09 - Scientific and electric apparatus and instruments
Goods & Services
Computer application software for mobile phones and handheld
computers, namely, downloadable software for uploading,
scanning, digitizing, viewing, organizing, sharing and
editing photographs and for integrating photographs into
genealogical databases and family trees; downloadable
software in the nature of a mobile application for
uploading, scanning, digitizing, viewing, organizing,
sharing and editing photographs and for integrating
photographs into genealogical databases and family trees;
downloadable computer application software for mobile phones
and handheld computers for researching and managing
genealogical information collected during family history and
genealogical research; electronic databases in the field of
genealogical historical data, family history data, census
data, birth, marriage and death records, photographs and
graphical representations of family trees recorded on
computer media; downloadable computer software for creating,
managing, recording, searching, indexing, filtering and
retrieval of genealogical historical data, family history
data, census data, birth, marriage and death records;
downloadable computer software for creating, managing,
recording, searching, indexing, filtering and retrieval of
sound and image files; downloadable computer software for
use in creating, displaying, sharing and storing multimedia
presentation files that include photographs and sound;
downloadable computer software for graphically depicting
genealogical historical data and family history data;
downloadable computer software to enable searching of data
and for connection to databases and the Internet;
downloadable computer software that allows interaction
between Internet sites; downloadable computer software for
production of genealogical tables and charts; downloadable
electronic publications in the nature of newsletters in the
field of genealogy and family history; downloadable reports
featuring genealogical historical data, family history data,
census data, birth, marriage and death records; downloadable
graphics featuring tables and charts in the field of
genealogical historical data, family history data, census
data, birth, marriage and death records.
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.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating and providing recommended genealogical content items using a selection-prediction neural network. For example, the disclosed systems utilize a transformer-based selection-prediction neural network to generate selection predictions for genealogical content items according to previous client device interactions as well as genealogical metrics, including content-based genealogical metrics, tree-level genealogical metrics, and/or account-level genealogical metrics. In some cases, the disclosed systems train a selection-prediction neural network by learning network parameters based on features extracted from content items, client device behavior, genealogy trees, and user accounts.
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.
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.
Disclosed herein are methods, systems, and non-transitory computer readable mediums for generating a shareable genealogical summary for a target individual. An example method includes receiving a request from a user to generate a shareable genealogical summary about a target user. The method generates the shareable genealogical summary comprising a genealogical history of the target user. The method provides genealogical information for the target user to a machine-learning language model. The genealogical information includes a family tree. The method receives a response generated by executing the machine-learning language model from a model serving system. The method provides the shareable genealogical summary for display to the user.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Kits for scientific and research purposes comprised
primarily of a sample collection tube, a swab for collecting
a genetic sample, and an instruction manual for use in DNA
testing for dogs (term considered too vague by the
International Bureau - Rule 13 (2) (b) of the Regulations);
DNA test kits for scientific and research use comprised of a
sample collection apparatus for the testing and analysis of
DNA and genetics in dogs; DNA collection kits for scientific
and research use for the testing and analysis of DNA and
genetics in dogs for the purposes of breed identification,
ancestry determination, and trait identification (term
considered too vague by the International Bureau - Rule 13
(2) (b) of the Regulations). Providing scientific analysis in the field of genetics;
reporting services based upon the results of laboratory
testing in the field of genetics; DNA testing and DNA
analysis services for non-medical use; providing a website
featuring temporary use of non-downloadable software for
providing results of DNA tests and genetic analyses in dogs
for the purposes of breed identification, ancestry
determination, and trait identification (term considered too
vague by the International Bureau - Rule 13 (2) (b) of the
Regulations); providing scientific analysis and
informational reports based upon results of laboratory
testing in the field of canine genetics for the purposes of
breed identification, ancestry determination, and trait
identification; providing online computer databases
featuring information based on the results of DNA testing
and genetic analyses in dogs for research purposes (term
considered too vague by the International Bureau - Rule 13
(2) (b) of the Regulations); computer services, namely,
hosting and maintaining an online website for others to
access and share information and data in the field of pet
genealogy; providing temporary use of non-downloadable
software for use in creating, displaying, sharing and
storing information and data in the field of pet genealogy;
application service provider services featuring software
allowing users to generate information and view analyses
based upon results of canine genetic testing. Providing an online resource center featuring information in
the field of pet genealogy (term considered too vague by the
International Bureau - Rule 13 (2) (b) of the Regulations);
providing pet genealogical information, namely, information
services in the nature of retrieving, recording and
reviewing pet breed identification, ancestral data, and
physical traits via the internet.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Kits for scientific and research purposes comprised
primarily of a sample collection tube, a swab for collecting
a genetic sample, and an instruction manual for use in DNA
testing for dogs (term considered too vague by the
International Bureau - Rule 13 (2) (b) of the Regulations)
(term considered too vague by the International Bureau -
Rule 13 (2) (b) of the Regulations); DNA test kits for
scientific and research use comprised of a sample collection
apparatus for the testing and analysis of DNA and genetics
in dogs; DNA collection kits for scientific and research use
for the testing and analysis of DNA and genetics in dogs for
the purposes of breed identification, ancestry
determination, and trait identification. Providing scientific analysis in the field of genetics;
reporting services based upon the results of laboratory
testing in the field of genetics; DNA testing and DNA
analysis services for non-medical use; providing a website
featuring temporary use of non-downloadable software for
providing results of DNA tests and genetic analyses in dogs
for the purposes of breed identification, ancestry
determination, and trait identification (term considered too
vague by the International Bureau - Rule 13 (2) (b) of the
Regulations); providing scientific analysis and
informational reports based upon results of laboratory
testing in the field of canine genetics for the purposes of
breed identification, ancestry determination, and trait
identification; providing online computer databases
featuring information based on the results of DNA testing
and genetic analyses in dogs for research purposes (term
considered too vague by the International Bureau - Rule 13
(2) (b) of the Regulations); computer services, namely,
hosting and maintaining an online website for others to
access and share information and data in the field of pet
genealogy; providing temporary use of non-downloadable
software for use in creating, displaying, sharing and
storing information and data in the field of pet genealogy;
application service provider services featuring software
allowing users to generate information and view analyses
based upon results of canine genetic testing. Providing an online resource center featuring information in
the field of pet genealogy (term considered too vague by the
International Bureau - Rule 13 (2) (b) of the Regulations);
providing pet genealogical information, namely, information
services in the nature of retrieving, recording and
reviewing pet breed identification, ancestral data, and
physical traits via the internet.
