Systems and methods for classifying media according to user negative propensities

1. A system for classifying media according to user negative propensities, the system comprising a computing device, the computing device further configured to:

obtain a physiological state data as a function of a user input;

identify a user propensity for problematic behavior associated with a human subject as a function of the physiological state data, wherein the user propensity for problematic behavior identifies a problematic behavior from a predetermined plurality of problematic behaviors, wherein identifying the user propensity for problematic behavior further comprises:

training a behavior machine-learning model using a feature learning algorithm and a behavioral training set, wherein the behavioral training set includes a plurality of physiological state data correlated to a plurality of problematic behavior propensities; and

generating the user propensity for problematic behavior using the trained behavior machine-learning model as a function of the physiological state data;

receive a media item to be transmitted to a device operated by the human subject;

identify a principal theme associated with the media item, wherein identifying the principal theme further comprises:

training a media theme machine-learning classifier using a classification algorithm and media training data, wherein the media training data includes a plurality of media items correlated with a plurality of principal themes; and

generating the principal theme using the trained media theme machine-learning classifier as a function of the media element; and

block transmission of the media item to the device operated by the human subject as a function of the principal theme and the user propensity for problematic behavior.

2. The system of claim 1 , wherein the user input includes a biological extraction.

3. The system of claim 1 , wherein identifying the user propensity for problematic behavior involves querying a vice database.

4. The system of claim 1 , wherein identifying the principal theme includes the use of an object classifier.

5. The system of claim 1 , wherein identifying the principal theme includes extracting a plurality of media item content elements from the media item.

6. The system of claim 1 , wherein blocking transmission of the media item involves matching the principal theme to the user propensity for problematic behavior.

7. The system of claim 1 , wherein the computing device is further configured to:

extract, from the media item, a plurality of content elements;

determine that the principal theme does not match the user propensity for problematic behavior;

classify each content element of the plurality of content elements to an object of a plurality of objects using an object classifier; and

determine that an object of the plurality of objects matches the user propensity for problematic behavior.

8. The system of claim 7 , wherein the computing device is further configured to:

receive, from a remote device, an indication that the human subject is engaging in a problematic behavior associated with the user propensity for problematic behavior; and

block transmission of the media item to the device operated by the human subject.

9. A method for classifying media according to user negative propensities, the method comprising:

obtaining, by a computing device, a physiological state data as a function of a user input;

identifying, by the computing device, a user propensity for problematic behavior associated with a human subject as a function of the physiological state data, wherein the user propensity for problematic behavior identifies a problematic behavior from a predetermined plurality of problematic behaviors, wherein identifying the user propensity for problematic behavior further comprises:

training a behavior machine-learning model using a feature learning algorithm and a behavioral training set, wherein the behavioral training set includes a plurality of physiological state data correlated to a plurality of problematic behavior propensities; and

generating the user propensity for problematic behavior using the trained behavior machine-learning model as a function of the physiological state data;

receiving, by the computing device, a media item to be transmitted to a device operated by the human subject;

identifying, by the computing device, a principal theme associated with the media item, wherein identifying the principal theme further comprises:

training a media theme machine-learning classifier using a classification algorithm and media training data, wherein the media training data includes a plurality of media items correlated with a plurality of principal themes; and

generating the principal theme using the trained media theme machine-learning classifier as a function of the media element; and

blocking, by the computing device, transmission of the media item to the device operated by the human subject as a function of the principal theme and the user propensity for problematic behavior.

10. The method of claim 9 , wherein the user input includes a biological extraction.

11. The method of claim 9 , wherein identifying a user propensity for problematic behavior involves querying a vice database.

12. The method of claim 9 , wherein identifying the principal theme includes the use of an object classifier.

13. The method of claim 9 , wherein identifying the principal theme includes extracting a plurality of media item content elements from the media item.

14. The method of claim 9 , wherein blocking transmission of the media item involves matching the principal theme to the user propensity for problematic behavior.

15. The method of claim 9 , wherein the method further comprises:

extracting, from the media item, a plurality of content elements;

determining that the principal theme does not match the user propensity for problematic behavior;

classifying each content element of the plurality of content elements to an object of a plurality of objects using an object classifier; and

determining that an object of the plurality of objects matches the user propensity for problematic behavior.

16. The method of claim 15 , wherein the method further comprises:

receiving, from a remote device, an indication that the human subject is engaging in a problematic behavior associated with the user propensity for problematic behavior; and

blocking transmission of the media item to the device operated by the human subject.