Abstract: In some embodiments, a version control system receives a first version of a digital object and a second version of the digital object. The version control system extracts data from the first version of the digital object, and data from the second version of the digital object. The version control system utilizes a categorization machine learning model to generate structured representations of the first digital object and a second digital object. The version control system identifies differences between the two digital objects and utilizes a natural language technique to produce pseudo-instructions representing an update request. The version control system validates differences between the two digital objects and sends an auto-validation report to a client computing device when the differences are compliant with the pseudo-instructions.

Abstract: Techniques are disclosed for identifying the same online user across different communication networks, and further creating a unified profile for that user. The unified profile is an aggregation of publicly available user profile attributes across the different networks. In an embodiment, the techniques are implemented as a computer implemented methodology, including: (1) feature space analysis to identify relevant user features that allows for clusterization of the given target network(s), (2) unsupervised candidate selection to identify one or more candidate user profiles from each target network and that are likely belonging to a target user or so-called queried user, and (3) supervised user identification to identify a likely matching user profile for that target user from each target network. A unified user profile can then be built from data taken from all matched user profiles, and effectively allows a marketer to better understand that user and hence execute more informed targeting.

Abstract: The disclosed embodiments illustrate method and system for medical data processing for generating personalized advisory information. The method includes receiving a request comprising a generic clinical care pathway report and medical data of a patient from a user-computing device. Further, the medical data comprises one or more factors associated with a patient profile and a population segment of the patient. The method further includes extracting one or more influence factors and one or more population segments to generate one or more knowledge bases. The method further includes comparing the one or more factors with the extracted one or more influence factors and the one or more population profiles. The method further includes generating a personalized advisory information. The method further includes rendering the generated personalized advisory information on an interactive user interface of the user-computing device over the communication network for selection by a medical practitioner.

Abstract: Techniques are disclosed for identifying the same online user across different communication networks, and further creating a unified profile for that user. The unified profile is an aggregation of publicly available user profile attributes across the different networks. In an embodiment, the techniques are implemented as a computer implemented methodology, including: (1) feature space analysis to identify relevant user features that allows for clusterization of the given target network(s), (2) unsupervised candidate selection to identify one or more candidate user profiles from each target network and that are likely belonging to a target user or so-called queried user, and (3) supervised user identification to identify a likely matching user profile for that target user from each target network. A unified user profile can then be built from data taken from all matched user profiles, and effectively allows a marketer to better understand that user and hence execute more informed targeting.