The present disclosure discloses methods and systems for automatically detecting Infrared (IR) security mark based on unknown halftone frequency information. The method includes receiving a document from a user including an IR security mark. The document is scanned. Then, one or more halftone frequencies associated with the IR security mark portion are estimated. Based on the estimation, the IR security mark portion is classified into a background region and the IR marked region including the IR security mark. The IR security mark is extracted and pixels falling in the IR marked region are reconstructed to identify content in the IR security mark. Finally, the identified content is compared with one or more pre-stored IR security marks to ascertain the presence of the IR security mark in the document for further assessment. This way, the method automatically detects the IR security mark in the document.

A Computer Vision (CV) model generated by a Machine Learning (ML) system may be retrained for more accurate computer image analysis in Robotic Process Automation (RPA). A designer application may receive a selection of a misidentified or non-identified graphical component in an image form a user, determine representative data of an area of the image that includes the selection, and transmit the representative data and the image to an image database. A reviewer may execute the CV model, or cause the CV model to be executed, to confirm that the error exists, and if so, send the image and a correct label to an ML system for retraining. While the CV model is being retrained, an alternative image recognition model may be used to identify the misidentified or non-identified graphical component.

A Computer Vision (CV) model generated by a Machine Learning (ML) system may be retrained for more accurate computer image analysis in Robotic Process Automation (RPA). A designer application may receive a selection of a misidentified or non-identified graphical component in an image form a user, determine representative data of an area of the image that includes the selection, and transmit the representative data and the image to an image database. A reviewer may execute the CV model, or cause the CV model to be executed, to confirm that the error exists, and if so, send the image and a correct label to an ML system for retraining. While the CV model is being retrained, an alternative image recognition model may be used to identify the misidentified or non-identified graphical component.

Systems, methods, and computer program products may be configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, for example, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.

Systems, methods, and computer program products may be configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, for example, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.

A method and a system for extracting information from a subpoena document are provided. The method includes: receiving a subpoena document; extracting raw text included in the subpoena document; identifying, based on the extracted raw text, entities that are named in the subpoena document; determining, based on the extracted raw text, first information that relates to a scope period, a law enforcement agency, and/or an investigative agent associated with the subpoena document; retrieving second information that relates to the identified entities from a customer database; and outputting a subset of the determined first information and a subset of the obtained second information. The method may also include using a weighted fuzzy name match algorithm to match the identified entities with the second information.

A medical device monitoring system and method extract information from screen images from medical device controllers, with a single OCR process invocation per screen image, despite critical information appearing in different screen locations, depending on which medical device controller's screen image is processed. For example, different software versions of the medical device controllers might display the same type of information in different screen locations. Copies of the critical screen information, one copy from each different screen location, are made in a mosaic image, and then the mosaic image is OCR processed to produce text results. Text is selectively extracted from the OCR text results, depending on contents of a selector field on the screen image, such as a software version number or a heart pump model identifier.

The present disclosure generally relates to web page analysis, and more particularly to a classification system for web pages. The classification system may classify a web page as malicious based upon one or more signatures generated for the web page. For example, the classification system may compare one or more signatures generated for a first web page to one or more signatures generated for a second web page, where the first web page and the second web page are the same web page at different times or different web pages. Based upon a similarity of the signatures, the classification system may output whether the first web page is malicious. For another example, the classification system may include a classification model that is trained based upon one or more signatures for one or more classified web pages. The classification model may output whether the web page is malicious.

The present disclosure generally relates to web page analysis, and more particularly to a classification system for web pages. The classification system may classify a web page as malicious based upon one or more signatures generated for the web page. For example, the classification system may compare one or more signatures generated for a first web page to one or more signatures generated for a second web page, where the first web page and the second web page are the same web page at different times or different web pages. Based upon a similarity of the signatures, the classification system may output whether the first web page is malicious. For another example, the classification system may include a classification model that is trained based upon one or more signatures for one or more classified web pages. The classification model may output whether the web page is malicious.

A data control system comprises a user terminal such as a cellular phone, or an assist appliance, or a combination thereof, and a server in communication with the user terminal. The user terminal acquires the name of a person and an identification data of the person for storage as a reference on an opportunity of the first meeting with the person, and acquires the identification data of the person on an opportunity of meeting again to inform the name of the person with visual and/or audio display if the identification data is in consistency with the stored reference. The reference is transmitted to a server which allows another person to receive the reference on the condition that the same person has given a self-introduction both to a user of the user terminal and the another person to keep privacy of the same person against unknown persons.