Using classified text and deep learning algorithms to identify drafting risks in a document and provide early warning
First Claim
1. A method of using classified text and deep learning algorithms to identify ambiguity risks in a draft document intended to create contractual obligations and provide early warning comprising:
- obtaining one or more training datasets for textual data corresponding to court opinions in which a contract is the subject of a dispute and one of the issues is whether the language of a provision is ambiguous;
training one or more deep learning algorithms using said one or more training datasets;
obtaining an internal electronic draft of a contract;
applying said one or more deep learning algorithms to said internal electronic draft contract to identify and report any one of said one or more ambiguous terms or phrases and related data;
determining if said identified one of said one or more ambiguous terms or phrases is a false positive or a true positive;
re-training said one or more deep learning algorithms if said identified one of said one or more ambiguous terms or phrases is a false positive; and
saving said internal electronic draft contract in a true positive database if said identified one of said one or more ambiguous terms or phrases is a true positive.
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Abstract
Deep learning is used to identify a potential risk that a contract will be unenforceable due to a drafting error whereby one or more terms or phrases are ambiguous. The system involves mining and using existing classifications of data (e.g., from a litigation database) to train one or more deep learning algorithms, and then examining the internal electronic drafts of contracts with the trained algorithm, to generate a scored output that will enable enterprise personnel to be alerted to the ambiguity risks and take action in time to prevent the risks from resulting in harm to the enterprise.
27 Citations
17 Claims
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1. A method of using classified text and deep learning algorithms to identify ambiguity risks in a draft document intended to create contractual obligations and provide early warning comprising:
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obtaining one or more training datasets for textual data corresponding to court opinions in which a contract is the subject of a dispute and one of the issues is whether the language of a provision is ambiguous; training one or more deep learning algorithms using said one or more training datasets; obtaining an internal electronic draft of a contract; applying said one or more deep learning algorithms to said internal electronic draft contract to identify and report any one of said one or more ambiguous terms or phrases and related data; determining if said identified one of said one or more ambiguous terms or phrases is a false positive or a true positive; re-training said one or more deep learning algorithms if said identified one of said one or more ambiguous terms or phrases is a false positive; and saving said internal electronic draft contract in a true positive database if said identified one of said one or more ambiguous terms or phrases is a true positive. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method of using classified text and deep learning algorithms to identify ambiguity risks in contractual documents and provide early warning comprising:
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obtaining one or more training datasets for textual data corresponding to court opinions for which ambiguity is a risk classification, wherein said risk classification comprises one or more ambiguous terms or phrases; training one or more deep learning algorithms using said one or more training datasets; obtaining an internal electronic contractual document; applying said one or more deep learning algorithms to said internal electronic contractual document to identify and report any one of said one or more ambiguous terms or phrases; determining if said identified one of said one or more ambiguous terms or phrases is a false positive or a true positive; and re-training said one or more deep learning algorithms if said identified one of said one or more ambiguous terms or phrases is a false positive. - View Dependent Claims (12, 13, 14, 15, 16, 17)
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Specification