Abstract: Disclosed herein relates to a method that analyzes the sentiment of user feedback for a genealogical system and identifies key phrases that may relate to novel themes in the user feedback. Sentiment analysis and novel theme prediction systems, methods, and computer-program products are described. Sentiment analysis of user feedback may include dividing user-generated unstructured text files into sections. The method classifies each section to an aspect of the genealogical system from a predetermined list of aspects monitored by the genealogical system. The method inputs the text belonging to the classified section to a supervised machine learning model and determines a sentiment associated with the classified section. In other embodiments, a method generates embedding vectors representing survey responses from users of a genealogical system. The method extracts a subset of survey responses having embedding vectors grouped into one cluster. The method extracts key phrases that may indicate a novel theme.

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: 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 transforming and navigating historical map images are presented. The systems and methods embodiments facilitate providing, searching for, retrieving, transforming, and/or navigating a historical map image vis-à-vis a modern location and/or map. A map interface facilitates automatedly overlaying, annotating, and aligning a historical map image(s) with a modern map, allowing a user to search for a location and see the same in the historical map image, and change a visibility of the overlaid and aligned map images relative to each other. The map interface provides user interactions that facilitate retrieving, viewing, and manipulating records, historical districts, and other pertinent data through interacting with a particular location and/or searched-for individual, such as an ancestor or other person of interest.

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 genealogical entity resolution. In some embodiments, first tree data and second tree data are obtained, the first tree data corresponding to a first tree person and the second tree data corresponding to a second tree person. A set of features is extracted from the first tree data and the second tree data. An individual-level similarity score for each possible pairing of tree persons is generated based on the set of features. A set of most-similar tree persons is identified based on the individual-level similarity score for each possible pairing. A plurality of individual-level similarity vectors for the set of most-similar tree persons are provided as input to a family-level ML model to determine that the first tree person and the second tree person correspond to a same individual.

Abstract: OCR-text correction system and method embodiments are described. The OCR-text correction embodiments comprise or cooperate with a transformer-based sequence-to-sequence language model. The model is pretrained to denoise corrupted text and is fine-tuned using OCR-correction-specific examples. Text obtained at least in part through OCR is applied to the fine-tuned pretrained transformer model to detect at least one error in a subset of the text. Responsive to detecting the at least one error, the fine-tuned pretrained transformer model outputs an updated subset of the text to correct the at least one error.

Abstract: Systems and methods for determining whether two tree persons in a genealogical database correspond to the same real-life individual. Embodiments include identifying two tree persons in a genealogical database and extracting a plurality of features from both tree persons to generate two vectors. Embodiments also include calculating a plurality of metrics between the two vectors to generate a metric function. Embodiments further include generating feature weights using a recursive process based on training data input by external users, and generating a score by calculating a weighted sum of the metric function being weighted by the feature weights. The generated score may then be compared to a threshold value.

Abstract: Methods, systems, and computer-program products for image enhancement include receiving an image and optionally a user request, classify the image, crop image components of the image, restore cropped image components of the image, colorized restored image components, and reconstruct the image from the colorized, restored image components and other components. The other components may include text components that are restored in a separate treatment pipeline.

Abstract: A computing server may receive genealogical records that include historical records of deceased individuals. The computing server may normalize the genealogical records into normalized genealogical records. Normalizing the genealogical records may include converting a text string of a genealogical record into a standardized format. The computing server may stitch the normalized genealogical records into a plurality of clusters. Each cluster corresponds to an individual and includes one or more genealogical records associated with the individual. The computing server may identify a life-event record that is commonly associated with a subset of clusters, the life-event record indicating that a plurality of deceased individuals are connected through a non-familial relationship in a life event documented by the life-event record.

Abstract: Systems, methods, and computer-program products for entity resolution are disclosed. Entity resolution embodiments include receiving tree data from each of a pair of entities, extracting and/or aggregating feature vectors or metric functions therefrom, and generating similarity scores between the pair of entities. The similarity scores may be weighted using machine-learned weights. The weighted similarity scores are used to generate a combinatorial probability score accounting for combined likelihoods of field values between the pair of entities. A classification of the pair of entities is performed based on the combinatorial probability score, with a genealogical database modified based on the classification.

