Ancestry.com Operations Inc.

Pearl.com LLC

Inquira, Inc.

Oracle Otc Subsidiary LLC

Oracle International Corporation

Gann Alexander Bierner

Edwin Riley Cooper

Michael Peter Dukes

Filippo Ferdinando Paulo Beghelli

Peng Jiang

Robert Weis

Tyler Folkman

Lei Wu

Carol Myrick Anderson

James Charles Williams

Filippo Beghelli

Laurel Kathleen Graham

Philip Theodore Crone

Deniz Yuret

Azadeh Moghtaderi

Yingrui Yang

Ruhan Wang

Roger Clifton Neel

Christopher Adam Mengel

Fengjie Alex Li

Kevan Craig McGowan

Joel Edward Hobson

Ashkan Gholam Zadeh

Sean Peter Handel

Stacey Anne Matinale

Bryan Dale Sivak

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.

Unified search systems and methods

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.

Data-sharding for efficient record search

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.

Search-result explanation systems and methods

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.

Extraction of genealogy data from obituaries

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.

Search and ranking of records across different databases

A method for ranking genealogical records includes using a machine learning model to rank multiple searched records based on relevancy. The relevancy may be determined by identifying features included in a record and scaling each feature by a corresponding weight factor. In addition, a method for training a machine learning model and increasing the convergence speed of the training is described. To train the model, a machine learning process is used to optimize a ranking performance metric. A set of weights corresponding to multiple features are used to rank multiple past search records in a training set. An initial set of the weights are set by the expectation values of the weights. The weights are incrementally changed to optimize the ranking performance metric. The step size of the increment is determined based on the sensitivity of the ranking performance metric relative to the step size.

Customized coordinate ascent for ranking data records

An information entropy-based metric is used to represent a degree of diversity of a search result of genealogical records. In response to a query, a data query server locates a set of multiple records that match the query. The records are classified into different record types based on the records' attributes. One or more distributions of numbers of records classified into each record type are determined. Each distribution corresponds to one of the subsets the records. For each distribution, an entropy value is determined. A cumulative entropy that corresponds to a sum of the entropy values of those distributions is then determined. The cumulative entropy may serve as the entropy-based metric of the search result. The cumulative entropy may also be normalized by an ideal cumulative entropy. The normalized metric allows the diversity of different search results to be compared across different queries that may generate different numbers of records.

Diversity evaluation in genealogy search

Embodiments of the present invention further provide systems and methods for allowing users in an online consultation system to create, edit and follow information feeds related to a subject matter category, topic, person or a combination thereof.

Method and apparatus for creating a personalized question feed platform

A system, method, and computer readable medium for applying one or more information retrieval technologies is disclosed for resolving a query. In one embodiment, an exemplary system generates a response using a language analysis module configured to parse a query into elements. This system can also include a rules engine configured to compare a condition of a rule against the elements, where the rule is configured to perform an action to retrieve information. Further, a response generator coupled to said rules engine receives the rule and retrieves the information for presentation to a querier in a portion of a display that adjusts proportionately to the degree of importance of said information.

Ontology for use with a system, method, and computer readable medium for retrieving information and response to a query

Embodiments of the present invention further provide systems and methods for automatically identifying questions on topics similar to a newly submitted question to an online the consultation system.

Method and apparatus for identifiying similar questions in a consultation system

Embodiments of the present invention provide for a method of identifying customer service and duplicate questions submitted by a user to a consultation system by creating a model from features extracted from questions previously identified as customer service questions, extracting features from a newly submitted question by performing phrasal analysis of the question, comparing the extracted features of the newly submitted question to the extracted features of the model, and determining whether the newly submitted question is a customer service question.

Method and apparatus for identifying customer service and duplicate questions in an online consultation system

Linguistic analysis is used to identify queries from a plurality of users over a period of time that use different natural language formations to request similar information. Common intent categories are identified for the queries requesting similar information. Intent responses can then be provided that are associated with the identified intent categories. An intent management tool can be used for identifying new intent categories, identifying obsolete intent categories, or refining existing intent categories. The said intent categories are used in identifying ontologies associated with the intent categories to help in selecting concepts as ontology parameters.

Intent management tool for identifying concepts associated with a plurality of users' queries

An intent guessing system receives partial user queries as they are entered by the user. The partial user queries are compared with different intents derived from previously logged queries. Guesses are made as to which of the intents are associated with the partial user query. The intent guesses are then provided as responses to the user query. Features are identified for the earlier logged queries and associated with the derived intents. The derived intents and associated features are then used to identify intents for the partial user queries.

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San Bruno, CA, United States of America

Gann Alexander Bierner