Abstract: Methods, systems and computer-program products are directed to a Privacy Engine for evaluating initial electronic documents to identify document content categories for portions of content within the electronic documents, with respect to extracted document structures and document positions, that may include privacy information for possible redaction via visual modification. The Privacy Engine builds a content profile based on detecting information at respective portions of electronic document content that indicate one or more pre-defined categories and/or sub-categories. For each respective portion of electronic document content, the Privacy Engine applies a machine learning model that corresponds with the indicated category (or categories and sub-categories) to determine a probability value of whether the respective portion of content includes data considered likely to be privacy information.

Abstract: Disclosed are systems and methods providing for automation of enterprise and other processes. The systems and methods involve receiving historical process data, applying process mining techniques and generating process models. The process models can be used to identify automation candidates. One or more automation tools designed and configured for the identified automation candidates can be deployed to automate or to increase the efficiency of the process. In one embodiment, automation tools include artificial intelligence networks, which can label a set of input data according to determined or preconfigured domain-specific labels. An aggregator module can combine the similarly labeled data as part of automating a process or to increase the efficiency of a process.

Abstract: Methods, systems and computer-program products are directed to a Privacy Engine for evaluating initial electronic documents to identify document content categories for portions of content within the electronic documents, with respect to extracted document structures and document positions, that may include privacy information for possible redaction via visual modification. The Privacy Engine builds a content profile based on detecting information at respective portions of electronic document content that indicate one or more pre-defined categories and/or sub-categories. For each respective portion of electronic document content, the Privacy Engine applies a machine learning model that corresponds with the indicated category (or categories and sub-categories) to determine a probability value of whether the respective portion of content includes data considered likely to be privacy information.

Abstract: Disclosed are systems and methods providing for automation of enterprise and other processes. The systems and methods involve receiving historical process data, applying process mining techniques and generating process models. The process models can be used to identify automation candidates. One or more automation tools designed and configured for the identified automation candidates can be deployed to automate or to increase the efficiency of the process. In one embodiment, automation tools include artificial intelligence networks, which can label a set of input data according to determined or preconfigured domain-specific labels. An aggregator module can combine the similarly labeled data as part of automating a process or to increase the efficiency of a process.

Abstract: Methods, systems and computer-program products are directed to a Privacy Engine for evaluating initial electronic documents to identify document content categories for portions of content within the electronic documents, with respect to extracted document structures and document positions, that may include privacy information for possible redaction via visual modification. The Privacy Engine builds a content profile based on detecting information at respective portions of electronic document content that indicate one or more pre-defined categories and/or sub-categories. For each respective portion of electronic document content, the Privacy Engine applies a machine learning model that corresponds with the indicated category (or categories and sub-categories) to determine a probability value of whether the respective portion of content includes data considered likely to be privacy information.