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.
G06F 3/04815 - Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Computer services, namely, hosting and maintaining an online website for others to access and share information and data in the field of pet genetics; providing temporary use of non-downloadable software for use in creating, displaying, sharing, and storing information and data in the field of pet genetics; Application service provider services featuring software allowing users to view and share information and analyses based upon results of canine genetic testing Providing an online database featuring information and results of pet genetic testing with the purpose of matching pet owners with other pet owners who have pets that are geographically nearby and which are a compatible pet breed type or breed mix for social interactions; Providing information to pet owners, namely, online social networking services in the nature of viewing, retrieving, and identifying socially compatible pets based on breed identification and geographic information; Online social networking services for pet owners
09 - Scientific and electric apparatus and instruments
Goods & Services
Computer application software for mobile phones and handheld computers, namely, downloadable software for uploading, scanning, digitizing, viewing, organizing, sharing and editing photographs and for integrating photographs into genealogical databases and family trees; downloadable software in the nature of a mobile application for uploading, scanning, digitizing, viewing, organizing, sharing and editing photographs and for integrating photographs into genealogical databases and family trees; downloadable computer application software for mobile phones and handheld computers for researching and managing genealogical information collected during family history and genealogical research; electronic databases in the field of genealogical historical data, family history data, census data, birth, marriage and death records, photographs and graphical representations of family trees recorded on computer media; downloadable computer software for creating, managing, recording, searching, indexing, filtering and retrieval of genealogical historical data, family history data, census data, birth, marriage and death records; downloadable computer software for creating, managing, recording, searching, indexing, filtering and retrieval of sound and image files; downloadable computer software for use in creating, displaying, sharing and storing multimedia presentation files that include photographs and sound; downloadable computer software for graphically depicting genealogical historical data and family history data; downloadable computer software to enable searching of data and for connection to databases and the Internet; downloadable computer software that allows interaction between Internet sites; downloadable computer software for production of genealogical tables and charts; downloadable electronic publications in the nature of newsletters in the field of genealogy and family history; downloadable reports featuring genealogical historical data, family history data, census data, birth, marriage and death records; downloadable graphics featuring tables and charts in the field of genealogical historical data, family history data, census data, birth, marriage and death records
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.
G06F 16/2457 - Query processing with adaptation to user needs
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Entertainment and educational services relating to genealogy and family history, namely, classes, workshops and educational conferences in the field of genealogy and family history; arranging and conducting seminars and webinars in the field of genealogy and family history; television and webcast entertainment relating to genealogy and family history, namely, on-going television programs and on-going series provided through webcasts, all in the field of genealogy and family history; publishing of books, e-books, and audio books, all in the field of genealogical historical data, family history data census data, birth, marriage and death records; video recording of personal genealogical documentaries Application service provider services featuring software for use in creating, displaying, sharing and storing multimedia presentations that include photographs and sound, all in the field of genealogy and family history; providing temporary use of non-downloadable computer software for use in creating, displaying, sharing and storing multimedia presentations that include photographs and sound, all in the field of genealogy and family history; hosting of digital content on the Internet, namely, hosting on-line journals and blogs in the field of genealogy and family history; computer services, namely, hosting and maintaining an online website for others to access photo albums and calendars; providing temporary use of non-downloadable computer software for use in the creation and publication of on-line journals and blogs in the field of genealogy and family history; genealogical services, namely, genealogy research, provided in person and via the Internet Provision of genealogical information, namely, provision of educational, research and historical tables of genealogical information; providing genealogical information, namely, family history information services, namely, retrieving, recording and reviewing ancestral data via the global computer network; providing an on-line computer database in the field of genealogy information and family history information; consultancy, information and advisory services relating to the aforesaid
14.
SYSTEM AND METHOD FOR GENEALOGICAL ENTITY RESOLUTION
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.
A system or method for recommending one or more entry collections based on a query or a data entry. The method includes obtaining a query requesting information from a plurality of entry collections, extracting features from the query, and determining one or more entry collections among the plurality of entry collections that are likely to contain information related to the query based in part on the extracted features. The method further includes generating one or more links linking to the one or more entry collections, and causing the one or more links to be displayed to a user at a client device.
Described herein are systems, methods, and other techniques for segmenting an input text. A set of tokens are extracted from the input text. Token representations are computed for the set of tokens. The token representations are provided to a machine learning model that generates a set of label predictions corresponding to the set of tokens. The machine learning model was previously trained to generate label predictions in response to being provided input token representations. Each of the set of label predictions indicates a position of a particular token of the set of tokens with respect to a particular segment. One or more segments within the input text are determined based on the set of label predictions.
41 - Education, entertainment, sporting and cultural services
Goods & Services
Entertainment media production services for the Internet;
entertainment services, namely, multimedia production
services; film and video production consulting services;
media production services, namely, production of video,
film, Internet and television entertainment content;
entertainment services in the nature of development,
creation, production, and post-production services of
multimedia entertainment content; entertainment services in
the nature of development, creation, production, and
post-production services of television shows, documentary
programs and videos.
18.
Systems and methods for identifying and segmenting objects from images
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.
