Abstract: Various embodiments of the present disclosure provide automated data compliance techniques for complex access controlled datasets subject to a plurality of data access constraints. Some of the techniques may include generating, using one or more natural language models, entity-relationship data for an access controlled dataset and generating a knowledge graph based on the entity-relationship data. The knowledge graph includes a plurality of vertices connected by a plurality of edges that may be traversed to identify a data access condition indicative of a data access violation or a data coverage violation. Some of the techniques may include generating, using the knowledge graph, a natural language condition description based on the data access condition and providing a condition alert indicative of the natural language condition description.

Abstract: Various embodiments of the present disclosure provide query processing techniques for generating optimized query results. The techniques include generating using a machine learning framework, one or more predictions for a natural language query. The one or more predictions may include an intent prediction and an event prediction. The technique may include generating an intent classification for the natural language query based on the intent prediction and the event prediction. The techniques may include in response to the intent classification corresponding to a target query intent: generating, a plurality of candidate data objects based on an identifier associated with the natural language query, identifying one or more relevant data objects from the plurality of candidate data objects based on a relevancy score for each of the plurality of candidate data object; and providing, via a user interface, a natural language query result.

Abstract: Various embodiments of the present disclosure provide a refined query resolution based on relevant search clustering using real time data. The techniques may include receiving a prefix text input associated with a search query, identifying a preceding text input associated with a historical search query preceding the search query, identifying a plurality of relevant search clusters from a clustered hierarchical tree based on the prefix text input and the preceding text input, identifying one or more search labels for the search query from the plurality of relevant search clusters, and initiating the performance of a query resolution operation for the search query based on the one or more search labels.

Abstract: Various embodiments of the present disclosure provide automated data compliance techniques for complex access controlled datasets subject to a plurality of data access constraints. Some of the techniques may include generating, using one or more natural language models, entity-relationship data for an access controlled dataset and generating a knowledge graph based on the entity-relationship data. The knowledge graph includes a plurality of vertices connected by a plurality of edges that may be traversed to identify a data access condition indicative of a data access violation or a data coverage violation. Some of the techniques may include generating, using the knowledge graph, a natural language condition description based on the data access condition and providing a condition alert indicative of the natural language condition description.

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 computer interpretation techniques for implementing a query resolution process to improve upon traditional search resolutions within a search domain. The techniques may include receiving a plurality of interaction data objects comprising a plurality of assessment codes and a plurality of intervention codes. The techniques may include generating a frequency distribution comprising a plurality of code pairs based on a plurality of cooccurrences of the plurality of assessment codes and the plurality of intervention codes within the plurality of interaction data objects. The techniques may include generating, using the frequency distribution, a cross-code dataset comprising one or more mapped code pairs from the plurality of code pairs based on a threshold cooccurrence value. The techniques may include initiating the performance of a query resolution operation for a search query based on the cross-code dataset.

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: Systems and methods for pill identification based on image and user claims data are disclosed. A pill identification request, including one or more images of a pill and a user identifier of a user associated with the pill, is received. A first machine learning system is used to generate one or more image embeddings based on the one or more images. The user identifier is used to retrieve claims data of the user, and the claims data are encoded to generate a claims embedding. A second machine learning system is used to identify the pill based on the one or more image embeddings and the claims embedding. A response to the pill identification request is generated based on the identifying.

