Provo, UT, United States
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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.


A handwriting recognition system converts word images on documents, such as document images of historical records, into computer searchable text. Word images (snippets) on the document are located, and have multiple word features identified. For each word image, a word feature vector is created representing multiple word features. Based on the similarity of word features (e.g., the distance between feature vectors), similar words are grouped together in clusters, and a centroid that has features most representative of words in the cluster is selected. A digitized text word is selected for each cluster based on review of a centroid in the cluster, and is assigned to all words in that cluster and is used as computer searchable text for those word images where they appear in documents. An analyst may review clusters to permit refinement of the parameters used for grouping words in clusters, including the adjustment of weights and other factors used for determining the distance between feature vectors.


A handwriting recognition system converts word images on documents, such as document images of historical records, into computer searchable text. Word images (snippets) on the document are located, and have multiple word features identified. For each word image, a word feature vector is created representing multiple word features. Based on the similarity of word features (e.g., the distance between feature vectors), similar words are grouped together in clusters, and a centroid that has features most representative of words in the cluster is selected. A digitized text word is selected for each cluster based on review of a centroid in the cluster, and is assigned to all words in that cluster and is used as computer searchable text for those word images where they appear in documents. An analyst may review clusters to permit refinement of the parameters used for grouping words in clusters, including the adjustment of weights and other factors used for determining the distance between feature vectors.


A handwriting recognition system converts word images on documents, such as document images of historical records, into computer searchable text. Word images (snippets) on the document are located, and have multiple word features identified. For each word image, a word feature vector is created representing multiple word features. Based on the similarity of word features (e.g., the distance between feature vectors), similar words are grouped together in clusters, and a centroid that has features most representative of words in the cluster is selected. A digitized text word is selected for each cluster based on review of a centroid in the cluster, and is assigned to all words in that cluster and is used as computer searchable text for those word images where they appear in documents. An analyst may review clusters to permit refinement of the parameters used for grouping words in clusters, including the adjustment of weights and other factors used for determining the distance between feature vectors.


A system identifies ancestral birth locations or surnames estimated to be associated with an individuals ancestors using an individuals genetic sample. The system identifies users who are genetic matches to the individual and determines whether and how often a birth location or surname appears in the pedigrees of those users. Birth locations or surnames that appear frequently throughout the pedigrees of genetically matching users may represent birth locations or surnames that are affiliated with the individuals ancestors. The system determines whether the frequency of appearance of a birth location or surname is statistically significant to eliminate biases for certain birth locations or surnames that appear more frequently than others. The birth location or surname may be provided to the individual based on an also-determined enrichment score.


A mounting bracket and hanger system for securing a tree stand to a tree includes a mounting bracket comprising a body and at least one recess defined in the body, and a hanger comprising a securing member, a body, and at least one extension member extending from the body of the hanger. The at least one extension member of the hanger may be configured for insertion into the at least one recess of the mounting bracket. The securing member of the hanger may be configured for insertion into a solid object.


Undated photos are organized by estimating the date of each photo. The date is estimated by building a model based on a set of reference photos having established dates, and comparing image characteristics of the undated photo to the image characteristics of the reference photos. The photo characteristics can include hues, saturation, intensity, contrast, sharpness and graininess as represented by image pixel data. Once the date of a photo is estimated, it can be tagged with identifying information, such as by using the estimated date to associate the photo with a node in a family tree.


Patent
Ancestry | Date: 2016-07-13

An input sample SNP genotype is divided into a plurality of windows, each including a sequence of SNPs. For each window, a diploid hidden Markov Model (HMM) is built and from a haplotype Markov Model (MM). The diploid HMM for a window is used to determine the probability that the window corresponds to a pair of labels (e.g., ethnicity labels). An inter-window HMM, with a set of states for each window, is built based on the diploid HMMs for each window. Labels are assigned to the input sample genotype based on the inter-window HMM.


Patent
Ancestry | Date: 2016-06-15

Described embodiments enable identification of family networks using combinations of DNA analysis and genealogical information. Genealogical data is provided by users of a genealogical research service or collected from other sources and used to create family trees for each user. DNA samples are also received from the users. By analyzing the DNA samples, potential genetic relationships can be identified between some users. Once these DNA-suggested relationships have been identified, common ancestors can be sought in the respective trees of the potentially related users. Where these common ancestors exist, an inference is drawn that the DNA-suggested relationship accurately represents a familial overlap between the individuals in question. People descended from the same common ancestor are each members of a family network. Members of a family network not in a users tree may be identified for the user, enabling the user to discover additional ancestors that might otherwise have remained unknown.


A tree building system that accesses data from a social networking site and, in some embodiments, other data sites having information that may pertain to the relationships between users of the social networking site. A rules engine within the system implements logic for analyzing the data from the social networking site. The system provides a candidate list of possible relatives for selection by a user and solicits information from the user through the use of guided questions.

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