41 - Education, entertainment, sporting and cultural services
Goods & Services
(1) Entertainment media production services for the Internet; entertainment services, namely, multimedia production services; film and video production consulting services; media production services, namely, production of video, film, Internet and television entertainment content; entertainment services in the nature of development, creation, production, and post-production services of multimedia entertainment content; entertainment services in the nature of development, creation, production, and post-production services of television shows, documentary programs and videos.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing subscription-based temporary use of on-line non-downloadable software for providing access to databases that contain the results of genetic testing and related genealogical or family history information; Providing subscription-based application service provider services featuring software for use in data management, data storage, data analysis, user identification, and membership identification, all in the fields of genetics and family history and genealogy; Providing subscription-based temporary use of non-downloadable software for use in creating, displaying, sharing and storing information and data in the fields of genetics and family history and genealogy; Providing subscription-based temporary use of non-downloadable computer software that enables family groups to create and maintain personalized websites for the purpose of sharing information regarding family members; DNA testing services for non-medical use, namely, DNA testing for investigating and learning about genealogical and family history; Computer services, namely, hosting and maintaining an online website for subscribers to access and share information and data in the fields of genetics and family history and genealogy
21.
MACHINE LEARNING MODELS FOR GENERATING TAGS IN UNSTRUCTURED TEXT
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.
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.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating and providing actionable data from newspaper articles identified and segmented from digital newspaper images. For example, the disclosed systems segment articles of a newspaper image by using specially designed models to generate polygons defining article boundaries within the newspaper image. In some cases, the disclosed systems further determine article text from a polygon of an article for additional processing to determine an article topic, determine an article type, predict entity names within the article, and/or predict a locality associated with the article.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating and providing actionable data from newspaper articles identified and segmented from digital newspaper images. For example, the disclosed systems segment articles of a newspaper image by using specially designed models to generate polygons defining article boundaries within the newspaper image. In some cases, the disclosed systems further determine article text from a polygon of an article for additional processing to determine an article topic, determine an article type, predict entity names within the article, and/or predict a locality associated with the article.
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.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
(1) Kits for scientific and research purposes comprised primarily of a sample collection tube, a swab for collecting a genetic sample, and an instruction manual for use in DNA testing for dogs (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations) (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations); DNA test kits for scientific and research use comprised of a sample collection apparatus for the testing and analysis of DNA and genetics in dogs; DNA collection kits for scientific and research use for the testing and analysis of DNA and genetics in dogs for the purposes of breed identification, ancestry determination, and trait identification. (1) Providing scientific analysis in the field of genetics; reporting services based upon the results of laboratory testing in the field of genetics; DNA testing and DNA analysis services for non-medical use; providing a website featuring temporary use of non-downloadable software for providing results of DNA tests and genetic analyses in dogs for the purposes of breed identification, ancestry determination, and trait identification (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations); providing scientific analysis and informational reports based upon results of laboratory testing in the field of canine genetics for the purposes of breed identification, ancestry determination, and trait identification; providing online computer databases featuring information based on the results of DNA testing and genetic analyses in dogs for research purposes (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations); computer services, namely, hosting and maintaining an online website for others to access and share information and data in the field of pet genealogy; providing temporary use of non-downloadable software for use in creating, displaying, sharing and storing information and data in the field of pet genealogy; application service provider services featuring software allowing users to generate information and view analyses based upon results of canine genetic testing.
(2) Providing an online resource center featuring information in the field of pet genealogy (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations); providing pet genealogical information, namely, information services in the nature of retrieving, recording and reviewing pet breed identification, ancestral data, and physical traits via the internet.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
(1) Kits for scientific and research purposes comprised primarily of a sample collection tube, a swab for collecting a genetic sample, and an instruction manual for use in DNA testing for dogs (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations); DNA test kits for scientific and research use comprised of a sample collection apparatus for the testing and analysis of DNA and genetics in dogs; DNA collection kits for scientific and research use for the testing and analysis of DNA and genetics in dogs for the purposes of breed identification, ancestry determination, and trait identification (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations). (1) Providing scientific analysis in the field of genetics; reporting services based upon the results of laboratory testing in the field of genetics; DNA testing and DNA analysis services for non-medical use; providing a website featuring temporary use of non-downloadable software for providing results of DNA tests and genetic analyses in dogs for the purposes of breed identification, ancestry determination, and trait identification (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations); providing scientific analysis and informational reports based upon results of laboratory testing in the field of canine genetics for the purposes of breed identification, ancestry determination, and trait identification; providing online computer databases featuring information based on the results of DNA testing and genetic analyses in dogs for research purposes (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations); computer services, namely, hosting and maintaining an online website for others to access and share information and data in the field of pet genealogy; providing temporary use of non-downloadable software for use in creating, displaying, sharing and storing information and data in the field of pet genealogy; application service provider services featuring software allowing users to generate information and view analyses based upon results of canine genetic testing.
(2) Providing an online resource center featuring information in the field of pet genealogy (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations); providing pet genealogical information, namely, information services in the nature of retrieving, recording and reviewing pet breed identification, ancestral data, and physical traits via the internet.
28.
MACHINE-LEARNING BASED AUTOMATED DOCUMENT INTEGRATION INTO GENEALOGICAL TREES
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.
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.
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.
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. The computing server may cause a graphical user interface to display a representation of a historical network among the plurality of deceased individuals that are connected through the non-familial relationship.
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. The computing server may cause a graphical user interface to display a representation of a historical network among the plurality of deceased individuals that are connected through the non-familial relationship.