Abstract: A genealogy system includes a server with memory and processors storing code that instructs the processors to store genealogy data and user profiles, providing a research platform for users. A remote client device, equipped with an image sensor and display, communicates with the server. The client device displays the genealogy research platform, allowing users to select a genealogy item for an artificial reality experience. Upon user command, the client device presents continually updating artificial reality images of an environment, overlaying a digital representation of the selected genealogy item on the artificial reality experience. This system seamlessly integrates genealogical research with artificial reality technology for an immersive user experience.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating and providing a hybrid search-and-browse interface for accurately and efficiently locating and presenting targeted genealogical content items. For example, the disclosed systems generate and provide a multi-layered navigational structure using browse trees that represent categories of genealogical content items, where each successive browse tree in the hybrid search-and-browse interface narrows the search results from the one before based on some (selected) criteria. In some cases, to support a hybrid search-and-browse interface, the hybrid search-and-browse system generates and maintains a facet index for genealogical content items stored in a database as a basis for generating and providing browse trees for navigating through search results of content items.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for determining an in-memory data structure for storing digital images (e.g., newspaper images representing individual pages of digitized newspapers) based on a first level hash and a second level hash that map to nested categories within a browse structure of a genealogical data system. For example, the disclosed systems generate a multilevel data block by implementing one or more compression techniques to reduce overall data size, particularly relating to month data and image/page identification data. In some cases, the disclosed systems greatly reduce the memory and processing requirements of storing, browsing, and searching digital content items (e.g., newspaper images) within a genealogical database.

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 computing server may continuously update a set of nodes that are addable to a data tree based on past interactions of the user with one or more nodes. The computing server may track a recently interacted set of interacted nodes with which the user has interacted within a number of past interactions. The computing server may select a pool of candidate nodes based on the recently interacted set. At least one of the candidate nodes is within a domain boundary of one of the interacted nodes that is in the recently interacted set. The domain boundary may be determined by the degree of relationship. The computing server may present one or more candidate nodes in the pool as a version of the continuously updated set of nodes. The computing server may update the pool of candidate nodes as additional interactions performed by the user updates the recently interacted set.

Abstract: A family tree interface may include a default number of family members in addition to a target node which are expandable upon selection by a user. The default tree interface is expandable by a user vertically to include more generations and laterally. The tree interface includes labels showing a relationship of a tree node to the target node. In some embodiments, one or more family members that have not been rendered may be cached to speed up the visual rendering process. A graphical user interface, in a viewing session, may display an initial view of the family tree associated with the target individual. Upon receipt of an expand request, the viewing session may add the one or more additional family members to generate an expanded view of the family tree. The expanded view may partially adjust the initial view without refreshing the viewing session.

Abstract: Systems and methods for importing documents are described. An input image is received and preprocessed. OCR and/or page segmentation and chapter detection are performed. Special-case processing is performed for lists, tables, free text, and other categories. Anaphora analysis, stemming, lemmatization, and relationship detection are performed. A genealogical tree is generated, augmented, or merged based on the extracted entities and relationships.

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 and methods for identifying and segmenting objects from images include a preprocessing module configured to adjust a size of a source image; a region-proposal module configured to propose one or more regions of interest in the size-adjusted source image; and a prediction module configured to predict a classification, bounding box coordinates, and mask. Such systems and methods may utilize end-to-end training of the modules using adversarial loss, facilitating the use of a small training set, and can be configured to process historical documents, such as large images comprising text. The preprocessing module within the systems and methods can utilize a conventional image scaler in tandem with a custom image scaler to provide a resized image suitable for GPU processing, and the region-proposal module can utilize a region-proposal network from a single-stage detection model in tandem with a two-stage detection model paradigm to capture substantially all particles in an image.