Abstract: Deep learning is used to identify specific risks to an enterprise of a pending litigation and identify documents of interest for the litigation. 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 electronically stored information with the trained algorithm, to generate a scored output that will enable enterprise personnel to review risks to the enterprise, e.g. to enable enterprise personnel to assess the nature and extent of the potential damage from the litigation, and to identify relevant documents that would be saved to prevent spoliation.

Abstract: Deep learning is used to identify specific, potential risks to an enterprise while such risks are still internal electronic communications. The combination of Deep Learning and blockchain technologies is a system for overcoming the problem of “small training sets” for highly adverse situations. Each enterprise's data is secure; is not revealed to any other enterprise and yet is being aggregated using blockchain technology into a training set that is provably viable for building a Deep Learning model which is specific to a given adverse situation. When deployed, the Deep Learning model may provide an early warning alert to an enterprise's corporate counsel (or leaders) of a potential adverse situation the enterprise would like to know about in time to conduct an internal investigation in order to prevent or avoid the risk.

Abstract: Deep learning is used to identify specific, potential risks to an enterprise (of which litigation is the prime example) while such risks are still internal electronic communications. 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 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.

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.

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.

Abstract: Deep learning is used to identify specific, potential financial advantage for an enterprise that are hidden in internal electronic documents. The system involves mining and using existing classifications of data (e.g., from previously sorted documents) to train one or more deep learning algorithms, and then examining internal electronic documents with the trained algorithm, to generate a scored output that will enable enterprise personnel to evaluate the identified documents for a potential financial advantage to the enterprise.

Abstract: Deep learning is used to identify specific, potential risks of missed diagnosis for a patient and reporting the risk to healthcare provider. The system involves mining and using existing electronic health records for specific medical diagnosis to train one or more deep learning algorithms, and then examining the internal electronic health record of the patient with the trained algorithm, to generate a scored output that will enable a healthcare provider to be alerted to potential risks of a missed diagnosis.

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.

Abstract: Deep learning is used to identify specific, potential risks to an enterprise (of which litigation is the prime example) while such risks are still internal electronic communications. 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 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.

Abstract: A system for detection of potential legal liability is presented. The system uses factual information that has triggered liability based on any number of legal theories, and compares the words expressing those facts to customer and employee communications in order to identify potential liability to an enterprise by reviewing of the enterprise's emails. The system generates seeding information based on the factual information and words expressing certain sentiments, and provides the seeding information to a document fracturing engine which scans the email archives and identifies emails with words that potentially give rise to a liability risk. The identified emails may then be reviewed by authorized personnel so that appropriate proactive and/or corrective action may be taken before the legal liability occurs.