Task-agnostic Integration of Human and Machine Intelligence
First Claim
Patent Images
1. A computer-implemented method comprising:
- receiving at least one user action from a user input device, the at least one user action including a plurality of actions performed on an object;
determining a type of object that includes the object on which the at least one user action was performed;
determining a feature vector of the at least one user action and the object type on which the at least one user action was performed;
using at least one user action and the feature vector to create a set of training data, the training data used to predict future user actions for objects of the type and to determine an accuracy of the predicted future user actions; and
selecting a sample size of users from which to receive additional user actions for the object, the sample size selected responsive to the determined accuracy of the predicted future user actions.
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Abstract
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.
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Citations
12 Claims
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1. A computer-implemented method comprising:
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receiving at least one user action from a user input device, the at least one user action including a plurality of actions performed on an object; determining a type of object that includes the object on which the at least one user action was performed; determining a feature vector of the at least one user action and the object type on which the at least one user action was performed; using at least one user action and the feature vector to create a set of training data, the training data used to predict future user actions for objects of the type and to determine an accuracy of the predicted future user actions; and selecting a sample size of users from which to receive additional user actions for the object, the sample size selected responsive to the determined accuracy of the predicted future user actions. - View Dependent Claims (2, 3, 4)
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5. A non-transitory computer-readable medium that includes instructions that, when loaded into memory, cause a processor to perform a method, the method comprising:
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receiving at least one user action from a user input device, the at least one user action including a plurality of actions performed on an object; determining a type of object that includes the object on which the at least one user action was performed; determining a feature vector of the at least one user action and the object type on which the at least one user action was performed; using at least one user action and the feature vector to create a set of training data, the training data used to predict future user actions for objects of the type and to determine an accuracy of the predicted future user actions; and selecting a sample size of users from which to receive additional user actions for the object, the sample size selected responsive to the determined accuracy of the predicted future user actions. - View Dependent Claims (6, 7, 8)
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9. A computer program product for a machine learning system, wherein the computer program product is stored on a computer-readable medium that includes instructions that, when loaded into memory, cause a processor to perform a method, the method comprising:
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receiving at least one user action from a user input device, the at least one user action including a plurality of actions performed on an object; determining a type of object that includes the object on which the at least one user action was performed; determining a feature vector of the at least one user action and the object type on which the at least one user action was performed; using at least one user action and the feature vector to create a set of training data, the training data used to predict future user actions for objects of the type and to determine an accuracy of the predicted future user actions; and selecting a sample size of users from which to receive additional user actions for the object, the sample size selected responsive to the determined accuracy of the predicted future user actions. - View Dependent Claims (10, 11, 12)
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Specification