The present disclosure is directed to systems and methods for analyzing digital images to determine alphanumeric strings depicted in the digital images. An electronic device may generate a set of filtered images using a received digital image. The electronic device may also perform an optical character recognition (OCR) technique on the set of filtered images, and may filter out any of the set of filtered images according to a set of rules. The electronic device may further identify a set of common elements representative of the alphanumeric string depicted in the digital image, and determine a machine-encoded alphanumeric string based on the set of common elements.

Systems and methods are provided for determining a media cost equivalent to attribute to the appearance of a sponsor's logo or other object in a media item, such as an image or video. This determination may be based on factors such as clarity, prominence, visibility, size, or placement of a sponsor logo or other object related to the sponsor within at least one media item. One or more machine learning models may be utilized to automatically identify objects within, and otherwise analyze, image data in order to determine sponsor value.

A method for displaying a video poster based on artificial intelligence is provided. The method includes obtaining a target video and at least one poster according to a user input, calculating a matching degree of an image description text of the at least one poster and a description text keyword of the target video, and sorting the at least one poster according to the matching degree in an order from high to low, to obtain a sorted poster sequence; performing element separation on one of the at least one poster with a highest matching degree in the poster sequence to obtain a main body element and a background element; and processing the main body element, the background element and title information of the target video according to requirements of a target scene to synthesize a new poster.

Techniques are described relating to automatic data standardization in a managed services domain of a cloud computing environment. An associated computer-implemented method includes receiving a dataset during a data onboarding procedure and classifying datapoints within the dataset. The method further includes applying a machine learning data standardization model to each classified datapoint within the dataset and deriving a proposed set of data standardization rules for the dataset based upon any standardization modification determined consequent to model application. Optionally, the method includes presenting the proposed set of data standardization rules for client review and, responsive to acceptance of the proposed set of data standardization rules, applying the proposed set of data standardization rules to the dataset. The method further includes, responsive to acceptance of the proposed set of data standardization rules, updating the machine learning data standardization model accordingly.

A system and method that enables users to provide authenticated medical records (e.g., vaccination records, viral anti-body test results, etc.) to a third-party (e.g., a venue) to gain access to the third-party is provided. In this way, the third party may confirm that the user is sufficiently immune to a particular disease (e.g., COVID-19) and may thereby minimize the threat of the user introducing the contagious disease to the third party. The system includes a biometric data recognition system that authenticates the identity of a user, a medical records acquisition system that acquires the medical records of the authenticated user, and a system for the displaying or otherwise providing the medical records to the third-party for review. The system also includes a system identification card that includes the user's contact information, alphanumeric characters associated with the user's driver's license number, medical records of the user, and other elements.

The present disclosure is directed to systems and methods for analyzing digital images to determine alphanumeric strings depicted in the digital images. An electronic device may generate a set of filtered images using a received digital image. The electronic device may also perform an optical character recognition (OCR) technique on the set of filtered images, and may filter out any of the set of filtered images according to a set of rules. The electronic device may further identify a set of common elements representative of the alphanumeric string depicted in the digital image, and determine a machine-encoded alphanumeric string based on the set of common elements

A computer-implemented tool for automated classification and interpretation of documents, such as life science documents supporting clinical trials, is configured to perform a combination of raw text, document construct, and image analyses to enhance classification accuracy by enabling a more comprehensive machine-based understanding of document content. The combination of analyses provides context for classification by leveraging relative spatial relationships among text and image elements, identifying characteristics and formatting of elements, and extracting additional metadata from the documents as compared to conventional automated classification tools.

Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

A method for testing medical data is provided. Each medical datum includes a plurality of information units and a plurality of separators, and the method includes the following steps: a. matching the medical data against a standard library including a plurality of patterns, a matching expression being: [\s\S][number/sequence/relation]&[\b|\B] (S101); and b. determining, based on a matching result of the step a, whether the medical datum is qualified (S102). A standardized standard library is first established, a matching result is obtained by matching the medical datum and the standard library for a non-initial boundary, an initial boundary, an information quantity, information sequences, a semantic relationship quantity, a character boundary, and a non-character boundary, and whether the medical datum meets a requirement is further determined according to the matching result.

The present disclosure is directed to systems and methods for analyzing digital images to determine alphanumeric strings depicted in the digital images. An electronic device may generate a set of filtered images using a received digital image. The electronic device may also perform an optical character recognition (OCR) technique on the set of filtered images, and may filter out any of the set of filtered images according to a set of rules. The electronic device may further identify a set of common elements representative of the alphanumeric string depicted in the digital image, and determine a machine-encoded alphanumeric string based on the set of common elements.