Abstract: Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion.

Abstract: Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for ranking entities provided in response to a search query by identifying one or more categorical identifiers based on a semantic similarity between a query embedding and a plurality of categorical description embeddings, generating a predicted distance preference for the search query based on a location associated with a querying user, identifying one or more entities based on the location associated with the querying user, the predicted distance preference, and entity activity data entries comprising a plurality of categorical descriptions matching the identified one or more categorical identifiers.

Abstract: Various embodiments of the present disclosure provide query processing techniques for resolving queries in a complex search domain to improve upon traditional search resolutions within such domains. The techniques may include generating a keyword and an embedding representation for an agnostic search query. The keyword representation may be compared against source text attributes within one or more domain channels to generate a plurality of keyword similarity scores between the search query and features within a search domain. The embedding representation may be compared against source embedding attributes within the one or more domain channels to generate a plurality of embedding similarity scores between the search query and the features within the search domain. The keyword and embedding similarity scores may be aggregated to generate aggregated similarity scores for identifying an intermediate query resolution for the search query. The intermediate query resolution may be leveraged to resolve the query.

Abstract: Various embodiments of the present disclosure provide an interactive map-based visualization system related to multi-channel search for search domains to improve upon traditional search resolutions within such domains. The techniques may include receiving a user interface request that comprises (i) character-level text input related to a search query via a user interface of a user device and (ii) filter metadata for a user identifier associated with the user interface request, generating a set of query result data objects for the user interface request by correlating the character-level text input to at least one domain knowledge profile, and generating a set of filtered query result data objects for the user interface request by filtering the set of query result data objects using the filter metadata. In some examples, the techniques may include initiating a rendering of a set of selectable graphical element options that are correlated to a real-time map visualization.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating domain-specific queries that are semantically similar to a search query by spell-correcting and tokenizing a search query, and then generating, using an embeddings dictionary data object associated with one or more domain vocabulary data objects, queries semantically related to the search query based on proximity of one or more similar embeddings to an embedding associated with the tokenized query within a domain vector space.

Abstract: Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion.

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.