Abstract: Image identification, retrieval, transformation and arrangement systems, methods, and computer-program products are configured to access a family tree of a user in a family tree database, identify one or more additional persons of interest in the family tree, determine whether the one or more persons of interest is associated with an image, retrieve the image, and transform the image of the one or more additional persons of interest with an image of the user or other person such as in an image arrangement template. Whether an image pertains to a person is determined using a machine learning classifier. A plurality of candidate lineages from a root or self node may be evaluated based on the number and/or quality of images associated therewith and/or based on filtering the one or more characteristics of the nodes in the candidate lineages.

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: Hybrid machine-learning systems and methods can be used to perform automatic keyphrase extraction from input text, such as historical records. For example, a computer-implemented method for extracting keyphrases from input text can include receiving input text having a plurality of words and identifying a set of candidate phrases from the plurality of words and a score for each of the candidate phrases using one or more unsupervised machine-learning models. The method can also include identifying named entities from the set of candidate phrases using one or more supervised machine-learning models and determining an updated set of scores for at least some of the candidate phrases within the set based on the named entities identified using the supervised machine-learning model. The method can also include identifying a keyphrase from the set of candidate phrases based on the updated set of scores.

Abstract: Systems and methods for training a machine learning (ML) ranking model to rank genealogy hints are described herein. One method includes retrieving a plurality of genealogy hints for a target person, where each of the plurality of genealogy hints corresponds to a genealogy item and has a hint type of a plurality of hint types. The method includes generating, for each of the plurality of genealogy hints, a feature vector having a plurality of feature values, the feature vector being included in a plurality of feature vectors. The method includes extending each of the plurality of feature vectors by at least one additional feature value based on the number of features of one or more other hint types of the plurality of hint types. The method includes training the ML ranking model using the extended plurality of feature vectors and user-provided labels.

Abstract: Described herein are systems, methods, and other techniques for training a generative adversarial network (GAN) to perform an image-to-image transformation for recognizing text. A pair of training images are provided to the GAN. The pair of training images include a training image containing a set of characters in handwritten form and a reference training image containing the set of characters in machine-recognizable form. The GAN includes a generator and a discriminator. The generated image is generated using the generator based on the training image. Update data is generated using the discriminator based on the generated image and the reference training image. The GAN is trained by modifying one or both of the generator and the discriminator using the update data.

Abstract: In accordance with an embodiment of the invention, a system and method is provided for determining a probability of a progeny having one or more phenotypes Phj each associated with a single gene Qj. A score sip may be assigned to each allele hip at a plurality of genetic loci (i) in a haploid genome profile Hp of a parent (p). A plurality (Nj) of the alleles hkp (k=1, . . . , Nj) associated with the gene Qj may be identified. The scores sip may be mapped or indexed to gene-specific scores ?j,kp associated with gene Qj for the plurality of (Nj) alleles hkp. A probability may be computed for altering the gene product from gene Qj in a progeny of the parent (p) to be a function of the gene-specific scores ?j,kp.

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: Described are techniques for determining population structure from identity-by-descent (IBD) of individuals. The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities.

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.

Abstract: Systems and methods for retrieving a set of ordered items from a distributed database. A plurality of ordered items may be stored at a cache. The plurality of ordered items may have a length of N+B at a first instant in time. A first instruction to delete a first item of the plurality of ordered items may be received. A second instruction to add a second item to the plurality of ordered items may be received. The first instruction and the second instruction may be stored in a change log. A request for the first N items of the plurality of ordered items may be received. The first instruction may be executed by deleting the first item from the plurality of ordered items. The second instruction may be executed by adding the second item to the plurality of ordered items. The first N items of the plurality of ordered items may be sent in response to the request.

Abstract: A biological sample collection system can include a sample collection vessel having a sample collection chamber with an opening configured to receive a biological sample into the sample collection chamber. The biological sample collection system can additionally include a selectively movable sleeve valve configured to associate with the opening of the sample collection chamber. The biological sample collection system can additionally include a sealing cap that is configured to associate with the selectively movable sleeve valve and with the sample collection vessel. The sealing cap can include a reagent chamber having reagent(s) stored therein, and when the sealing cap is associated with the sample collection vessel, the selectively movable sleeve valve opens, dispensing the reagent(s) into the sample collection chamber.

