Using classified text or images and deep learning algorithms to identify risk of product defect 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 risks to an enterprise (of which product liability is the prime example here) while such risks are still internal electronic communications. The system involves mining and using existing classifications of data (e.g., from an internal litigation database, or from external sources such as customer complaints, and/or warranty claims) to train one or more deep learning algorithms, and then examining the enterprise's internal electronic communications 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 or others.
Claims16
A method of using classified text and deep learning algorithms to identify product liability risk and provide early warning comprising:
- creating one or more training datasets for textual data corresponding to a product liability risk classification;
- training one or more deep learning algorithms using said one or more training datasets;
- extracting electronic communications related to an enterprise;
- applying said one or more deep learning algorithms to said electronic communications to identify and report said product liability risk;
- determining if said identified product liability risk is a false positive or a true positive;
- re-training said one or more deep learning algorithms if said identified product liability risk is a false positive;
- and
- saving said electronic communications in a true positive database if said identified product liability risk is a true positive.
A method of using classified text and deep learning algorithms to identify product liability risk and provide early warning comprising:
- creating one or more training datasets by mining litigation database for textual data corresponding to a product liability;
- training one or more deep learning algorithms using said one or more training datasets;
- extracting and indexing one or more electronic communications, wherein said electronic communications comprises internal and external communications;
- applying said one or more deep learning algorithms to said one or more electronic communications to identify and report any one of said product liability risk;
- determining if said identified one of said product liability risk is a false positive or a true positive;
- re-training said one or more deep learning algorithms if said identified one of said product liability risk is a false positive;
- and
- saving said electronic communications in a true positive database for evaluation if said identified one of said product liability risk is a true positive, wherein said true positive database provides early warning.
Dependencies are inferred from the claim text.