A system combines inputs from human processing and machine processing, and employs machine learning to improve processing of individual tasks based on comparison of human processing results. Once performance of a particular task by machine processing reaches a threshold, the level of human processing used on that task is reduced.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: at a computer comprising a processor with access to executable instructions for, building a machine learning model by replacing solicited user actions performed by users on an object with additional user actions in the machine learning model over time, beginning at least in response to accuracy of the additional user actions being at or near accuracy of the solicited user actions and increasing in number as accuracy of the additional user actions improves relative to accuracy of the solicited user actions, the additional user actions predicted from a set of training data that includes the solicited user actions and information about the object. 2. The method of claim 1, further comprising: collecting the solicited user actions from a sample size of the users, wherein the sample size of the users has a value that relates to the additional user actions in the machine learning model. 3. The method of claim 2, wherein the value of the sample size of the users decreases over time. 4. The method of claim 2, further comprising: periodically measuring accuracy of the additional user actions. 5. The method of claim 4, further comprising: cross-validating the machine learning model to arrive at the accuracy of the additional user actions by splitting the set of training data into a first portion and a second portion, using the first portion as the set of training data, and comparing the additional user actions predicted using the first portion to the solicited user actions in the second portion. 6. The method of claim 5, further comprising: determining the value of the sample size of the users according to the accuracy. 7. The method of claim 1, further comprising: combining features extracted from the solicited user actions into a feature vector, wherein the information about the object in the set of training data includes the feature vector. 8. The method of claim 1, further comprising: identifying the solicited user actions in relation to reference actions in an action library. 9. The method of claim 1, further comprising: estimating quality of the solicited user actions for use in the set of training data. 10. The method of claim 8, further comprising: estimating quality of the users for use in the set of training data. 11. A method, comprising: soliciting information from a sample size of users about actions on objects; creating a set of training data comprising the solicited information; and predicting additional information from the set of training data using a machine learning model, wherein the machine learning model replaces solicited information with additional information over time, beginning at least in response to accuracy of the additional information being at or near accuracy of the solicited information and increasing in number as accuracy of the additional information improves relative to accuracy of the solicited information. 12. The method of claim 11, further comprising: extracting feature vectors from the solicited information, wherein the set of training data includes the feature vectors. 13. The method of claim 11, further comprising: identifying, from information in the set of training data, the objects by type and the actions performed on the objects, wherein the additional information relates to the object type and the actions performed on the objects. 14. The method of claim 13, wherein the object types correspond with different types of media objects. 15. The method of claim 11, further comprising: extracting keywords from text documents, wherein the set of training data includes the keywords. 16. A system, comprising: a computing device accessible to data over a network, the computing device comprising a processor and memory with instructions configuring the processor for, building a machine learning model by replacing solicited user actions performed by users on an object with additional user actions in the machine learning model over time, beginning at least in response to accuracy of the additional user actions being at or near accuracy of the solicited user actions and increasing in number as accuracy of the additional user actions improves relative to accuracy of the solicited user actions, the additional user actions predicted from a set of training data that includes the solicited user actions and information about the object. 17. The system of claim 16, wherein the instructions further comprising instructions configuring the processor for, serving web pages configured for receiving the solicited user actions. 18. The system of claim 17, wherein the instructions further comprising instructions configuring the processor for, supplying tasks for evaluation by the user and receiving the solicited user actions in response to the tasks 19. The system of claim 16, further comprising instructions configuring the processor for, indexing the solicited user actions for use in the set of training data according to a type of action. 20. The system of claim 16, further comprising instructions configuring the processor for, indexing the object for use in the set of training data according to a type of object.