Using classified text and deep learning algorithms to identify entertainment risk and provide early warning
AssigneeIntraspexion Inc.
InventorNelson E. Brestoff
Life of this patent
Estimating the term…
Abstract
Deep learning is used to identify specific, potential entertainment risks to an enterprise while such risks before the enterprise commits large sums of money to a project. The system involves mining and using existing classifications of data (e.g., from a database of previously successful book and film franchises) to train one or more deep learning algorithms, and then examining a proposed entertainment document with the trained algorithm, to generate a scored output that will enable enterprise personnel to be alerted to risks and take action in time to prevent the risks from resulting in harm to the enterprise.
Claims19
A method of using classified text and deep learning algorithms to identify entertainment risk and provide objective assessment comprising:
- obtaining one or more training datasets for textual data corresponding to one or more entertainment classifications, wherein said entertainment classifications comprise one or more genres of interest;
- training one or more deep learning algorithms using said one or more training datasets;
- obtaining one or more proposed entertainment documents as test data;
- applying said one or more deep learning algorithms to said one or more entertainment documents to determine and report how accurately said one or more entertainment documents is representative of said one or more genres of interest;
- enabling a user with a graphical user interface to determine if said determination of representativeness is a false positive or a true positive and to what degree;
- and
- enabling said user to re-train said one or more deep learning algorithms if said determination of representativeness is a false positive or a true positive.
A method of using classified text and deep learning algorithms to identify entertainment risk and provide objective assessment comprising:
- obtaining one or more training datasets for textual data corresponding to one or more entertainment classifications, wherein said entertainment classifications comprises one or more genres of interest;
- training one or more deep learning algorithms using said one or more training datasets;
- obtaining a proposed entertainment document, wherein said entertainment document comprises a book or a movie script;
- applying said one or more deep learning algorithms to said entertainment document to generate success scores for each of said one or more genres of interest;
- and
- reporting said success scores for each of said one or more genres of interest.
A method of using classified text and deep learning algorithms to identify entertainment risk and provide objective assessment comprising:
- obtaining one or more training datasets for textual data corresponding to one or more entertainment classifications, wherein said entertainment classifications comprise one or more genres of interest;
- training one or more deep learning algorithms using said one or more training datasets;
- obtaining a proposed entertainment document;
- applying said one or more deep learning algorithms to said entertainment document to generate prediction if said entertainment document is representative of said one or more genres of interest;
- determining if said prediction for one of said one or more genres of interest is a false positive or a true positive;
- and
- re-training said one or more deep learning algorithms if said prediction for said one or more genres of interest is a false positive.
Dependencies are inferred from the claim text.