the correct alternative. The incorrect alternative is replaced by the correct alternative in the text field.

Abstract: Systems and methods for selectively updating permissions associated with a cloud resource deployment are disclosed. An example method includes receiving a first request to deploy first target cloud resources based on a first specified state defined in a configuration repository, selectively updating deployment permissions associated with the first specified state and deploying the first target cloud resources based at least in part on the first specified state and the updated deployment permissions.

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: A method includes receiving a data stream comprising content generated by an application executing on a user device. The data stream is received from a guidance service that is separate from the application. The data stream is processed using a set of machine learning models to identify a first set of artifacts within the content. A first state of the application is identified based on the first set of artifacts. First transition data is identified in a logic flow of the application. The first transition data corresponds to transitioning from the first state to a second state of the application. Based on the first transition data, first guidance data is generated that describes user input for transitioning the application from the first state to the second state. The first guidance data is sent to the user device, where it is separately presented from the application by the guidance service.

Abstract: A method may include selecting a cohort of entities for migration from a source storage repository to a target storage repository, obtaining a mapping between a source storage schema of the source storage repository to a target storage schema of the target storage repository, and migrating data for the entities in the cohort. Migrating the data of an entity may include copying, without locking the data in the source storage repository and in the target storage repository, the data from the source storage repository to the target storage repository, verifying, while the data is locked, that the data in the source storage repository is the same as the data in the target storage repository, changing, while the data in the source storage repository and the target storage repository is locked, an entity pointer for the entity to the target storage repository based on the verifying, and unlocking the data.

Abstract: A method implements hybrid artificial intelligence generated actionable recommendations. The method includes processing an event to identify an action of an event action set. The event includes an event value. The method further includes processing the event action set to generate an objective value, corresponding to the action, and a probability, corresponding to the action, and to form a model action set from the event action set. The method further includes filtering the model action set using action rule data and rule user data to generate a filtered action set. The method further includes processing, using the objective value and the probability, the filtered action set with an optimization controller to generate suggested action sets from which a selected action set is selected. The selected action set corresponds to a combined action value that satisfies the event value.

Abstract: This disclosure relates to systems and methods for providing user content on a social network. In some aspects, the social network receives, over a communications network from a first computing device associated with a first user of the social network, a transmission including a post to be published on the social network. The social network detects, in the post, goods or services sought or inquired about by the first user, and determines a proximity of the first user. The social network identifies one or more other users of the social network located within a geographical area or the proximity associated with the first user, and presents the post only to the one or more identified users of the social network.

Abstract: Systems and methods of the present disclosure provide processes for determining how much to adjust machine-learning parameter values in a direction of a gradient for gradient-descent steps in training processes for machine-learning models. Current parameter values of a machine-learning model are vector components that define an initial estimate for a local extremum of a cost function used to measure how well the machine-learning model performs. The initial estimate and the gradient of the cost function for the initial estimate are used to define an auxiliary function. A root estimate is determined for the auxiliary function of the gradient. The parameters are adjusted in the direction of the gradient by an amount specified by the root estimate.

Abstract: This disclosure relates to systems and methods for processing electronic payments for customer purchases. In some implementations, a system receives a payment request from a merchant, and transmits payment information to a mobile computing device associated with the merchant. The payment information includes a transaction ID to identify the purchase, a purchase amount, and instructions that cause the mobile computing device to activate a card reader, present a notification of the purchase amount to the customer, receive an acceptance of the purchase amount from the customer, and authorize the card reader to process an electronic payment for the purchase amount. The system receives payment confirmation from the mobile computing device, and provides the parameter confirmation to the merchant.

Abstract: A method collects word-based data corresponding to a first identifier. A first phrase vector is generated for the first identifier by extracting frequency data from the word-based data. A similarity metric is generated corresponding to the first identifier and a second identifier by comparing the first phrase vector of the first identifier to a second phrase vector of the second identifier. A tuple is generated that includes the first identifier and the second identifier using the similarity metric. A machine learning model is trained with the tuple to generate an embedded vector corresponding to the first identifier.

Abstract: A method for generating a synthetic dataset involves generating discretized synthetic data based on driving a model of a cumulative distribution function (CDF) with random numbers. The CDF is based on a source dataset. The method further includes generating the synthetic dataset from the discretized synthetic data by selecting, for inclusion into the synthetic dataset, values from a multitude of entries of the source dataset, based on the discretized synthetic data, and providing the synthetic dataset to a downstream application that is configured to operate on the source dataset.

Abstract: Systems and methods for dynamic presentation of content items in a user interface are disclosed. An example method includes presenting a plurality of content items on the user interface in a predetermined geometric arrangement, receiving a user selection of a first content item of the plurality of content items, in response to receiving the user selection of the first content item, expanding the first content item while moving the first content item to a predetermined focus position within the geometric arrangement, and presenting, in the expanded first content item, additional information associated with the first content item, where the additional information is not displayed before receiving the user selection of the first content item.

Abstract: Systems and methods for a boosted Latent Dirichlet Allocation model with predefined clusters and repelling force. Herein, clusters are pre-populated with seed words with amplified weights to predefine the characteristics of clusters. A repel force is also applied while assigning words to specific clusters in order to assign words more accurately to clusters in instances where clusters have similar characteristics. In addition to generating improved clustering of words, systems and methods may analyze words within queries to provide explainability output related to the performance of content or data.

Abstract: Systems and methods that may be used to implement overlapping data caching for object application program interfaces (APIs). A unique identifier may be assigned to each sub-component of an object API request. The unique identifiers may be used to determine if sub-components of one object API request overlaps with a sub-component of a prior different request such that a data response for the prior different request can be retrieved from the cache and used as part of the data response for the current object API request.

Abstract: A method including monitoring, using a machine learning model, network events of a network. The machine learning model generates fraud scores representing a corresponding probability that a corresponding network event is fraudulent. The method also includes detecting a failure of the machine learning model to generate, within a threshold time, a given fraud score for a given network event. The method also includes determining, by the machine learning model and after the threshold time, the given fraud score. The method also includes logging, responsive to detecting the failure, the given network event in a first table, including logging the given fraud score. The method also includes determining a metric based on comparing the first table to a second table which logs at least the given fraud score and the fraud scores. The method also includes generating an adjusted machine learning model based on the metric.

Abstract: A method of blank detection involves receiving a document from a user, where the document includes derived text; applying a trained blank detection model to the document to make a first prediction, where the first prediction indicates whether at least one field in the document is blank; comparing the first prediction with a second prediction, where the second prediction is made by an extraction model; and extracting the at least one field using the extraction model.

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: A method including receiving a first command including both a data extraction expression and a first report configuration expression. The data extraction expression includes program code for extracting fields of a dataset of a data source. The first report configuration expression includes program code configured to populate cells of first dimensions of a first report and to generate a first tree including subset nodes including records of the dataset. The first command is executed by executing the data extraction expression on the dataset to generate the records. Executing the first command also includes executing the first report configuration expression on the records to generate the first tree. Executing the first command also includes populating, using the first report configuration expression and the first tree, the cells. Executing the first command also includes generating, in response to receiving the first command and by traversing the first tree, the first report.