A method for method for classification of virtual reality (VR) content for use in head mounted displays (HMDs). The method includes accessing a model that identifies a plurality of learned patterns associated with the generation of corresponding baseline VR content that is likely to cause discomfort. The method includes executing a first application to generate first VR content. The method includes extracting data associated with simulated user interactions with the first VR content, the extracted data generated during execution of the first application. The method includes comparing the extracted data to the model to identify one or more patterns in the extracted data matching at least one of the learned patterns from the model such that the one or more patterns are likely to cause discomfort.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for classification of virtual reality (VR) content for use in head mounted displays (HMDs), comprising: accessing a model that identifies a plurality of learned patterns associated with the generation of corresponding baseline VR content that is likely to cause discomfort; executing a first application to generate first VR content; extracting data associated with simulated user interactions with the first VR content, the extracted data generated during execution of the first application; and comparing the extracted data to the model to identify one or more patterns in the extracted data matching at least one of the learned patterns from the model such that the one or more identified patterns are likely to cause discomfort. 2. The method of claim 1, further comprising: determining a level of discomfort for the first VR content based on the model, wherein the model predicts a level of discomfort experienced by a prospective user when interacting with the first VR content; and assigning a discomfort classification for the first VR content based on the determined level of discomfort. 3. The method of claim 2, further comprising: provide notification of the discomfort classification to a prospective user. 4. The method of claim 2, further comprising: comparing the determined level of discomfort to a targeted level of discomfort designed for the first VR content; and providing notification of a mismatch when the determined level of discomfort does not match the targeted level of discomfort. 5. The method of claim 4, further comprising: providing a recommendation to change the first application when there is a mismatch. 6. The method of claim 1, wherein one of the identified patterns includes a pattern of images in the first VR content. 7. The method of claim 1, wherein one of the identified patterns includes a pattern of actions taken by a simulated user during execution of the first application. 8. The method of claim 1, wherein the discomfort classification is taken from a group of discomfort classifications including a first classification level indicating high discomfort and a second classification level indicating low or no discomfort. 9. The method of claim 1, further comprising: executing a plurality of testing applications for interaction by a plurality of testers to generate a plurality of baseline VR content; extracting data associated with tester interactions with the baseline VR content, the extracted data generated during execution of the plurality of testing applications; monitoring physiological measurements of the plurality of testers when interacting with the plurality of baseline VR content, wherein the plurality of baseline VR content is associated with a plurality of baseline discomfort reactions, wherein known patterns of physiological measurements are associated with expected discomfort reactions; applying the physiological measurements and the extracted data associated with tester interactions to a deep learning engine to generate the model; building the model by correlating the physiological measurements and the extracted data associated with tester interactions to determine the plurality of learned patterns that are likely to produce discomfort experiences in the testers based on physiological measurements that align with the plurality of baseline discomfort reactions. 10. The method of claim 9, wherein the physiological measurements include at least one of electrogastrography, galvanic skin resistance, alpha wave, heart rate, eye gaze, and head motion. 11. The method of claim 1, further comprising: classifying points in the first application by determining at least one point in the first application associated with at least one of the one or more patterns likely to cause discomfort. 12. A method for classification of virtual reality (VR) content for use in head mounted displays (HMDs), comprising: executing a plurality of testing applications for interaction by a plurality of testers to generate a plurality of baseline VR content; extracting testing data associated with tester interactions with the baseline VR content, the extracted data generated during execution of the plurality of testing applications; monitoring physiological measurements of the plurality of testers when interacting with the plurality of baseline VR content, wherein the plurality of baseline VR content is associated with a plurality of baseline discomfort reactions, wherein known patterns of physiological measurements are associated with expected discomfort reactions; applying the physiological measurements and the extracted testing data associated with tester interactions to a deep learning engine; and correlating the physiological measurements and the extracted testing data associated with tester interactions to determine the plurality of learned patterns that are likely to produce discomfort experiences in the testers based on physiological measurements that align with the plurality of baseline discomfort reactions. 13. The method of claim 12, further comprising: building a model that identifies the plurality of learned patterns associated with the generation of corresponding baseline VR content that are likely to cause discomfort. 14. The method of claim 12, wherein one of the learned patterns includes a pattern of images in the baseline VR content. 15. The method of claim 12, wherein one of the learned patterns includes a pattern of actions taken by one or more testers during execution of the plurality of testing applications. 16. The method of claim 12, further comprising: accessing the model that identifies the plurality of learned patterns associated with the generation of corresponding baseline VR content that is likely to cause discomfort; executing a first application to generate first VR content; extracting data associated with simulated user interactions with the first VR content, the extracted data generated during execution of the first application; and comparing the extracted data to the model to identify one or more patterns in the extracted data matching at least one of the learned patterns from the model such that the one or more identified patterns are likely to cause discomfort. 17. The method of claim 16, further comprising: determining a level of discomfort for the first VR content based on the model, wherein the model predicts a level of discomfort experienced by a prospective user when interacting with the first VR content; and assigning a discomfort classification for the first VR content based on the determined level of discomfort. 18. A method for classification of virtual reality (VR) content for use head mounted displays (HMDs), comprising: accessing a model that identifies a plurality of learned patterns associated with the generation of corresponding baseline VR content that is likely to cause discomfort; processing a first application to generate data associated with simulated user interactions with first VR content of the first application; and comparing the extracted data to the model to identify a pattern in the extracted data matching at least one of the learned patterns, such that the identified pattern is likely to cause discomfort. 19. The method of claim 18, wherein the processing a first application further comprises: executing the first application to generate the first VR content; and extracting the data associated with simulated user interactions. 20. The method of claim 18, further comprising: determining a level of discomfort for the first VR content based on the model, wherein the model predicts a level of discomfort experienced by a prospective user when interacting with the first VR content; and assigning a discomfort classification for the first VR content based on the determined level of discomfort. 21. The method of claim 18, further comprising: classifying points in the first application by determining at least one point in the first application associated with the pattern likely to cause discomfort.