Abstract: A method comprising generating, during multiple user sessions of a first user with a software application, first clickstream data from the multiple user sessions, and extracting, from the first clickstream data, a first plurality of task instances of the first user performing a first plurality of tasks. The method also includes decomposing, from the first clickstream data, each task instance of the first plurality of task instances into a first plurality of steps to obtain a first plurality of decomposed task instances. The first plurality of steps in the first plurality of decomposed task instances are each associated with a timestamp. The method further includes training a first user model with the first plurality of decomposed task instances to learn a user optimal order to perform the first plurality of tasks and presenting, to the first user, the user optimal order to perform the first plurality of tasks.

Abstract: Systems and methods are described for automatically documenting queries and dynamically populating interactive graphical user interfaces with query recommendations. A computing system receives an initial query from an interactive graphical user interface and asynchronously parses the query for strings matching predetermined phrases. In response to determining that the initial query recites strings matching the predetermined phrases, the system extracts metadata and identifies a table name in the initial query, and modifies a table data in key-value data structure corresponding to the table. Subsequent queries related to the initial query cause the system to asynchronously populate the graphical user interface with query recommendations related to the initial query.

Abstract: A method comprising generating, during multiple user sessions of a first user with a software application, first clickstream data from the multiple user sessions, and extracting, from the first clickstream data, a first plurality of task instances of the first user performing a first plurality of tasks. The method also includes decomposing, from the first clickstream data, each task instance of the first plurality of task instances into a first plurality of steps to obtain a first plurality of decomposed task instances. The first plurality of steps in the first plurality of decomposed task instances are each associated with a timestamp. The method further includes training a first user model with the first plurality of decomposed task instances to learn a user optimal order to perform the first plurality of tasks and presenting, to the first user, the user optimal order to perform the first plurality of tasks.

Abstract: Certain embodiments of the present disclosure provide techniques for identifying and blocking anomalous transactions within a computing system. An example method generally includes selecting a set of accounts for analysis. For each respective account, an anomaly score is calculated based on an account number associated with the respective account and transaction amounts associated with the respective account. An aggregated anomaly score is generated for each respective account provider of a plurality of account providers based on the anomaly score associated with each respective account. The aggregated anomaly score for each respective account provider is normalized based on a historical minimum and historical maximum anomaly score for the respective account provider. One or more account providers that are potential targets of anomalous activity are identified.

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: 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: Techniques for detecting fraud may include obtaining a merchant's financial data; determining, via a machine learning model, a first prediction of the merchant's industry; generating a first probability matrix based on the first prediction and the declared information regarding the merchant's industry; determining, via the machine learning model, a second prediction of the merchant's industry; generating a second probability matrix based on the second prediction and the declared information regarding the merchant's industry; obtaining a declared industry of a subject merchant in a runtime environment; determining, via the machine learning model, a predicted industry for the subject merchant; obtaining, based on the declared industry and the predicted industry of the subject merchant, a first value from the first probability matrix and a second value from the second probability matrix; and labeling the subject merchant for further investigation.

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: Systems and methods are described for automatically documenting queries and dynamically populating interactive graphical user interfaces with query recommendations. A computing system receives an initial query from an interactive graphical user interface and asynchronously parses the query for strings matching predetermined phrases. In response to determining that the initial query recites strings matching the predetermined phrases, the system extracts metadata and identifies a table name in the initial query, and modifies a table data in key-value data structure corresponding to the table. Subsequent queries related to the initial query cause the system to asynchronously populate the graphical user interface with query recommendations related to the initial query.

Abstract: Systems and methods are described for training a large language model with query auto-completion training data and automatically generating query auto-completion training data in an interactive GUI. A computing system continuously trains and refines a large language model utilizing masking techniques to on complex software-related queries. The computing system is further configured to utilize the large language model to provide complex software-related query suggestions to users operating a graphical user interface real-time.

Abstract: Systems and methods for k-anonymizing a corpus of documents using linguistic similarities and embeddings distances between words. For instance, a word pair is selected based on linguistic similarity (e.g., belonging to the same part of speech) and small embeddings distance. For the selected word pair, a plurality of words is retrieved, also based on linguistic similarity to, and embeddings distances from, the selected word pair. Out of the plurality of words, a third word is identified that has a closer linguistic similarity to the word pair and also has smaller embeddings distances from the word pair. Each word in the word pair is then replaced by the third word. The process is repeated until k-anonymity is achieved.