41 - Education, entertainment, sporting and cultural services
Goods & Services
Entertainment media production services for the Internet; Entertainment services, namely, multimedia production services; Film and video production consulting services; Media production services, namely, production of video, film, Internet and television entertainment content; Entertainment services in the nature of development, creation, production, and post-production services of multimedia entertainment content; Entertainment services in the nature of development, creation, production, and post-production services of television shows, documentary programs and videos
41 - Education, entertainment, sporting and cultural services
Goods & Services
Providing online non-downloadable videos in the field of genealogy and family history; Entertainment services, namely, production and distribution of documentary programs and videos; Entertainment services, namely, an ongoing series featuring personal stories about the ancestry and family history of a featured celebrity, athlete, or social media influencer delivered by the internet
A method or a system for classifying users into a plurality of categories. The system uses a first machine learning (ML) model to segment users into a first plurality of groups based in part on a first set of features, indicating relative research-skill levels of the respective users. The system uses a second ML model to segment users into a second plurality of groups based in part on a second set of features, indicating relative engagement levels of the respective users. The system then uses a third ML model to classify the plurality of users into a plurality of classes based in part on the research-skill levels and the engagement levels of the respective users, and selects and presents content to the user based in part on their classifications.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Kits for scientific and research purposes comprised primarily of a sample collection tube, a swab for collecting a genetic sample, and an instruction manual for use in DNA testing for dogs; DNA test kits for scientific and research use comprised of a sample collection apparatus, namely, a swab for collecting a genetic sample and a collection tube, for the testing and analysis of DNA and genetics in dogs; DNA collection kits comprised of collection tubes and swabs for collecting genetic samples from dogs, collection envelopes, and instruction manuals for using DNA collection kits for scientific and research use for the testing and analysis of DNA and genetics in dogs for the purposes of breed identification, ancestry determination, and trait identification Providing scientific analysis in the field of genetics; Reporting services based upon the results of laboratory testing in the field of genetics; DNA testing and DNA analysis services for non-medical use; Providing a website featuring temporary use of nondownloadable software for providing results of DNA tests and genetic analyses in dogs for the purposes of breed identification, ancestry determination, and trait identification; Providing scientific analysis and informational reports based upon results of laboratory testing in the field of canine genetics for the purposes of breed identification, ancestry determination, and trait identification; Providing online computer databases featuring information based on the results of DNA testing and genetic analyses in dogs for research purposes; Computer services, namely, hosting and maintaining an online website for others to access and share information and data in the field of pet genealogy; providing temporary use of non-downloadable software for use in creating, displaying, sharing and storing information and data in the field of pet genealogy; Application service provider services featuring software allowing users to generate information and view analyses based upon results of canine genetic testing Providing an online resource center featuring information in the field of pet genealogy; Providing pet genealogical information, namely, information services in the nature of retrieving, recording and reviewing pet breed identification, ancestral data, and physical traits via the internet
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Kits for scientific and research purposes comprised primarily of a sample collection tube, a swab for collecting a genetic sample, and an instruction manual for use in DNA testing for dogs; DNA test kits for scientific and research use comprised of a sample collection apparatus, namely, a swab for collecting a genetic sample and a collection tube, for the testing and analysis of DNA and genetics in dogs; DNA collection kits comprised of collection tubes and swabs for collecting genetic samples from dogs, collection envelopes, and instruction manuals for using DNA collection kits for scientific and research use for the testing and analysis of DNA and genetics in dogs for the purposes of breed identification, ancestry determination, and trait identification Providing scientific analysis in the field of genetics; Reporting services based upon the results of laboratory testing in the field of genetics; DNA testing and DNA analysis services for non-medical use; Providing a website featuring temporary use of non-downloadable software for providing results of DNA tests and genetic analyses in dogs for the purposes of breed identification, ancestry determination, and trait identification; Providing scientific analysis and informational reports based upon results of laboratory testing in the field of canine genetics for the purposes of breed identification, ancestry determination, and trait identification; Providing online computer databases featuring information based on the results of DNA testing and genetic analyses in dogs for research purposes; Computer services, namely, hosting and maintaining an online website for others to access and share information and data in the field of pet genealogy; providing temporary use of non-downloadable software for use in creating, displaying, sharing and storing information and data in the field of pet genealogy; Application service provider services featuring software allowing users to generate information and view analyses based upon results of canine genetic testing Providing an online resource center featuring information in the field of pet genealogy; Providing pet genealogical information, namely, information services in the nature of retrieving, recording and reviewing pet breed identification, ancestral data, and physical traits via the internet
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.
G06F 18/2113 - Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
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.
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.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
41.
IMAGE IDENTIFICATION, RETRIEVAL, TRANSFORMATION, AND ARRANGEMENT
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.
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.
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.
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/56 - Extraction of image or video features relating to colour
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
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.
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06V 30/262 - Techniques for post-processing, e.g. correcting the recognition result using context analysis, e.g. lexical, syntactic or semantic context
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 30/416 - Extracting the logical structure, e.g. chapters, sections or page numbersIdentifying elements of the document, e.g. authors
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
45.
EXTRACTION OF KEYPHRASES FROM GENEALOGICAL DESCRIPTIONS
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.
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.
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.
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.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
Disclosed herein relates to example embodiments for recognizing handwritten information in a genealogical record. A computing server may receive a genealogical record. The genealogical record may take the form of an image of a physical form having a structured layout, fields, and handwritten information. The computing server may divide the genealogical record into a plurality of areas based on the structured layout. The computing server may identify, for a particular area, a type of field that is included within the particular area. The computing server may select a handwriting recognition model for identifying the handwritten information in the particular area. The handwriting recognition model may be selected based on the type of the field. The computing server may input an image of the particular area to the handwriting recognition model to generate text of the handwritten information. The computing server may store the text of the handwritten information.
G06V 30/412 - Layout analysis of documents structured with printed lines or input boxes, e.g. business forms or tables
G06V 30/18 - Extraction of features or characteristics of the image
G06V 30/226 - Character recognition characterised by the type of writing of cursive writing
G06V 30/414 - Extracting the geometrical structure, e.g. layout treeBlock segmentation, e.g. bounding boxes for graphics or text
G06Q 90/00 - Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
50.
