Abstract: A system for regulating online merchandise link activity. The system may comprise a memory storing instructions and at least one processor configured to execute the instructions. The instructions may comprise: maintaining a set of items for online link processing, the items being associated with a website; selecting a first item from the set of items; receiving an electronic request from a first affiliate system related to the first item; registering the first affiliate system as associated with the first item; generating an online link associated with the first item and the registered affiliate system; detecting an actuation of the online link, the actuation being associated with digital activity; and, based on the detected actuation and digital activity, permitting or denying the first affiliate system from operating the online link.

Abstract: A system for regulating online merchandise link activity. The system may comprise a memory storing instructions and at least one processor configured to execute the instructions. The instructions may comprise: maintaining a set of items for online link processing, the items being associated with a website; selecting a first item from the set of items; receiving an electronic request from a first affiliate system related to the first item; registering the first affiliate system as associated with the first item; generating an online link associated with the first item and the registered affiliate system; detecting an actuation of the online link, the actuation being associated with digital activity; and, based on the detected actuation and digital activity, permitting or denying the first affiliate system from operating the online link.

Abstract: A system for regulating online merchandise link activity. The system may comprise a memory storing instructions and at least one processor configured to execute the instructions. The instructions may comprise: maintaining a set of items for online link processing, the items being associated with a website; selecting a first item from the set of items; receiving an electronic request from a first affiliate system related to the first item; registering the first affiliate system as associated with the first item; generating an online link associated with the first item and the registered affiliate system; detecting an actuation of the online link, the actuation being associated with digital activity; and, based on the detected actuation and digital activity, permitting or denying the first affiliate system from operating the online link.

Abstract: Under one aspect, first user input including free-form text is received in a first graphical user interface (GUI). A classification engine of the computer system incorporating a machine learning model classifies words of the free-form text into a male-biased class, a female-biased class, or a neutral class. At least one of the words is classified into the male-biased class or the female-biased class. At least one of the words classified into the male-biased class or the female-biased class is flagged in the first GUI. Second user input is received in the first GUI including at least one revision to at least one of the words of the free-form text classified into the male-biased class or the female-biased class responsive to the flagging. The revised free-form text is posted to a web site for display in a second GUI.

Abstract: A classification engine classifies words into gender-biased or neutral classes and outputs classification labels and probabilities the words were correctly classified. A dictionary engine generates a set of gender-biased words based on their labels, if their probability exceeds a threshold, if they are defined within a set of definitions. For each such word, the dictionary engine parses the set of definitions to extract the most frequent meaning of that word in a part of speech; parses the set of definitions to extract a synonym for the most frequent meaning of that word in that part of speech; and parses the classification engine output to determine that synonym's classification label. A synonym is selected that is labeled in the neutral class as a replacement for the gender-biased word. A dictionary entry is generated that includes the word, the part of speech, and the synonym.

Abstract: A computing system can detect a session trigger corresponding to execution of a service application on a computing device of a user, the session trigger initiating a user session of an application-based service. The system can execute one or more of a plurality of models corresponding to a unique objective for the application-based service, wherein each model runs real-time session data corresponding to the user session. The system can generate, from each model of the plurality of models, a session score indicating a priority level for the unique objective corresponding to the model. Based on the session score from each of the plurality of models, the system can transmit treatment data to the computing device of the user, the treatment data causing treatment content to be displayed on the computing device of the user.

Abstract: Under one aspect, first user input including free-form text is received in a first graphical user interface (GUI). A classification engine of the computer system incorporating a machine learning model classifies words of the free-form text into a male-biased class, a female-biased class, or a neutral class. At least one of the words is classified into the male-biased class or the female-biased class. At least one of the words classified into the male-biased class or the female-biased class is flagged in the first GUI. Second user input is received in the first GUI including at least one revision to at least one of the words of the free-form text classified into the male-biased class or the female-biased class responsive to the flagging. The revised free-form text is posted to a web site for display in a second GUI.

Abstract: Under one aspect, first user input including free-form text is received in a first graphical user interface (GUI). A classification engine of the computer system incorporating a machine learning model classifies words of the free-form text into a male-biased class, a female-biased class, or a neutral class. At least one of the words is classified into the male-biased class or the female-biased class. At least one of the words classified into the male-biased class or the female-biased class is flagged in the first GUI. Second user input is received in the first GUI including at least one revision to at least one of the words of the free-form text classified into the male-biased class or the female-biased class responsive to the flagging. The revised free-form text is posted to a web site for display in a second GUI.