Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing cross-temporal search result predictions. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform cross-temporal search result predictions using a multimodal hierarchical attention machine learning framework.

Abstract: Various embodiments of the present disclosure disclose generating contraindication alert communications. A knowledge graph data structure, including a graph-based representation associated with a user identifier and having nodes and edges, is accessed. Edge weights are adjusted based on medical data associated with the user identifier. One or more sequential traversals of the knowledge graph data structure are performed until an equilibrium condition is met. Based on determining that a subset of nodes is associated with visit tallies totaling more than a threshold proportion of all node visits associated with the one or more sequential traversals, a contraindication alert communication, which includes representation of a biological effect for the user identifier, can be generated and transmitted.

Abstract: A method and system learn functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

Abstract: A method and system to learn new forms to be incorporated into an electronic document preparation system, or to learn the behavior of existing systems, receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system include multiple analysis modules that each generate candidate functions for providing data values for the data fields of the new form. The method and system evaluate the candidate functions from each analysis technique and select the candidate functions that are most accurate based on comparisons with the training set data.

Abstract: A computer-implemented system and method for predicting rule-based compliance scenarios to implement rule-based topic determinations. A server computing device generates a compliance scenario prediction model by training a machine learning model for a topic with historical user data and cohort labels created by analyzing the scenarios in a completeness graph to predict a set of scenario cohorts that constitute a set of most probable compliance scenarios. The server computing device executes the scenario prediction model to process a user profile including data features associated with the topic to predict a scenario cohort and a compliance scenario corresponding to the predicted cohort for the user. The server computing device automatically infers one or more personalized responses to at least one question of the respective decision node based on the predicted compliance scenario.

Abstract: A method and system learn functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

Abstract: A method and system learns functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

Abstract: A method and system to learn new forms to be incorporated into an electronic document preparation system, or to learn the behavior of existing systems, receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system include multiple analysis modules that each generate candidate functions for providing data values for the data fields of the new form. The method and system evaluate the candidate functions from each analysis technique and select the candidate functions that are most accurate based on comparisons with the training set data.

Abstract: A method and system to learn new forms to be incorporated into an electronic document preparation system, or to learn the behavior of existing systems, receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system include multiple analysis modules that each generate candidate functions for providing data values for the data fields of the new form. The method and system evaluate the candidate functions from each analysis technique and select the candidate functions that are most accurate based on comparisons with the training set data.

Abstract: A method and system generate sample data set for efficiently and accurately testing a new calculation for preparing a portion of an electronic document for users of an electronic document preparation system. The method and system receive the new calculation and gather historical use data related to previously prepared electronic documents for a large number of historical users. The method and system group the historical users into groups based on which sections of a previous version of electronic document preparation software were executed for each historical user in preparing electronic documents for the historical users. The groups are then sampled by selecting a small number of historical users from each group.

Abstract: A method and system generates sufficient sample data sets for efficiently and accurately testing a new calculation for preparing a portion of an electronic document for users of an electronic document preparation system. The method and system prepares the new calculation and gathers historical user data related to previously prepared electronic documents for a large number of historical users. The method and system generates a representative value for each historical user data, based on the sections of a previous version of electronic document preparation software which were executed for each historical user in preparing electronic documents for the historical users. The method and system groups the historical users based on the hash values which indicates the historical user's behavior in the software. The groups are then sampled by selecting a small number of historical users from each group.

Abstract: A method for collecting a payment. The method includes sending, to a payor, a request for the payment, wherein the request comprises an original payment amount and an original payment due date, generating, based on a set of payment collection records from an online financial management application used by payees, a payment statistical measure representing a payment behavior of payors paying the payees, generating, based on the payment statistical measure, the original payment amount, and the original payment due date, an adjusted payment amount and a condition to qualify the adjusted payment amount for completing the payment, enabling, prior to the payor completing the payment, the payor to view the adjusted payment amount and the condition, wherein the payor completes the payment in response to at least the payor viewing the adjusted payment amount and the condition, and collecting the payment completed by the payor.

Abstract: A method and system learns new forms to be incorporated into an electronic document preparation system. The method and system receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system group the training set data into groups and sample the groups. The method and system utilize machine learning in conjunction with the sampled training set data to identify an acceptable function for each of the data fields of the new form. The grouped and sampled training set data can also be passed to a quality assurance system.

Abstract: A method and system generate sample data set for efficiently and accurately testing a new calculation for preparing a portion of an electronic document for users of an electronic document preparation system. The method and system receive the new calculation and gather historical use data related to previously prepared electronic documents for a large number of historical users. The method and system group the historical users into groups based on which sections of a previous version of electronic document preparation software were executed for each historical user in preparing electronic documents for the historical users. The groups are then sampled by selecting a small number of historical users from each group.

Abstract: A method and system generate sample data set for efficiently and accurately testing a new calculation for preparing a portion of an electronic document for users of an electronic document preparation system. The method and system receive the new calculation and gather historical use data related to previously prepared electronic documents for a large number of historical users. The method and system group the historical users into groups based on the attributes of the historical users. The groups are selected to include groups dedicated to users with rare combinations of attributes, as well as groups for users with more common combinations of attributes. The groups are then sampled by selecting a small number of historical users from each group.

Abstract: A method and system learns new forms to be incorporated into an electronic document preparation system. The method and system receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system group the training set data into groups and sample the groups. The method and system utilize machine learning in conjunction with the sampled training set data to identify an acceptable function for each of the data fields of the new form. The grouped and sampled training set data can also be passed to a quality assurance system.

Abstract: A method and system learns new forms to be incorporated into an electronic document preparation system. The method and system receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system utilize machine learning in conjunction with the training set data to identify the correct function for each of the data fields of the new form.

Abstract: A method and system to learn new forms to be incorporated into an electronic document preparation system, or to learn the behavior of existing systems, receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system include multiple analysis modules that each generate candidate functions for providing data values for the data fields of the new form. The method and system evaluate the candidate functions from each analysis technique and select the candidate functions that are most accurate based on comparisons with the training set data.

Abstract: A method and system learns functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

Abstract: Techniques are described herein for providing a real-world reward based on performance of action(s) indicated by virtual card(s) in an online card game. For instance, online offer(s) may be provided to player(s) of the online card game. Each online offer offers a real-world reward to designated player(s) in return for the designated player(s) providing a set of virtual cards that is specified by the offer. Once a designated player performs action(s) that are associated with a virtual card, the virtual card is provided to the designated player. When the designated player obtains all of the virtual cards in the set that is specified by the offer, the designated player may provide the set of virtual cards in acceptance of the online offer. Upon receipt of the set of virtual cards from the designated player, the real-world reward is provided to the designated player.