Capital One Services, LLC

Louis Buell

Mohamed Seck

Joshua S Edwards

Michael Bakr Mossoba

Lin Ni Lisa Cheng

Cruz Vargas

Phoebe Atkins

Max Miracolo

Alexander Lin

Brian McClanahan

Kadhiresan Kanniyappan

Donald J Gennetten

Maureen Jules-Perez

Ebrima N Ceesay

Steven Coulbourne

Nour Omar

Michael Kirby

Bret Sebastian

In some implementations, a device may determine a session-specific profile for a user that is to begin a session for a computing environment. The device may determine a computing configuration, for the computing environment, that is personalized for the user based on the session-specific profile for the user and based on historical behavior of the user. The computing configuration may include at least one of a hardware configuration or a software configuration for the computing environment. The device may cause configuration of the computing environment according to the computing configuration.

Dynamic provisioning of a computing environment

Disclosed embodiments may include a system for optimizing data for a serverless file system. The system may receive input data and determine whether the input data is appropriate for optimization. If so, the system may optimize the input data to reduce a size to create optimized data, group the optimized data, apply metadata, versioning, tags, enterprise governance and compliance requirements to the optimized data and store the optimized data in a data repository. In response to receiving an input to upload the optimized data to the serverless file system and determining, from the metadata, that the optimized data is not stored in the serverless file system, the system may upload the optimized data to the serverless file system via an application programming interface. The system may update metadata of the optimized data to indicate a new location at which the optimized data is stored on the serverless file system.

Systems and methods for optimizing data for a serverless file system

A system for recordation and retrieval of incident resolution action information is provided. A recordation system processor is provided with a recordation application configured to receive a response action initiation instruction identifying an action taken to resolve an incident on a target system, The processor determines one or more responsible responders each having a responder data processor that displays a responder dashboard. The processor transmits a recordation instruction to the responder data processor of each responsible responder. The recordation instruction includes an instruction to begin recording responder dashboard information. The recordation application is also configured to receive a response action termination instruction and to transmit a recordation termination instruction to the responder data processor of each responsible responder. A data storage unit is configured for receiving, from the responder data processors, real-time responder dashboard information recordings.

Systems and methods for recording major incident response information

An automated system is provided for facilitating resolution of an incident. An incident information data processor is configured to receive incident information and an action estimation data processor is configured to receive critical action information that includes a description of an action and an action initiation time. The action information data processor obtains previous action information on actions taken to resolve previous incidents and determines an estimated action outcome and an estimated critical action time interval for resolution of the critical action using the critical action information and the previous action information. An incident estimation data processor is configured to determine an overall estimated time interval for incident resolution and an estimated incident resolution time. An incident display data processor is configured to transmit, to a user data processing system for display to a user, at least the estimated incident resolution time.

Systems and method for informing incident resolution decision making

An automated method is provided for mitigating data service system performance degradation due to excess user traffic. A traffic assessment and management server (TAMS) retrieves historical time-varying environment information and transaction data for a data service processing system (DSPS). The TAMS receives a set of expected environment conditions for a future time interval and determines, using the time-varying environment and transaction data, estimated traffic information for the DSPS for the future time interval. The estimated traffic information includes an estimated usage level for at least one system component. The TAMS establishes a simulated system configuration for the DSPS and simulates DSPS operation using the estimated traffic information. The TAMS then determines from the simulation, an estimated likelihood of failure for the at least one system component.

System and method for reducing system performance degradation due to excess traffic

An automated system is provided for facilitating improvements to an incident resolution system. The system includes a scenario construction processor configured for establishing a system incident scenario and a machine learning model constructed using incident response information for each of a plurality of incidents. A simulation processor is configured to use the machine learning model to run simulations of the system incident scenario. Each simulation includes at least one variation of a selected critical action characteristic critical actions included in the scenario and each simulation produces a simulated incident resolution parameter. An improvement evaluation processor determines from the simulations, one or more of the at least one variation that results in a simulated incident resolution parameter meeting predetermined improvement criteria.

Systems and methods for optimizing incident resolution

An automated system is provided for facilitating resolution of an incident on a digital processing system. The automated system has a data storage unit with information on previous incidents, a user interface and a resolution facilitation server. The server receives incident information from a monitoring system, including status information for at least one operating parameter of the digital processing system. The server is configured to apply a machine learning model to determine a first recommended action using the incident information and the previous incident information, present the first recommended action to the user, and receive a recommendation response. The server is also configured to establish a recommendation score based at least in part on the recommendation response and to update the machine learning model using the incident information, the first action recommendation, and/or the first recommendation score.

Incident resolution system

In some implementations, a credit decision platform may receive a credit request from an applicant and obtain domestic historical data associated with the applicant from a credit bureau device. The credit decision platform may obtain access to an email account associated with the applicant based on determining that the domestic historical data associated with the applicant is insufficient to process the credit request. The credit decision platform may identify, using one or more machine learning models, a set of email messages included in the email account that are relevant to the credit request and may analyze content included in the set of email messages to generate non-domestic historical data associated with the applicant. The credit decision platform may generate a decision on the credit request based on an estimated creditworthiness of the applicant, which may be determined based on the non-domestic historical data.

Using email history to estimate creditworthiness for applicants having insufficient credit history

A system, process, and computer-readable medium for configuring agents for monitoring deployed applications is described. A system, process, and computer-readable medium for configuring monitoring user interfaces, e.g., monitoring dashboards, that use information made available from the agents is also described. Through using application data available during creation of the agents, the agents may be configured using the user interface as modified by selections and displaying subsequent choices from the received application data. Using knowledge of the generated agents, monitoring dashboards may be generated via developers interacting with a user interface providing a list of available metrics accessible by the generated agents. Using the one or more user interfaces, developers may generate agents and/or monitoring dashboards with greater efficiency.

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Chevy Chase, MD, United States of America

Louis Buell