Abstract: A user may select one or more potential common ancestors with a DNA match to view the target individual's relationship with them. The process may include identifying, from a first genealogical profile of the target individual. A first individual has a first linkage that connects the target individual towards the selected potential common ancestor. The process may also include identifying, from a second genealogical profile of the DNA match, a second individual who has a second linkage that connects the DNA match towards the selected potential common ancestor. The process may further include connecting the first linkage and the second linkage with the selected potential common ancestor by adding one or more individuals whose profiles are retrieved from other searchable genealogical profiles stored in the online system. With the nodes and connections available, the process may generate a map of visual connections between the target individual and the DNA match.

Abstract: Embodiments described herein relate generally to a methodology of efficient object classification within a visual medium. The methodology utilizes a first neural network to perform an attention based object localization within a visual medium to generate a visual mask. The visual mask is applied to the visual medium to generate a masked visual medium. The masked visual medium may be then fed into a second neural network to detect and classify objects within the visual medium.

Abstract: The disclosed system links an individual dataset to a database. The system receives a target individual dataset associated with a target individual and identifies candidate individual datasets that are potentially related to the target individual dataset. The system identifies a related individual dataset that has data bits that match some data bits in the target individual dataset. The system then identifies a parent node that is a common parent node to both the target individual dataset and the related individual dataset. The system retrieves a data tree that the parent node belongs to with the data tree containing information describing inter-relationships among datasets in the data tree. A node in the data tree is identified to assign the target individual dataset based on strings of matched data bits and number of the matched strings between the target individual dataset and the datasets in the data tree.

Abstract: A sample collection system can include a sample collection vessel having a sample collection chamber with an opening configured to receive a sample into the sample collection chamber. The sample collection system can additionally include a selectively movable sleeve valve configured to associate with the opening of the sample collection chamber. The sample collection system can include a sealing cap that is configured to associate with the selectively movable sleeve valve and with the sample collection vessel. The sealing cap can include a reagent chamber having reagent(s) stored therein, and when the sealing cap is associated with the sample collection vessel, the selectively movable sleeve valve opens, dispensing the reagent(s) into the sample collection chamber. When the selectively moveable sleeve associates with the sample collection chamber, an outer sleeve slides relative to an inner vessel, opening the sleeve and dispensing reagent into the sample collection chamber.

Abstract: 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.

Abstract: Methods and systems for creating a cluster view person for genealogical studies. Methods may include obtaining a plurality of genealogical trees. Each of the genealogical trees may include a plurality of interconnected nodes representing individuals that are related to each other. Methods may also include identifying one or more of the genealogical trees that contain a similar individual. Whether two individuals are grouped may depend on similarity and/or quality thresholds. Methods may include creating an aggregate individual including each of the similar individuals in each of the identified genealogical trees. The aggregate individual may combine information from each of the similar individuals.

Abstract: Described herein are systems, methods, and other techniques for extracting one or more keyphrases from an input text. The input text may include a plurality of words. A plurality of token-level attention matrices may be generated using a transformer-based machine learning model. The plurality of token-level attention matrices may be converted into a plurality of word-level attention matrices. A set of candidate phrases may be identified from the plurality of words based on the plurality of word-level attention matrices. The one or more keyphrases may be selected from the set of candidate phrases.

Abstract: Systems and methods for determining whether two tree persons in a genealogical database correspond to the same real-life individual. Embodiments include obtaining, from a tree database, a first tree person from a first genealogical tree and a second tree person from a second genealogical tree. Embodiments also include identifying a plurality of familial categories. Embodiments further include, for each familial category of the plurality of familial categories, extracting a first quantity of features for each of the tree persons in the familial category, generating a first similarity score for each possible pairing of tree persons, identifying a representative pairing based on a maximum first similarity score, and extracting a second quantity of features for each of the tree persons in the representative pairing. Embodiments may also include generating a second similarity score based on the second quantity of features.