Abstract: Data-sharding systems and/or methods for cost- and time-efficient record search are described. Data-sharding embodiments utilize a name-sharding dimension, optionally in combination with one or more additional dimensions such as record type and year, to reduce latency and reduce search-associated costs. The data-sharding systems and methods embodiments utilize an optimization algorithm to determine a distribution of records related to names. The optimization algorithm may use a three-character prefix for surnames in records to distribute shards across documents, with specific shards relating to no-name and multi-name records allocated.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for utilizing an improved search algorithm which prioritizes content items that include data fields (or other information) missing from a search query. For example, in response to a search query, the disclosed systems can prioritize or rank candidate content items to focus on candidate content items which include new information. Indeed, the disclosed systems can prioritize content items that include new information by ranking according to which content items include data fields missing from the search query. In some cases, the disclosed systems can prioritize content items with new information by determining which content items include data fields not already stored within a database associated with a user account (e.g., the user account performing the search) and/or for a particular entity or record within a genealogical database.

Abstract: A genealogical research assistant is provided by receiving a user query at a user interface; classifying the user query using a classification module, refining the classified user query using a refinement module, vectorizing the refined, classified user query using an embeddings module; retrieving, from a vector database, a plurality of results based on the vectorized, refined, classified user query; generating, using a generative machine-learning module, a response to the user query based on the plurality of results; and displaying, at the user interface, the response. The vector database may comprise a plurality of domain-specific content the generative machine-learning module may rely upon to generate the response. The generative machine-learning module may be configured to provide in-line links to the top n results from the vector database in the response.

Abstract: A genealogy online system may cause to display, at a graphical user interface associated with a genealogy online system, a search box, the genealogy online system configured to provide functions comprising family-tree building and historical record search. The genealogy online system may receive a query from a user entered at the search box. The genealogy online system may use a machine learning language model to determine an intent of the user associated with the query. The genealogy online system may cause to display, at the graphical user interface as a result of the query, one or more links to one or more functions of the genealogy online system based on the intent determined by the machine learning language model.

Abstract: A genealogy online system may cause to display, at a graphical user interface associated with a genealogy online system, a search box, the genealogy online system configured to provide functions comprising family-tree building and historical record search. The genealogy online system may receive a query from a user entered at the search box. The genealogy online system may use a machine learning language model to determine an intent of the user associated with the query. The genealogy online system may cause to display, at the graphical user interface as a result of the query, one or more links to one or more functions of the genealogy online system based on the intent determined by the machine learning language model.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating data subclusters for content items stored in a genealogical content database and allocating computing resources to the subclusters on an individual, customized basis. For example, the disclosed systems segment stored content items according to content type to generate a number of type-specific subclusters of content items. In addition, the disclosed systems allocate different (numbers of) virtual machines of a cloud computing system to process data for each of the subclusters independently. The disclosed systems can allocate duplicate virtual machines (along with corresponding processing power and memory) to some subclusters for redundancy while allocating only a single set of virtual machines for others.

Abstract: Data-sharding systems and/or methods for cost- and time-efficient record search are described. Data-sharding embodiments utilize a name-sharding dimension, optionally in combination with one or more additional dimensions such as record type and year, to reduce latency and reduce search-associated costs. The data-sharding systems and methods embodiments utilize an optimization algorithm to determine a distribution of records related to names. The optimization algorithm may use a three-character prefix for surnames in records to distribute shards across documents, with specific shards relating to no-name and multi-name records allocated.

Abstract: Data-sharding systems and/or methods for cost- and time-efficient record search are described. Data-sharding embodiments utilize a name-sharding dimension, optionally in combination with one or more additional dimensions such as record type and year, to reduce latency and reduce search-associated costs. The data-sharding systems and methods embodiments utilize an optimization algorithm to determine a distribution of records related to names. The optimization algorithm may use a three-character prefix for surnames in records to distribute shards across documents, with specific shards relating to no-name and multi-name records allocated.

Abstract: Search-result explanation systems, methods, and computer-program products receive a user search query, expand the search query into a plurality of sub-queries, perform a database search using the expanded user search query, and determine which sub-queries of the plurality of sub-queries matched with a particular search result. Results from the database search are re-indexed in an index generated on-the-fly and in-memory, within which the results are searched using the sub-queries to determine matching fields and match types. A score is determined based on the type of match(es) with a particular search result based on one or more predefined weights and normalized using a denominator comprising a fictitious, on-the-fly record configured to receive a perfect score according to the received user search query. A user interface showing ranked results and explanations for the ranking, including a score for the result based on the expanded user search query.

Abstract: Search-result explanation systems, methods, and computer-program products receive a user search query, expand the search query into a plurality of sub-queries, perform a database search using the expanded user search query, and determine which sub-queries of the plurality of sub-queries matched with a particular search result. Results from the database search are re-indexed in an index generated on-the-fly and in-memory, within which the results are searched using the sub-queries to determine matching fields and match types. A score is determined based on the type of match(es) with a particular search result based on one or more predefined weights and normalized using a denominator comprising a fictitious, on-the-fly record configured to receive a perfect score according to the received user search query. A user interface showing ranked results and explanations for the ranking, including a score for the result based on the expanded user search query.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for utilizing a sophisticated lifespan filter algorithm for searching genealogical databases to accurately identify genealogical records that match a record query. For example, utilizing the lifespan filter algorithm, the disclosed systems can access and analyze data pertaining to relatives of candidate records (e.g., genealogical records that could match a record query). In some cases, for a given candidate record, the disclosed systems access genealogical data fields for a spouse, one or both parents, and/or one or more children of the individual represented by the candidate record. From the relative-data fields, the disclosed systems can determine a record lifespan for the candidate record and can compare the record lifespan with a query lifespan of the record query to determine whether the candidate record matches the record query.