Handwriting recognition pipelines for genealogical records
Disclosed herein relates to example embodiments for recognizing handwritten information in a genealogical record. A computing server may receive a genealogical record. The genealogical record may take the form of an image of a physical form having a structured layout, fields, and handwritten information. The computing server may divide the genealogical record into a plurality of areas based on the structured layout. The computing server may identify, for a particular area, a type of field that is included within the particular area. The computing server may select a handwriting recognition model for identifying the handwritten information in the particular area. The handwriting recognition model may be selected based on the type of the field. The computing server may input an image of the particular area to the handwriting recognition model to generate text of the handwritten information. The computing server may store the text of the handwritten information.
Disclosed herein relates to example embodiments for recognizing handwritten information in a genealogical record. A computing server may receive a genealogical record. The genealogical record may take the form of an image of a physical form having a structured layout, fields, and handwritten information. The computing server may divide the genealogical record into a plurality of areas based on the structured layout. The computing server may identify, for a particular area, a type of field that is included within the particular area. The computing server may select a handwriting recognition model for identifying the handwritten information in the particular area. The handwriting recognition model may be selected based on the type of the field. The computing server may input an image of the particular area to the handwriting recognition model to generate text of the handwritten information. The computing server may store the text of the handwritten information.
G06V 30/412 - Layout analysis of documents structured with printed lines or input boxes, e.g. business forms or tables
G06V 30/414 - Extracting the geometrical structure, e.g. layout treeBlock segmentation, e.g. bounding boxes for graphics or text
G06V 30/226 - Character recognition characterised by the type of writing of cursive writing
G06V 30/18 - Extraction of features or characteristics of the image
G06Q 90/00 - Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Downloadable computer software for providing access to
databases that contain the results of genetic analysis and
family history and genealogical data; downloadable computer
software for use in data management, data storage, data
analysis, and report generation, all in the fields of
genetics and family history and genealogy; downloadable
computer software to allow users to generate information and
view analyses based upon results of genetic testing;
downloadable mobile applications for researching and
managing genetic and genealogical information; downloadable
publications in the nature of electronic reports in the
fields of genotyping and genealogy. Application service provider services featuring software for
providing access to databases that contain the results of
genetic analysis and family history and genealogical data;
application service provider services featuring software for
use in data management, data storage, data analysis, and
report generation, all in the fields of genetics and family
history and genealogy; application service provider services
featuring software allowing users to generate information
and view analyses based upon results of genetic testing;
providing online non-downloadable software for providing
access to databases that contain the results of genetic
analysis and family history and genealogical data; providing
online non-downloadable software for use in data management,
data storage, data analysis, and report generation, all in
the fields of genetics and family history and genealogy;
providing online non-downloadable software to allow users to
generate information and view analyses based upon results of
genetic testing; reporting services based upon the results
of laboratory testing in the fields of genetics and family
history and genealogy; providing scientific analysis and
informational reports based upon results of laboratory
testing in the field of genetics; providing information
based on the results of genetic testing from online computer
databases; providing information that contain aggregated
results of genotyping from online computer databases. Providing information in the fields of genetics and family
history and genealogy from an online resource center;
provision of information in the fields of personal
historical data and information, genealogy, and family
history.
53.
DOMAIN KNOWLEDGE GUIDED SELECTION OF NODES FOR ADDITION TO DATA TREES
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.
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.
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.
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.
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.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
(1) Downloadable computer software for providing access to databases that contain the results of genetic analysis and family history and genealogical data; downloadable computer software for use in data management, data storage, data analysis, and report generation, all in the fields of genetics and family history and genealogy; downloadable computer software to allow users to generate information and view analyses based upon results of genetic testing; downloadable mobile applications for researching and managing genetic and genealogical information; downloadable publications in the nature of electronic reports in the fields of genotyping and genealogy. (1) Application service provider services featuring software for providing access to databases that contain the results of genetic analysis and family history and genealogical data; application service provider services featuring software for use in data management, data storage, data analysis, and report generation, all in the fields of genetics and family history and genealogy; application service provider services featuring software allowing users to generate information and view analyses based upon results of genetic testing; providing online non-downloadable software for providing access to databases that contain the results of genetic analysis and family history and genealogical data; providing online non-downloadable software for use in data management, data storage, data analysis, and report generation, all in the fields of genetics and family history and genealogy; providing online non-downloadable software to allow users to generate information and view analyses based upon results of genetic testing; reporting services based upon the results of laboratory testing in the fields of genetics and family history and genealogy; providing scientific analysis and informational reports based upon results of laboratory testing in the field of genetics; providing information based on the results of genetic testing from online computer databases; providing information that contain aggregated results of genotyping from online computer databases.
(2) Providing information in the fields of genetics and family history and genealogy from an online resource center; provision of information in the fields of personal historical data and information, genealogy, and family history.
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.
A simplified handwriting recognition approach includes a first network (100) comprising convolutional neural network (150) comprising one or more convolutional layers (104, 106, 108, 110, 112, 114, 116) and one or more max-pooling layers. The first network (100) receives an input image (102) of handwriting and outputs an embedding (175) based thereon. A second network (200) comprises a network of cascaded convolutional layers including one or more subnetworks (201, 211, 221, 231) configured to receive an embedding (175) of a handwriting image (102) and output one or more character predictions (210, 220, 230, 240). The subnetworks (201, 211, 221, 231) are configured to downsample and flatten the embedding (175) to a feature map and then a vector before passing the vector to a dense neural network (209, 219, 229, 239) for character prediction. Certain subnetworks (211, 221, 231) are configured to concatenate an input embedding with an upsampled version of the feature map.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 30/18 - Extraction of features or characteristics of the image
G06V 30/226 - Character recognition characterised by the type of writing of cursive writing
A simplified handwriting recognition approach includes a first network comprising convolutional neural network comprising one or more convolutional layers and one or more max-pooling layers. The first network receives an input image of handwriting and outputs an embedding based thereon. A second network comprises a network of cascaded convolutional layers including one or more subnetworks configured to receive an embedding of a handwriting image and output one or more character predictions. The subnetworks are configured to downsample and flatten the embedding to a feature map and then a vector before passing the vector to a dense neural network for character prediction. Certain subnetworks are configured to concatenate an input embedding with an upsampled version of the feature map.
