Abstract: Systems and methods are disclosed comprising techniques for signal processing, such as receiving a first digital artifact comprising unstructured alphanumeric signal data indicating contextual information associated with a decision logic condition, identifying a set of non-compliant alphanumeric signals from the unstructured alphanumeric signal data, generating a set of masking elements comprising a mapping to the set of non-compliant alphanumeric signals, generating a second digital artifact comprising alphanumeric signal data that substitutes the identified non-compliant alphanumeric signals with the set of masking elements, determining a set of signal domain categories for the second digital artifact corresponding to alphanumeric signal properties satisfied by the alphanumeric signal data of the second digital artifact, generating a composite alphanumeric signal for the second digital artifact based on the determined set of signal domain categories, and generating for display at a user interface a set of g

Abstract: A method of extracting meta-data from a document includes capturing style attributes from the document, identifying cell-wise location coordinates for text characters using page segmentation and border table extraction, and finding relationship between nearby cells using surrounding embedding by determining shortest distant text cell in top, left, right, and bottom direction. The method further includes applying Graph Convolution Network with Informative Attention (GCN-IA) for providing more attention to informative nodes for generating better representation of surrounding embedding and capturing a deep contextual meaning from text cells. A domain specific language model is utilized and improved by a domain aware tokenizer.

Abstract: A text recognition system causes a trained region encoder to determine a region of interest of an image file. The system modifies a first image associated with the first region of interest (e.g., parsed out from the first region) to generate a data augmentation entity that includes a modified image. Using a trained instance encoder, the system generates a first set of visual instances corresponding to the first region of interest image and a second set of visual instances corresponding to the data augmentation entity. The system generates the corresponding first and second sequences. By executing a self-supervised contrastive loss function on the first and second sequences, the system automatically updates a continual knowledge distillation model of the trained region encoder. The system provides the first sequence to an instance decoder to generate output text in response to the prompt.

Abstract: A galley cart restraint mechanism is provided. The galley cart restraint mechanism including a base portion, a post portion coupled to the base portion, and a foot portion coupled to the post portion. At least the foot portion is configured to engage with a floor track to prevent movement of the galley cart in at least a first direction.

Abstract: Disclosed herein is system and method for examining relevancy of documents. The system, based on request from the user extracts documents from one or more data sources. The system then obtains from the user, user intention information and user queries. The system then analyses each document with respect to user intention information in order to determine a relevancy level of each document. The relevancy level is indicated in the form of a ranking score. The system ranks and displays the documents to the user in the order of their scores. The system also highlights important excerpts from the documents and provides one or more responses to the one or more user queries submitted by the user for each and every document. Based on the received responses, user provides feedback for further training the system, thereby achieving better accuracy.

Abstract: A text recognition system receives a prompt and, based on the prompt, causes a trained region encoder to determine a first region of interest of an image file. The system modifies a first image associated with the first region of interest (e.g., parsed out from the first region) to generate a data augmentation entity that includes a modified image. Using a trained instance encoder, the system generates a first set of visual instances corresponding to the first region of interest image and a second set of visual instances corresponding to the data augmentation entity. The system generates the corresponding first and second sequences. By executing a self-supervised contrastive loss function on the first and second sequences, the system automatically updates a continual knowledge distillation model of the trained region encoder. The system provides the first sequence to an instance decoder to generate output text in response to the prompt.

Abstract: Disclosed herein is system and method for examining relevancy of documents. The system, based on request from the user extracts documents from one or more data sources. The system then obtains from the user, user intention information and user queries. The system then analyses each document with respect to user intention information in order to determine a relevancy level of each document. The relevancy level is indicated in the form of a ranking score. The system ranks and displays the documents to the user in the order of their scores. The system also highlights important excerpts from the documents and provides one or more responses to the one or more user queries submitted by the user for each and every document. Based on the received responses, user provides feedback for further training the system, thereby achieving better accuracy.