Abstract: A method of training machine learning models (MLMs). An issue vector is generated using an issue MLM to generate a first output including first embedded natural language issue statements. An action vector is generated using an action MLM to generate a second output including related embedded natural language action statements. The issue and action MLMs are of a same type. An inner product of the first and second output is calculated, forming a third output. The third output is processed according to a sigmoid gate process to predict whether a given issue statement and corresponding action statement relate to a same call, resulting in a fourth output. A loss function is calculated from the fourth output by comparing the fourth output to a known result. The issue MLM and the action MLM are modified using the loss function to obtain a trained issue MLM and a trained action MLM.

Abstract: This disclosure provides systems, methods and apparatuses for recommending items to users of a recommendation system. In some implementations, the recommendation system determines a plurality of contribution values based on interactions between a plurality of users and a plurality of items. Each of the plurality of contribution values represents a confidence level that a respective user prefers a respective item. The recommendation system further determines a capacity of each of the plurality of items. The capacity of each item represents a maximum number of users to which the item can be recommended. The recommendation system recommends one or more items of the plurality of items to each of the plurality of users based at least in part on the plurality of contribution values and the capacities of the plurality of items.

Abstract: A method and system for classification of cyber-threats is provided. The method includes receiving a request for classifying a cyber-threat detected by a cyber-security system, wherein the request includes initial information about the detected cyber-threat; enriching the initial information about the detected cyber-threat to provide textual information about at least one perceived threat related to the detected cyber-threat; and classifying each of the at least one perceived threat into a security service, wherein the classification is performed based on the respective textual information.

Abstract: A method for classifying organizations involves obtaining, for an unknown organization, transactional data representing a multitude of transactions. The transactional data comprises a descriptive text for each of the multitude of transactions. The method further involves processing the descriptive text for each of the multitude of transactions to obtain one vector representing the unknown organization, categorizing the unknown organization using a classifier applied to the vector, and identifying a software service for the unknown organization, according to the categorization.

Abstract: A method may include receiving, for a package, shipment details including attributes, obtaining, for a subset of the attributes, logistic preferences, applying the logistic preferences to the shipment details to obtain modified shipment details, training a classifier using shipment transactions each including values for the attributes and labeled with a vendor logistic service, generating, by applying the classifier to the modified shipment details, scores for vendor logistic services, and recommending a vendor logistic service from the vendor logistic services using the scores.

Abstract: A method may include generating, using a flow proportionalized graph, scores for platform sellers of an online platform. The flow proportionalized graph may include nodes corresponding to the platform sellers and buyers, and edges each connecting a buyer node corresponding to a buyer initiating a monetary transfer and a platform seller node corresponding to a platform seller receiving the monetary transfer. Each edge may have a weight that is a proportion of total monetary transfers by the buyer received by the platform seller. The method may further include matching, using the scores and a seller similarity metric, a non-platform seller with a platform seller, receiving a scenario for the platform seller to sell an item of the non-platform seller via the online platform, and generating a prediction regarding an outcome of the scenario by applying a model to scenarios.

Abstract: Systems and methods that may be used to allow married couples to prepare separate individual tax returns while also being able to evaluate the merits of filing a joint return in a manner that does not breach each spouse's financial privacy.

Abstract: Systems and methods may be used to generate and use a merchant community graph generated based on merchant financial transaction data. Connections between merchants and other data within the merchant community graph can be used to detect fraud, target product offerings and or other advertisements, detect similar communities, generate dynamic attributes that may be used to develop machine learning models, and develop new user interfaces (UIs) and other features of an information service.

Abstract: Systems and methods for validating credentials are disclosed. One example method, performed by one or more processors of a computing device associated with a neural network, includes training the neural network to infer validity information for encrypted credentials received from a credential source, wherein the validity information is inferred without decrypting the encrypted credentials, receiving a first encrypted credential from the credential source, generating an encrypted validity indicator for the first encrypted credential based on the validity information inferred by the neural network, and providing the encrypted validity indicator to the credential source.

Abstract: A method for privacy preserving synthetic string generation using recurrent neural networks includes receiving input data that includes a plurality of strings with private information. A neural network model is trained using the plurality of strings. The neural network model includes a recurrent neural network (RNN). An anonymous string is generated with the neural network model after training the neural network model with the plurality of strings from the input data. The anonymous string is validated to preclude the private information from the anonymous string. Anonymous data is transmitted that includes the anonymous string and precludes the private information in response to a request for the anonymous data.

Abstract: Certain aspects of the present disclosure provide techniques for detecting personally identifiable information, including: receiving a plurality of text strings, each text string of the plurality of text strings associated with a user support session; providing the plurality of text strings to one or more bidirectional long short-term memory (BiLSTM) neural network models; receiving output from the one or more BiLSTM neural network models, the output indicating one or more text data elements in the plurality of text strings comprising predicted personally identifiable information; redacting the one or more text data elements comprising the predicted personally identifiable information from the plurality of text strings to form redacted text strings; and providing, to a data repository, the redacted text strings.