A simplified handwriting recognition approach includes a first network (100) comprising convolutional neural network (150) comprising one or more convolutional layers (104, 106, 108, 110, 112, 114, 116) and one or more max-pooling layers. The first network (100) receives an input image (102) of handwriting and outputs an embedding (175) based thereon. A second network (200) comprises a network of cascaded convolutional layers including one or more subnetworks (201, 211, 221, 231) configured to receive an embedding (175) of a handwriting image (102) and output one or more character predictions (210, 220, 230, 240). The subnetworks (201, 211, 221, 231) are configured to downsample and flatten the embedding (175) to a feature map and then a vector before passing the vector to a dense neural network (209, 219, 229, 239) for character prediction. Certain subnetworks (211, 221, 231) are configured to concatenate an input embedding with an upsampled version of the feature map.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 30/18 - Extraction of features or characteristics of the image
G06V 30/226 - Character recognition characterised by the type of writing of cursive writing
63.
IMPROVING HANDWRITING RECOGNITION WITH LANGUAGE MODELING
Systems and methods for handwriting recognition using language modeling facilitate improved results by using a trained language model (276) to improve results from a handwriting recognition machine learning model (204). The language model (276) may be a character-based language model trained on a dataset pertinent to field values on which the handwriting recognition model (204) is to be used. A loss prediction module (256) may be trained with the handwriting recognition model (204) and/or the language model (276) and used to determine whether a prediction (210) from the handwriting recognition model (204) should be refined by passing the prediction (210) through the trained language model (276).
Systems and methods for handwriting recognition using language modeling facilitate improved results by using a trained language model (276) to improve results from a handwriting recognition machine learning model (204). The language model (276) may be a character-based language model trained on a dataset pertinent to field values on which the handwriting recognition model (204) is to be used. A loss prediction module (256) may be trained with the handwriting recognition model (204) and/or the language model (276) and used to determine whether a prediction (210) from the handwriting recognition model (204) should be refined by passing the prediction (210) through the trained language model (276).
Systems and methods for handwriting recognition using language modeling facilitate improved results by using a trained language model to improve results from a handwriting recognition machine learning model. The language model may be a character-based language model trained on a dataset pertinent to field values on which the handwriting recognition model is to be used. A loss prediction module may be trained with the handwriting recognition model and/or the language model and used to determine whether a prediction from the handwriting recognition model should be refined by passing the prediction through the trained language model.
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06N 3/04 - Architecture, e.g. interconnection topology
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 40/30 - Writer recognitionReading and verifying signatures
66.
Image captioning with weakly-supervised attention penalty
Techniques for training a machine-learning (ML) model for captioning images are disclosed. A plurality of feature vectors and a plurality of visual attention maps are generated by a visual model of the ML model based on an input image. Each of the plurality of feature vectors correspond to different regions of the input image. A plurality of caption attention maps are generated by an attention model of the ML model based on the plurality of feature vectors. An attention penalty is calculated based on a comparison between the caption attention maps and the visual attention maps. A loss function is calculated based on the attention penalty. One or both of the visual model and the attention model are trained using the loss function.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
G06V 20/20 - ScenesScene-specific elements in augmented reality scenes
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Downloadable computer software for providing access to databases that contain the results of genetic analysis and family history and genealogical data; downloadable computer software for use in data management, data storage, data analysis, and report generation, all in the fields of genetics and family history and genealogy; downloadable computer software to allow users to generate information and view analyses based upon results of genetic testing; downloadable mobile applications for researching and managing genetic and genealogical information; downloadable publications in the nature of electronic reports in the fields of genotyping and genealogy Application service provider services featuring software for providing access to databases that contain the results of genetic analysis and family history and genealogical data; Application service provider services featuring software for use in data management, data storage, data analysis, and report generation, all in the fields of genetics and family history and genealogy; Application service provider services featuring software allowing users to generate information and view analyses based upon results of genetic testing; Providing online non-downloadable software for providing access to databases that contain the results of genetic analysis and family history and genealogical data; Providing online non-downloadable software for use in data management, data storage, data analysis, and report generation, all in the fields of genetics and family history and genealogy; Providing online non-downloadable software to allow users to generate information and view analyses based upon results of genetic testing; Reporting services, namely, providing scientific information based upon the results of laboratory testing in the fields of genetics and family history and genealogy; Providing scientific analysis and informational reports, namely, providing scientific information based upon results of laboratory testing in the field of genetics Provision of genealogical information in the fields of personal historical data and information, genealogy, and family history
68.
Systems and methods for identifying and segmenting objects from images
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 said 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.
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 said 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.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
70.
SYSTEMS AND METHODS FOR IDENTIFYING AND SEGMENTING OBJECTS FROM IMAGES
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 said 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.
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.
