Ca, Inc.

Stephen Tyler

Debra Jean Danielson

Carol Piccus

Diane Norris

Karen Sleeth

Naresh Pillarikuppam

Vijay Senapathi

Ronald Doyle

Apoorva Choudhary

Wendell Beckwith

Provided is a process that includes: obtaining a training set of n-grams labeled as offensive; causing a machine learning model to be trained based on the training set of n-grams, wherein the machine learning model, when trained, is configured to classify natural language text as offensive or non-offensive; obtaining input natural language text expressing a computer-generated utterance; classifying after causing training, the computer-generated utterance as offensive or non-offensive using the machine learning model; and causing an output to be provided to a recipient, the output being based on whether the machine learning model classifies the computer-generated utterance as offensive or non-offensive.

Machine learning model for identifying offensive, computer-generated natural-language text or speech

According to another embodiment of the disclosure, a method includes determining a first original identifier. The original identifier uniquely identifies a first original identity. The method also includes sending a request to a cooperative synthetic identity manager to create a first cooperative synthetic identity case for the first original identity. The method further includes receiving, from the cooperative synthetic identity manager, a first case identifier that uniquely identifies the first cooperative synthetic identity case. The method also includes requesting, from a first data entity, first cooperative synthetic identity information for the first original identity. The first cooperative synthetic identity information request comprises the first case identifier and the first original identifier. The method also includes receiving, from the first data entity, the first cooperative synthetic identity information.

System and method for analyzing cooperative synthetic identities

According to one embodiment of the disclosure, a method includes receiving, from a first requestor, a request to create a cooperative synthetic identity case for an original identity. The method includes determining whether a cooperative synthetic identity case has already been created for the original identity. The method also includes generating a case identifier that uniquely identifies the cooperative synthetic identity case. The method further includes associating the case identifier with an expiration period. The method also includes storing the cooperative synthetic identity case, the case identifier, and the expiration period to a memory. The method also includes sending the case identifier to the first requestor.

System and method for managing cooperative synthetic identities for privacy protection through identity obfuscation and synthesis

Provided is a process, including: obtaining a model of an application program interface ('API'); receiving a request to generate a functional-test specification for the API; detecting sets of functionality identified by the model of the API and respective sets of parameters corresponding to the detected sets of functionality by parsing the model of the API; in response to receiving the request, generating the functional-test specification for the API by, for at least some respective detected sets of functionality, specifying respective sets of functional tests based on corresponding sets of parameters.

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Islandia, NY, United States of America

Stephen Tyler