Abstract: A genealogy online system may cause to display, at a graphical user interface associated with a genealogy online system, a search box, the genealogy online system configured to provide functions comprising family-tree building and historical record search. The genealogy online system may receive a query from a user entered at the search box. The genealogy online system may use a machine learning language model to determine an intent of the user associated with the query. The genealogy online system may cause to display, at the graphical user interface as a result of the query, one or more links to one or more functions of the genealogy online system based on the intent determined by the machine learning language model.

Abstract: A genealogy online system may cause to display, at a graphical user interface associated with a genealogy online system, a search box, the genealogy online system configured to provide functions comprising family-tree building and historical record search. The genealogy online system may receive a query from a user entered at the search box. The genealogy online system may use a machine learning language model to determine an intent of the user associated with the query. The genealogy online system may cause to display, at the graphical user interface as a result of the query, one or more links to one or more functions of the genealogy online system based on the intent determined by the machine learning language model.

Abstract: Systems, methods, and other techniques for extracting data from obituaries are provided. In some embodiments, an obituary containing a plurality of words is received. Using a machine learning model, an entity tag from a set of entity tags may be assigned to each of one or more words of the plurality of words. Each particular tag from the set of entity tags may include a relationship component and a category component. The relationship component may indicate a relationship between a particular word and the deceased individual. The category component may indicate a categorization of the particular word to a particular category from a set of categories. The extracted data may be stored in a genealogical database.

Abstract: Systems, methods, and other techniques for extracting data from obituaries are provided. In some embodiments, an obituary containing a plurality of words is received. Using a machine learning model, an entity tag from a set of entity tags may be assigned to each of one or more words of the plurality of words. Each particular tag from the set of entity tags may include a relationship component and a category component. The relationship component may indicate a relationship between a particular word and the deceased individual. The category component may indicate a categorization of the particular word to a particular category from a set of categories. The extracted data may be stored in a genealogical database.

Abstract: Search-result explanation systems, methods, and computer-program products receive a user search query, expand the search query into a plurality of sub-queries, perform a database search using the expanded user search query, and determine which sub-queries of the plurality of sub-queries matched with a particular search result. Results from the database search are re-indexed in an index generated on-the-fly and in-memory, within which the results are searched using the sub-queries to determine matching fields and match types. A score is determined based on the type of match(es) with a particular search result based on one or more predefined weights and normalized using a denominator comprising a fictitious, on-the-fly record configured to receive a perfect score according to the received user search query. A user interface showing ranked results and explanations for the ranking, including a score for the result based on the expanded user search query.

Abstract: Image-faceted search systems and/or methods are described. Image-faceting embodiments receive genealogy records certain of which are imaged genealogy records associated with an image. Metadata of the imaged genealogy records are determined or extracted and used to assign the image genealogy records to one or more categories and optionally subcategories. Machine learning may be used to extract the metadata and/or to categorize the records, along with in embodiments a translation algorithm. A user faceted search query is received, with pertinent search results filtered according to a selected facet, such as an image facet, and according to filtering criteria. The filtered search results, including images matching the faceted search query, are presented to a user.

Abstract: Data-sharding systems and/or methods for cost- and time-efficient record search are described. Data-sharding embodiments utilize a name-sharding dimension, optionally in combination with one or more additional dimensions such as record type and year, to reduce latency and reduce search-associated costs. The data-sharding systems and methods embodiments utilize an optimization algorithm to determine a distribution of records related to names. The optimization algorithm may use a three-character prefix for surnames in records to distribute shards across documents, with specific shards relating to no-name and multi-name records allocated.

Abstract: Systems, methods, and other techniques for extracting data from obituaries are provided. In some embodiments, an obituary containing a plurality of words is received. Using a machine learning model, an entity tag from a set of entity tags may be assigned to each of one or more words of the plurality of words. Each particular tag from the set of entity tags may include a relationship component and a category component. The relationship component may indicate a relationship between a particular word and the deceased individual. The category component may indicate a categorization of the particular word to a particular category from a set of categories. The extracted data may be stored in a genealogical database.

Abstract: A search system performs a federated search across multiple databases and generates a ranked combined list of found genealogical records. The system receives a user query with one or more specified characteristics. The system may determine expanded characteristics derived from the specified characteristics. The system searches the various databases with the characteristics retrieving records according to the characteristics. The system combines the retrieved records and ranks them using a machine learning model. The machine learning model is configured to assign a weight to the records returned from each of the genealogical databases based on the characteristics specified in the user query. The machine learning model may be trained by any combination of one or more of: a Nelder-Mead method, a coordinate ascent method, and a simulated annealing method. The ranked combined results are provided in response to the user query.