Abstract: A processor may receive a request to display a user interface (UI) for a user account. The processor may determine a context for the UI from information specific to the user account. The processor may select one of a plurality of UI elements based on the determining. In some embodiments, the selecting may include evaluating a value function taking the information specific to the user account and information about the context as inputs. The processor may cause the UI including the selected one of the plurality of UI elements to be displayed in response to the request.

Abstract: Certain aspects of the present disclosure provide techniques for placing targeted messages in communications within a software application using machine learning models. An example method generally includes retrieving, from a repository, a data set of targeted messages. For each respective targeted message in the data set of targeted messages, an effectiveness score for a party associated with the respective targeted message, a distance score between the party associated with the respective targeted message and a host party, and a match score between the party associated with the respective targeted message and the host party. Based on the effectiveness score, the distance score, and the match score for each respective targeted message in the data set of targeted messages, a message is selected to be included in one or more communications by the host party. The one or more communications including the selected message are generated and output for transmission.

Abstract: Certain aspects of the present disclosure provide techniques for updating a policy of an agent, including receiving a first transaction file associated with an entity; predicting, by the agent, an expected reward for each respective string of a plurality of strings associated with the first transaction file based on a policy of the agent, wherein the policy is determined based on a context comprising at least an attribute of the entity; determining a first string based on a highest expected reward; providing, to an environment, the first string; receiving a response to the first string, wherein the response comprises an actual reward; and updating the policy of the agent based on the response to the first string.

Abstract: Certain aspects of the present disclosure provide techniques for providing an indicator to an inexperience business owner to enter transactions into an accounting system. Transaction data from applications is grouped into two groups based on experience of each group. Transaction data from an experienced group will have transactions with accounting professionals, while an inexperienced group may not, or have fewer such transactions. A transaction is decomposed into at least two transaction events, each with showing two accounts per transaction event, as per double entry accounting rules. Probability values for each transaction event are determined based on the accounts involved: probability value=p(account_1=account_type_1, credit/debit amount, account_2=account_type_2, credit/debit amount|previous event account_3=account_type_3, credit/debit amount, account_4=account_type_4, credit/debit amount).

Abstract: Certain embodiments of the present disclosure provide techniques for identifying and blocking anomalous transactions within a computing system. An example method generally includes selecting a set of accounts for analysis. For each respective account, an anomaly score is calculated based on an account number associated with the respective account and transaction amounts associated with the respective account. An aggregated anomaly score is generated for each respective account provider of a plurality of account providers based on the anomaly score associated with each respective account. The aggregated anomaly score for each respective account provider is normalized based on a historical minimum and historical maximum anomaly score for the respective account provider. One or more account providers that are potential targets of anomalous activity are identified.

Abstract: Aspects of the present disclosure provide techniques for database documentation propagation. Embodiments include scanning a log comprising a plurality of database queries to identify one or more database queries of the plurality of database queries, the one or more database queries being associated with generating a new table of a database based on information in an existing table of the database. Embodiments include generating, based on the one or more database queries identified during the scanning, a directed acyclic graph (DAG) comprising: a first vertex representing the existing table; a second vertex representing the new table; and a directed edge connecting the first vertex to the second vertex. Embodiments include obtaining documentation associated with the existing table. Embodiments include propagating, based on the DAG, at least a subset of the documentation associated with the existing table to the new table.

Abstract: Aspects of the present disclosure provide techniques for database documentation propagation. Embodiments include scanning a log comprising a plurality of database queries to identify one or more database queries of the plurality of database queries, the one or more database queries being associated with generating a new table of a database based on information in an existing table of the database. Embodiments include generating, based on the one or more database queries identified during the scanning, a directed acyclic graph (DAG) comprising: a first vertex representing the existing table; a second vertex representing the new table; and a directed edge connecting the first vertex to the second vertex. Embodiments include obtaining documentation associated with the existing table. Embodiments include propagating, based on the DAG, at least a subset of the documentation associated with the existing table to the new table.