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.

A system and method for developing applications (Apps) for automated assessment and analysis of processed biological samples. Such samples are obtained, combined with nutrient media and incubated. The incubated samples are imaged and the image information is classified according to predetermined criteria. The classified image information is then evaluated according to Apps derived from classified historical image information in a data base. The classified historical image information is compared with the classified image information to provide guidance on further processing of the biological sample through Apps tailored to process provide sample process guidance tailored to the classifications assigned to the image information.

Provided are an ink data generation apparatus, an ink data generation method, and an ink data generation program that are capable of improving ease of handling ink data when or after metadata is generated. An ink data generation apparatus performs a determination as to an inclusion relation between a first set and a second set by comparing stroke elements of first set data and stroke elements of second set data using the first set data and the second set data. The ink data generation apparatus generates first metadata for the first set described in a form that varies in accordance with a result of the determination.

A computer-implemented method for automatically detecting events and creating photo-product designs based on the events in a photo-product design system includes automatically identifying an event by an event detection module based on daily numbers of captured photos over a plurality of days, automatically selecting a photo-product type by an intelligent product design creation engine in the photo-product design system, calculating a daily weight for a photo product design in the photo-product type based on the daily numbers of captured photos, automatically determining a number of product photos allocated to each day based on associated daily weight, automatically selecting product photos from the captured photos each day at the event according to the number of product photos allocated to each day, and automatically creating a photo-product design for the event using the selected product photos.

A method for retrieving footprint images is provided, comprising: pre-training models; cleaning footprint data and conducting expansion pre-processing by using the pre-trained models, dividing the footprint data into multiple data sets; adjusting full connection layers and classification layers of the models; training the models again by using the data sets through the parameters of the pre-trained models; saving the models trained twice, removing the classification layer, executing a feature extraction for images in an image library and a retrieval library to form a feature index library; connecting the features extracted by three models to form fused features, establishing a fused feature vector index library; extracting the features of the images in the image library to be retrieved in advance, and establishing a feature vector library; calculating distances in the retrieval library and the image library when a single footprint image is inputted, thereby outputting the image with the highest similarity.

A method for retrieving footprint images is provided, comprising: pre-training models; cleaning footprint data and conducting expansion pre-processing by using the pre-trained models, dividing the footprint data into multiple data sets; adjusting full connection layers and classification layers of the models; training the models again by using the data sets through the parameters of the pre-trained models; saving the models trained twice, removing the classification layer, executing a feature extraction for images in an image library and a retrieval library to form a feature index library; connecting the features extracted by three models to form fused features, establishing a fused feature vector index library; extracting the features of the images in the image library to be retrieved in advance, and establishing a feature vector library; calculating distances in the retrieval library and the image library when a single footprint image is inputted, thereby outputting the image with the highest similarity.

Embodiments of the present invention are directed towards providing contextual tags for an image based on a contextual analysis of associated images captured in the same environment as the image. To determine contextual tags, content tags can be determined for images. The determined content tags can be associated with categories based on a contextual classification of the content tags. These associated content tags can then be designated as contextual tags for a respective category. To associate these contextual tags with the images, the images can be iterated through based on how the images relate to the contextual tags. For instance, when an image is associated with a category, the contextual tags classified into that category can be assigned to that image.

A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.

A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.

A method includes: acquiring an image of first piece of clothing to be collocated; determining information of one or more second piece of clothing for collocation with the first piece of clothing; determining clothing collocation images containing the information of the one or more second piece of clothing in the collocation image library; and selecting clothing collocation images matched with the image of the first piece of clothing from the determined clothing collocation images. The information of the one or more second piece of clothing is pre-marked clothing category information of clothing collocation images in a collocation image library.