Abstract: Embodiments may provide a document system that receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Theses responsiveness calls may be used to refining the scoring algorithm used by the document system of to generate a desired confidence score for the document system.

Abstract: Embodiments may provide a document system that receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Theses responsiveness calls may be used to refining the scoring algorithm used by the document system of to generate a desired confidence score for the document system.

Abstract: Embodiments may provide a document system that receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Theses responsiveness calls may be used to refining the scoring algorithm used by the document system of to generate a desired confidence score for the document system.

Abstract: Embodiments may provide a document system that receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Theses responsiveness calls may be used to refining the scoring algorithm used by the document system of to generate a desired confidence score for the document system.

Abstract: Embodiments may provide a document system that receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Theses responsiveness calls may be used to refining the scoring algorithm used by the document system of to generate a desired confidence score for the document system.

Abstract: Embodiments may provide a document system that receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Theses responsiveness calls may be used to refining the scoring algorithm used by the document system of to generate a desired confidence score for the document system.

Abstract: The invention provided is a system configured to use a topic-related generative model to build a document map from a plurality of documents in a storage medium and generate a control set from the plurality of documents including at least two stratified document sets. The system then receives a set of control set metrics regarding the control set from a user. The system selects a machine call responsive document from a document map based on a determined predictive responsiveness for that document. The system receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Finally, the system compares the responsiveness of the machine call document to the control set metrics and rebuilds the document map based on the results of the comparison between the machine call document responsiveness and the control set metrics.

Abstract: The invention provided is a system configured to use a topic-related generative model to build a document map from a plurality of documents in a storage medium and generate a control set from the plurality of documents including at least two stratified document sets. The system then receives a set of control set metrics regarding the control set from a user. The system selects a machine call responsive document from a document map based on a determined predictive responsiveness for that document. The system receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Finally, the system compares the responsiveness of the machine call document to the control set metrics and rebuilds the document map based on the results of the comparison between the machine call document responsiveness and the control set metrics.

Abstract: The invention provided is a system configured to use a topic-related generative model to build a document map from a plurality of documents in a storage medium and generate a control set from the plurality of documents including at least two stratified document sets. The system then receives a set of control set metrics regarding the control set from a user. The system selects a machine call responsive document from a document map based on a determined predictive responsiveness for that document. The system receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Finally, the system compares the responsiveness of the machine call document to the control set metrics and rebuilds the document map based on the results of the comparison between the machine call document responsiveness and the control set metrics.

Abstract: The invention provided is a system configured to use a topic-related generative model to build a document map from a plurality of documents in a storage medium and generate a control set from the plurality of documents including at least two stratified document sets. The system then receives a set of control set metrics regarding the control set from a user. The system selects a machine call responsive document from a document map based on a determined predictive responsiveness for that document. The system receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Finally, the system compares the responsiveness of the machine call document to the control set metrics and rebuilds the document map based on the results of the comparison between the machine call document responsiveness and the control set metrics.