09 - Scientific and electric apparatus and instruments
Goods & Services
Downloadable software for organizing and viewing digital images and photographs in the field of genealogy and family history; downloadable software for creating, managing, recording, searching, indexing, filtering, and retrieving image files; downloadable software for use in creating, displaying, sharing, and storing presentations in the field of genealogy and family history; downloadable software for uploading, scanning, digitizing, viewing, organizing, sharing, and editing photographs associated with genealogical databases and family trees
42 - Scientific, technological and industrial services, research and design
Goods & Services
Application service provider featuring software for use in organizing and viewing digital images and photographs in the field of genealogy and family history; providing temporary use of non-downloadable computer software for use in organizing and viewing digital images and photographs in the field of genealogy and family history; application service provider services featuring software for use in creating, displaying, sharing, and storing presentations in the field of genealogy and family history; providing temporary use of non-downloadable computer software for use in creating, displaying, sharing, and storing presentations in the field of genealogy and family history; computer services, namely, hosting and maintaining an online website for others to access photo albums; application service provider featuring software for uploading, scanning, digitizing, viewing, organizing, sharing, and editing photographs with genealogical databases and family trees; providing temporary use of non-downloadable software applications for uploading, scanning, digitizing, viewing, organizing, sharing, and editing photographs associated with genealogical databases and family trees; computer services, namely, hosting of digital content on the internet in the field of genealogy and family history
74.
Providing grave information using augmented reality
Augmented reality is used to display graphical elements overlaid on a continually updating image of an area around an augmented reality device (e.g., a mobile device). The graphical element may contain geographical location information about a grave of an ancestor and/or biographical information about the ancestor. The continually updating image is captured by a camera of the augmented reality device and updates in response to time and motion of the augmented reality device. Based on orientation data and geographical location data collected by the augmented reality device, the graphical element is updated and displayed on the mobile device.
41 - Education, entertainment, sporting and cultural services
Goods & Services
Providing group training in the field of organizational effectiveness featuring team building activities; arranging and conducting workshops, seminars, and training in the field of genealogy, family history, and culture; arranging and conducting workshops, seminars, and training in engagement, trust, and accountability; providing group training in the field of organizational effectiveness featuring team building activities, namely arranging and conducting customized corporate team building events based on applicable professional genealogy research and cultural content
36 - Financial, insurance and real estate services
41 - Education, entertainment, sporting and cultural services
Goods & Services
Providing grants to classrooms and schools Educational services, namely, developing educational lesson plans for others in the field of history; providing online publications, namely, magazines, newspapers, resource guides and journals featuring lesson plans, course materials, articles, personal narratives, historical records, reports, and charts in the field of history
Described herein are systems, methods, and other techniques for segmenting an input text. A set of tokens are extracted from the input text. Token representations are computed for the set of tokens. The token representations are provided to a machine learning model that generates a set of label predictions corresponding to the set of tokens. The machine learning model was previously trained to generate label predictions in response to being provided input token representations. Each of the set of label predictions indicates a position of a particular token of the set of tokens with respect to a particular segment. One or more segments within the input text are determined based on the set of label predictions.
Described herein are systems, methods, and other techniques for segmenting an input text. A set of tokens are extracted from the input text. Token representations are computed for the set of tokens. The token representations are provided to a machine learning model that generates a set of label predictions corresponding to the set of tokens. The machine learning model was previously trained to generate label predictions in response to being provided input token representations. Each of the set of label predictions indicates a position of a particular token of the set of tokens with respect to a particular segment. One or more segments within the input text are determined based on the set of label predictions.
Systems, methods, and other techniques for genealogical entity resolution. The systems obtain first tree data and second tree data, the first tree data corresponding to a first tree person and the second tree data corresponding to a second tree person. The systems extract a set of features from the first tree data and the second tree data. The systems further generate an individual-level similarity score for each possible pairing of tree persons based on the set of features to identify a set of most-similar tree persons based on the individual-level similarity score for each possible pairing. The systems also provide a plurality of individual-level similarity scores for the set of most-similar tree persons 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.
Described herein are systems, methods, and other techniques for segmenting an input text. A set of tokens are extracted from the input text. Token representations are computed for the set of tokens. The token representations are provided to a machine learning model that generates a set of label predictions corresponding to the set of tokens. The machine learning model was previously trained to generate label predictions in response to being provided input token representations. Each of the set of label predictions indicates a position of a particular token of the set of tokens with respect to a particular segment. One or more segments within the input text are determined based on the set of label predictions.
41 - Education, entertainment, sporting and cultural services
Goods & Services
Entertainment services, namely, an ongoing series in the field of genealogy and family history provided through webcasts and non-downloadable videos; educational services, namely, providing online, non-downloadable videos in the field of genealogy and family history
82.
MULTICLASS CLASSIFICATION WITH DIVERSIFIED PRECISION AND RECALL WEIGHTINGS
Described herein are systems, methods, and other techniques for evaluating a classifier model. The classifier model may be provided with a set of elements to be classified into N classes. Classification results may be obtained from the classifier model. N class-specific precisions and N class-specific recalls for the N classes may be computed based on the classification results. N class-specific precision weights and N class-specific recall weights corresponding to the N classes may be obtained. A weighted f-measure may be computed by weighting the N class-specific precisions with the N class-specific precision weights and weighting the N class-specific recalls with the N class-specific recall weights.
Described herein are systems, methods, and other techniques for evaluating a classifier model. The classifier model may be provided with a set of elements to be classified into N classes. Classification results may be obtained from the classifier model. N class-specific precisions and N class-specific recalls for the N classes may be computed based on the classification results. N class-specific precision weights and N class-specific recall weights corresponding to the N classes may be obtained. A weighted f-measure may be computed by weighting the N class-specific precisions with the N class-specific precision weights and weighting the N class-specific recalls with the N class-specific recall weights.
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
G06N 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
Described herein are systems, methods, and other techniques for training a machine learning (ML) model to jointly perform named entity recognition (NER) and relation extraction (RE) on an input text. A set of hyperparameters for the ML model are set to a first set of values. The ML model is trained using a training dataset to produce a first training result. The set of hyperparameters are modified from the first set of values to a second set of values. The ML model is trained using the training dataset to produce a second training result. Either the first set of values or the second set of values are selected and used for the set of hyperparameters for the ML model based on a comparison between the first training result and the second training result.
