A machine learning system may be implemented as a set of trained models. A set of trained models, for example, a deep learning system, is disclosed wherein one or more types of media input may be analyzed to determine an associated engagement of the one or more types of media input.
Please help me write a proper abstract based on the patent claims. CLAIM: 1.-3. (canceled) 4. An engagement estimator system to estimate an engagement level for media input, the system including: a first level comprising a plurality of trained model recursive neural networks including at least: a first trained model recursive neural network trained to determine a first engagement, including a social response to media content in text portions of the media input; and a second trained model recursive neural network trained to determine a second engagement, including a social response to media content in image portions of the media input; wherein each of the trained model recursive neural networks provides as output a set of possible engagement vectors appropriate to the media input portion applied to each respective trained model recursive neural network and a metadata set of confidence levels corresponding to the possible engagement vectors; and a second level comprising at least: a single trained model recursive neural network trained to process an input including select ones of the set of possible engagement vectors appropriate to each media portion applied to the plurality of trained model recursive neural networks of the first level, the selection in accordance with the metadata set of confidence levels corresponding to the possible engagement vectors; wherein the single trained model recursive neural network provides as output an engagement output for the set of media input; and wherein the trained model recursive neural networks of the first level and the trained model recursive neural network of the second level are trained by receiving repeated application of a training set including a set of media inputs and a set of engagement indicia and storing the set of media inputs and a set of engagement indicia in a tangible machine readable memory for use in estimating engagement of new media inputs; and wherein once trained, the trained model recursive neural networks of the first level and the trained model recursive neural network of the second level receive a prospective media input and use information from learning repeated application of a set of media inputs and a set of engagement indicia to predict an engagement for the prospective media input prior to the prospective media input being posted to a network server. 5. The system of claim 4, wherein the indicia includes at least one selected from: i. a number of likes, thumbs up, favorites, hearts, or other indicator of enthusiasm towards the content; ii. a number of forwards, reshares, re-links, or other indicator of desire to “share” the content with others; and iii. a number of followers, fans, or subscribers. 6. The system of claim 4, wherein the training set includes one or a combination of indicia subjected to a threshold to determine whether the indicia is engaging (“of interest”) or not engaging (“not interesting”). 7. The system of claim 4, wherein the second level determines that the text portion is not engaging but the image portion is engaging, the system providing indication that the text may be re-written. 8. The system of claim 4, wherein the second level determines that the image portion is not engaging but the text portion is engaging, the system providing indication that the image may be replaced. 9. The system of claim 4, the first level further including a third trained model recursive neural network trained to determine a third engagement, including a social response to media content in audio portions of the media input. 10. The system of claim 4, the first level further including a fourth trained model recursive neural network trained to determine a fourth engagement, including a social response to media content in video portions of the media input. 11. The system of claim 4, wherein the prospective media input includes a 140 character message. 12. The system of claim 4, wherein the prospective media input includes a status update in “tweet” form. 13. An engagement estimation method to estimate an engagement level for media input, the method including: storing for a first level a plurality of trained model recursive neural networks, the neural networks including at least: a first trained model recursive neural network trained to determine a first engagement, including a social response to media content in text portions of the media input; and a second trained model recursive neural network trained to determine a second engagement, including a social response to media content in image portions of the media input; wherein each of the trained model recursive neural networks provides as output a set of possible engagement vectors appropriate to the media input portion applied to each respective trained model and a metadata set of confidence levels corresponding to the possible engagement vectors; and storing for a second level at least a single trained model recursive neural network trained to process an input including select ones of the set of possible engagement vectors appropriate to each media portion applied to the plurality of trained model recursive neural networks of the first level, the selection in accordance with the metadata set of confidence levels corresponding to the possible engagement vectors; wherein the single trained model provides as output an engagement output for the set of media input; and wherein the trained model recursive neural networks of the first level and the trained model recursive neural network of the second level are trained by receiving repeated application of a training set including a set of media inputs and a set of engagement indicia and storing the set of media inputs and a set of engagement indicia in a tangible machine readable memory for use in estimating engagement of new media inputs; and wherein once trained, the trained model recursive neural networks of the first level and the trained model recursive neural network of the second level receive a prospective media input and use information from learning repeated application of a set of media inputs and a set of engagement indicia to predict an engagement for the prospective media input prior to the prospective media input being posted to a network server. 14. The method of claim 13, wherein the indicia includes at least one selected from: i. a number of likes, thumbs up, favorites, hearts, or other indicator of enthusiasm towards the content; ii. a number of forwards, reshares, re-links, or other indicator of desire to “share” the content with others; and iii. a number of followers, fans, or subscribers. 15. The method of claim 13, wherein the training set includes one or a combination of indicia subjected to a threshold to determine whether the indicia is engaging (“of interest”) or not engaging (“not interesting”). 16. The method of claim 13, wherein when the second level determines that the text portion is not engaging but the image portion is engaging, further including providing indication that the text may be re-written. 17. The method of claim 13, wherein the second level determines that the image portion is not engaging but the text portion is engaging, further including providing indication that the image may be replaced. 18. The method of claim 13, the storing for the first level further including storing a third trained model recursive neural network trained to determine a third engagement, including a social response to media content in audio portions of the media input. 19. The method of claim 13, the storing for the first level further including storing a fourth trained model recursive neural network trained to determine a fourth engagement, including a social response to media content in video portions of the media input. 20. The method of claim 13, wherein the prospective media input includes a 140 character message. 21. The method of claim 13, wherein the prospective media input includes a status update in “tweet” form. 22. A non-transitory computer readable storage medium impressed with computer program instructions to estimate an engagement level for media input, the instructions, when executed on a processor, implement a method comprising: storing for a first level a plurality of trained model recursive neural networks, the neural networks including at least: a first trained model recursive neural network trained to determine a first engagement, including a social response to media content in text portions of the media input; and a second trained model recursive neural network trained to determine a second engagement, including a social response to media content in image portions of the media input; wherein each of the trained model recursive neural networks provides as output a set of possible engagement vectors appropriate to the media input portion applied to each respective trained model and a metadata set of confidence levels corresponding to the possible engagement vectors; and storing for a second level at least a single trained model recursive neural network trained to process an input including select ones of the set of possible engagement vectors appropriate to each media portion applied to the plurality of trained model recursive neural networks of the first level, the selection in accordance with the metadata set of confidence levels corresponding to the possible engagement vectors; wherein the single trained model provides as output an engagement output for the set of media input; and wherein the trained model recursive neural networks of the first level and the trained model recursive neural network of the second level are trained by receiving repeated application of a training set including a set of media inputs and a set of engagement indicia and storing the set of media inputs and a set of engagement indicia in a tangible machine readable memory for use in estimating engagement of new media inputs; and wherein once trained, the trained model recursive neural networks of the first level and the trained model recursive neural network of the second level receive a prospective tweet and use information from learning repeated application of a set of media inputs and a set of engagement indicia to predict an engagement for the tweet prior to the tweet being posted to twitter.