Abstract: A method may be used to predict a business' category by analyzing the business' vendors. A neural network architecture may be trained via supervised learning to predict categories for businesses based on listed vendors. The neural network may be used to classify uncategorized businesses within an accounting software database. A list of factors associated with a business' success may be generated by analyzing, aggregating and ranking factors determined to be relevant to a business based on its categorization. The factors associated with the business' success may be related to the products and/or services offered by the business and the format of which those products and/or services are offered by the business. The factors may also be related to the products and/or services purchased by the business from a vendor and the format of which those products and/or services are purchased from the vendor.

Abstract: A method of training machine learning models (MLMs). An issue vector is generated using an issue MLM to generate a first output including first embedded natural language issue statements. An action vector is generated using an action MLM to generate a second output including related embedded natural language action statements. The issue and action MLMs are of a same type. An inner product of the first and second output is calculated, forming a third output. The third output is processed according to a sigmoid gate process to predict whether a given issue statement and corresponding action statement relate to a same call, resulting in a fourth output. A loss function is calculated from the fourth output by comparing the fourth output to a known result. The issue MLM and the action MLM are modified using the loss function to obtain a trained issue MLM and a trained action MLM.

Abstract: A method for detecting fraud rings involves clustering unknown users into unknown user clusters based on a grouping attribute. The method further involves, for each of the unknown user clusters, determining aggregated features including at least one quantification of at least one homogeneity attribute across the unknown users in the unknown user cluster. The method also involves, for each of the unknown user clusters, determining a predictive suspiciousness score based on the aggregated features, determining that at least one of the unknown user clusters is suspicious based on the determined predictive suspiciousness scores, and taking a protective action for the at least one suspicious unknown user cluster.

Abstract: A method for recommending offerings to a business may include: receiving a request for recommended business offerings from a device; receiving business data associated with a business from the device, the business data comprising invoice data associated with the business; embedding the business data to a vector space to obtain a business vector, the vector space comprising a plurality of other vectors associated with other businesses; calculating a relation metric between the business vector and a vector of the plurality of other vectors, the vector being associated with a second business, the relation metric representing a degree of relation between the business and the second business; determining that the relation metric is above a pre-defined threshold value; and responsive to the determining, sending business data associated with the second business to the device, the business data associated with the second business comprising invoice data associated with the second business.

Abstract: In one aspect, the present disclosure relates to a method of generating business descriptions performed by a server, said method may include: receiving a plurality of invoices, each invoice being associated with a business of a plurality of businesses; extracting a plurality of texts from the plurality of invoices; embedding the plurality of texts to a vector space to obtain a plurality of invoice vectors; generating a plurality of clusters in the vector space, each cluster of the plurality of clusters comprising at least one invoice vector of the plurality of invoice vectors; generating a description for a cluster, the description for the cluster representing all invoice vectors assigned to the cluster; for each business of the plurality of businesses that has at least one invoice vector assigned to the cluster, associating the business with the description; and indexing the plurality of businesses within a database by the generated descriptions.

Abstract: Systems and methods that may be used to automatically generate inventory templates for use with an accounting platform. The automatically generated templates may be for a first user within a particular industry and may be based on established inventory trees of other system users within the same industry that have similar demographics of the first user.

Abstract: A method may include extracting, from a transaction, a routing transit number (RTN) and an account number, embedding the RTN to obtain an RTN vector, embedding the account number to obtain an account number matrix, combining, using a trained machine learning model, the RTN vector and the account number matrix to obtain a combined matrix, and classifying the account number as invalid. The classifying may include applying the trained machine learning model to the combined matrix.

Abstract: Certain aspects of the present disclosure provide techniques for automatic form detection and completion. When a form is first identified by systems and methods disclosed herein, an image of the form is acquired and the form is uniquely identified. The image of the form is presented to a user, who clicks on areas of the form image and provides data into the areas so chosen. The coordinates of the user input in the form image, data provided by the user, and attributes of that data, are stored. Once a sufficient number of users, determined by a confidence interval threshold, have used the form in this manner, the user data input is clustered around user defined input coordinates. From the user data, a data schema of the user input is determined, and the data schema is assigned to the related user defined input coordinates.

Abstract: Certain aspects of the present disclosure provide techniques for detecting personally identifiable information, including: receiving a plurality of text strings, each text string of the plurality of text strings associated with a user support session; providing the plurality of text strings to one or more bidirectional long short-term memory (BiLSTM) neural network models; receiving output from the one or more BiLSTM neural network models, the output indicating one or more text data elements in the plurality of text strings comprising predicted personally identifiable information; redacting the one or more text data elements comprising the predicted personally identifiable information from the plurality of text strings to form redacted text strings; and providing, to a data repository, the redacted text strings.