Described herein are systems, methods, and other techniques for training a machine learning (ML) model to jointly perform named entity recognition (NER) and relation extraction (RE) on an input text. A set of hyperparameters for the ML model are set to a first set of values. The ML model is trained using a training dataset to produce a first training result. The set of hyperparameters are modified from the first set of values to a second set of values. The ML model is trained using the training dataset to produce a second training result. Either the first set of values or the second set of values are selected and used for the set of hyperparameters for the ML model based on a comparison between the first training result and the second training result.
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Providing access to computer databases of historical data and information related to genealogy and family history; providing access to computer databases of historical data and information related to military records and military histories Providing an online computer database in the field of history, namely, providing a searchable database focusing on historical armed conflicts and militaries and featuring primary source documents, images, and records related to historical armed conflicts, historical militaries, and persons associated with the same; providing webcasts in the field of historical data and information related to military records and military histories; online journals, namely, blogs in the field of historical data and information related to military records and military histories Computer services, namely, hosting and maintaining an online website for others to access and share information and data in the fields of historical data and information, genealogy, and family history; hosting of digital content on the internet, namely, hosting historical data and information and online journals and blogs in the field of historical data and information related to military records and military histories; computer services, namely, hosting and maintaining an online website for others to access and share information and data in the fields of historical data and information related to military records and military histories; digitization of documents Provision of information in the field of personal historical data and information, genealogy and family history; Provision of information in the field of personal historical data and information related to military records and military histories; provision of information resulting from educational research in the field of personal historical data and information related to military records and military histories; providing an online computer database in the field of personal historical data and information related to military records and military histories; Providing an online computer database in the field of genealogy and family history, namely, providing a searchable database featuring genealogical and family history information about participation in historical armed conflicts and militaries and featuring primary source documents, images, and records related to historical armed conflicts, historical militaries, and persons associated with the same
88.
JOINT EXTRACTION OF NAMED ENTITIES AND RELATIONS FROM TEXT USING MACHINE LEARNING MODELS
Described herein are systems, methods, and other techniques for training a machine learning (ML) model to jointly perform named entity recognition (NER) and relation extraction (RE) on an input text. A set of hyperparameters for the ML model are set to a first set of values. The ML model is trained using a training dataset and is evaluated to produce a first result. The set of hyperparameters are modified from the first set of values to a second set of values. The ML model is trained using the training dataset and is evaluated to produce a second result. Either the first set of values or the second set of values are selected and used for the set of hyperparameters for the ML model based on a comparison between the first result and the second result.
36 - Financial, insurance and real estate services
41 - Education, entertainment, sporting and cultural services
Goods & Services
Providing grants to classrooms and schools. Educational services, namely, developing educational lesson
plans for others in the field of history; providing online
non-downloadable publications, namely, magazines,
newspapers, resource guides and journals featuring lesson
plans, course materials, articles, personal narratives,
historical records, reports, and charts in the fields of
history.
90.
Ventral-dorsal neural networks: object detection via selective attention
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.
41 - Education, entertainment, sporting and cultural services
Goods & Services
Entertainment services, namely, production and distribution of a quiz show; Entertainment services, namely, an ongoing series featuring trivia about the participant's family history provided through online non-downloadable videos; Entertainment services, namely, the provision of continuing segments featuring questions and answers about the genealogical ancestry and family history of a featured celebrity, athlete, or social media influencer delivered by the internet; Providing online non-downloadable videos in the field of genealogy and family history
41 - Education, entertainment, sporting and cultural services
Goods & Services
Entertainment services in the nature of a non-fiction television programming series on topics relating to family stories told by family members to preserve their heritage.; Entertainment services, namely, storytelling; Entertainment services, namely, an ongoing series featuring ancestral anecdotes provided through online non-downloadable videos; Providing online non-downloadable videos in the field of geneology
93.
Clustering historical images using a convolutional neural net and labeled data bootstrapping
Systems and methods for classifying historical images. A feature extractor may create feature vectors corresponding to a plurality of images. A first classification of the plurality of images may be performed based on the plurality of feature vectors, which may include assigning a label to each of the plurality of images and assigning a probability for each of the assigned labels. The assigned probability for each of the assigned labels may be related to a statistical confidence that a particular assigned label is correctly assigned to a particular image. A subset of the plurality of images may be displayed to a display device. An input corresponding to replacement of an incorrect label with a corrected label for a certain image may be received from a user. A second classification of the plurality of images based on the input from the user may be performed.
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.
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.
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.
36 - Financial, insurance and real estate services
41 - Education, entertainment, sporting and cultural services
Goods & Services
(1) Providing grants to classrooms and schools.
(2) Educational services, namely, developing educational lesson plans for others in the field of history; providing online non-downloadable publications, namely, magazines, newspapers, resource guides and journals featuring lesson plans, course materials, articles, personal narratives, historical records, reports, and charts in the fields of history.
Described are methods for identification of likelihood of health outcomes such as the development of a medical condition using health histories from genetically related individuals. Embodiments include: receiving a first set of genetic data associated with the human subject; comparing the first set of genetic data to a plurality of sets of genetic data from a plurality of other individuals; identifying from the comparison a family network comprising individuals genetically related to the human subject as defined by identity by descent; receiving a set of health history data for each individual and each individual in the family network; analyzing the set of health history data to generate a health outcome score for the human subject, the health outcome score being a measure of risk for the human subject to develop a pre-defined health outcome that is associated with the health outcome score; and reporting the health outcome score.
G16B 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
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.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 30/262 - Techniques for post-processing, e.g. correcting the recognition result using context analysis, e.g. lexical, syntactic or semantic context
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.
