Abstract: Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

Abstract: Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

Abstract: Aspects of the disclosure relate to using machine learning to modify account privacy settings. A computing platform may identify initial account settings for an individual of a plurality of individuals. Using a settings optimization model, the computing platform may identify account data and third party data for the individual. Using the settings optimization model, the computing platform may identify discrepancies between the initial account settings for the first individual and account settings for a subset of the plurality of individuals having common characteristics with the individual. Based on the discrepancies, the computing platform may identify settings modifications for the individual, and may determine that a modification of the settings modifications applies to a detected interaction of the individual. The computing platform may direct an enterprise data source to perform the modification, which may cause the enterprise data source to modify the initial account settings based on the modification.

Abstract: Systems for dynamically controlling authentication data are presented. In some examples, registration data may be received. The registration data may include user data, contact information, and authentication data which may include deoxyribonucleic acid (DNA) data of a user. In some arrangements, a request to process an event may be received. The request may include event details which may be used to determine or identify an authentication tier of the event. Based on the identified authentication tier, one or more authentication factors for request may be dynamically identified. The request for authentication factors may be transmitted to a user device and authentication response data may be received. The authentication response data may be compared to the authentication data received at registration (or otherwise pre-stored) to determine whether a match exists. If the data matches, the event may be authorized for processing. If not, the event process request may be denied.

Abstract: Aspects of the disclosure relate to a cognitive automation based compliance management system. A computing platform may receive, via a network, an alert notification indicative of a change to a legal framework applicable to an enterprise organization. Subsequently, the computing platform may, in response to the alert notification, detect a task that may be impacted by the change. Then, the computing platform may, upon detecting the task, identify at least one task parameter associated with the task. Then, the computing platform may compare the at least one task parameter with the change to determine whether the task is impacted by the change. Subsequently, the computing platform may, upon a determination that the task is impacted by the change, recommend, via the network, one or more actions associated with the task.

Abstract: Systems for using cognitive automation techniques to generate resource recommendations are provided. In some examples, current resource data may be received. The current resource data may include current staffing data, skills associated with current staff, and the like. In some examples, environmental condition data may also be received. The environmental condition data may include current weather information, predicted weather information, power outage information, and the like. The resource data and environmental data may be analyzed using cognitive automation techniques to identify and/or generate one or more resource recommendations. The resource recommendations may include adjustments to staffing, modification of locations at which processes are performed, and the like. The identified or generated recommendations may be transmitted to one or more computing devices.

Abstract: Aspects of the disclosure relate to a cognitive automation based vendor compliance system. A computing platform may monitor, via an enterprise network, use of a software product hosted by an enterprise computing infrastructure. Subsequently, the computing platform may determine a product usage score, where the product usage score is indicative of a number network devices using the software product. Then, the computing platform may determine, for the software product, a product performance score, where the product performance score is indicative of one or more performance issues associated with the software product. Then, the computing platform may compare, for the software product, at least one of: (1) the product usage score to a product usage threshold, and (2) the product performance score to a product performance threshold. Subsequently, the computing platform may recommend, based on the comparing, one or more actions associated with the software product.

Abstract: Aspects of the disclosure relate to cognitive automation-based engine processing on audio files and streams received from meetings and/or telephone calls. A noise mask can be applied to enhance the audio. Real-time speech analytics separate speech for different speakers into time-stamped streams, which are transcribed and merged into a combined output. The output is parsed by analyzing the combined output for correct syntax, normalized by breaking the parsed data into record groups for efficient processing, validated to ensure that the data satisfies defined formats and input criteria, and enriched to correct for any errors and to augment the audio information. Notifications based on the enriched data may be provided to call or meeting participants.

Abstract: Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

Abstract: Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.