Abstract: A distributed messaging platform may employ a trial system to provide a micro-randomized trial by identifying, over an electronic network, a plurality of user devices associated with a plurality of subjects; and at each of a plurality of intervals, and assigning the subjects into a respective distribution over a plurality of cohorts, such that the subjects are re-assigned at each fixed interval. The trial system may further perform operations that include, for each cohort, causing a respective subset of the user devices associated with subjects assigned to the cohort to output a respective treatment, the treatment for each cohort being different from each other; and collecting, from the user devices, response data of the subjects; such that, as the subjects are re-assigned to different cohorts across the different intervals, the user devices output different treatments corresponding to the different cohorts.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for retrieving relevant items for user queries by generating, using a search engine machine learning model, a prediction-based action for the query input wherein query input embeddings of the query input are generated. For each query input embedding, a k-Nearest-Neighbor (KNN) search is performed with respect to search engine repository item embeddings to generate initial search results, and for each initial set result, performing N hops within a semantic graph starting from nodes associated with the initial search result to generate related search results. The search engine machine learning model is trained by generating a search engine repository item embeddings according to embedding techniques for respective content categories and generating the semantic graph based at least in part on a measure of similarity for pairs of search engine repository item embeddings.

Abstract: Systems and methods for improved spelling checking are disclosed. A method includes receiving a search query from a user device and determining that the search query does not exist in a data store that stores (a) a corpus of correctly spelled words or (b) forced correction mapping data. The method further includes, in response to the determining, determining a plurality of suggested search queries generated by a plurality of respective spell corrector models, selecting a suggested search query determined using a spell corrector model from the plurality of spell corrector models based on at least one of a frequency of the suggested search query in historical search data or a weightage associated with the spell corrector model, and causing the suggested search query to be displayed on the user device.

Abstract: Various embodiments of the present disclosure provide query processing techniques for generating optimized query results. The techniques include generating using a machine learning framework, one or more predictions for a natural language query. The one or more predictions may include an intent prediction and an event prediction. The technique may include generating an intent classification for the natural language query based on the intent prediction and the event prediction. The techniques may include in response to the intent classification corresponding to a target query intent: generating, a plurality of candidate data objects based on an identifier associated with the natural language query, identifying one or more relevant data objects from the plurality of candidate data objects based on a relevancy score for each of the plurality of candidate data object; and providing, via a user interface, a natural language query result.

Abstract: Various embodiments of the present disclosure provide computer interpretation techniques for implementing a query resolution process to improve upon traditional search resolutions within a search domain. The techniques may include generating a plurality of interaction embeddings for a plurality of textual descriptions corresponding to a plurality of interaction codes identified within an interaction dataset and a plurality of taxonomy embeddings for a plurality of taxonomy categories identified within a taxonomy dataset. The techniques may include generating similarity scores for a plurality of description-category pairs based on a comparison between the plurality of interaction embeddings and the plurality of taxonomy embeddings.

Abstract: Systems and methods for improved spelling checking are disclosed. A method includes receiving a search query from a user device and determining that the search query does not exist in a data store that stores (a) a corpus of correctly spelled words or (b) forced correction mapping data. The method further includes, in response to the determining, determining a plurality of suggested search queries generated by a plurality of respective spell corrector models, selecting a suggested search query determined using a spell corrector model from the plurality of spell corrector models based on at least one of a frequency of the suggested search query in historical search data or a weightage associated with the spell corrector model, and causing the suggested search query to be displayed on the user device.

Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include retrieving an initial ranking data object associated with a plurality of search result data objects, retrieving a plurality of relevance score data objects, generating a plurality of ranking comparison score data objects, generating a multi-measure optimized ranking data object associated with the plurality of search result data objects, and performing one or more prediction-based actions based at least in part on the multi-measure optimized ranking data object.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing personalized autocomplete predictions. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform personalized autocomplete predictions using a general search corpus and/or individual curated search corpus.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for retrieving relevant items for user queries by generating, using a search engine machine learning model, a prediction-based action for the query input wherein query input embeddings of the query input are generated. For each query input embedding, a k-Nearest-Neighbor (KNN) search is performed with respect to search engine repository item embeddings to generate initial search results, and for each initial set result, performing N hops within a semantic graph starting from nodes associated with the initial search result to generate related search results. The search engine machine learning model is trained by generating a search engine repository item embeddings according to embedding techniques for respective content categories and generating the semantic graph based at least in part on a measure of similarity for pairs of search engine repository item embeddings.