American Express Travel Related Services Company, Inc.

Jeremy David Seideman

Matthew Kent Meyer

Gurusamy Ramasamy

Sandeep Bose

Ravi Arasan

Debasish Das

Marimuthu Ponnambalam

Ravi K Kaushik

Ambika Aggarwal

Carmen Patricia Argüello

Kunal Chandrashekhar Joshi

Roopesh R Varier

Mohammad Torkzahrani

Himanshu Prabhakar

Emery Schoenly

Anil Kumar Bashetty

Mazhar Ladji

Deepak Kumar

Zeyu Liu

Mark E Johnson

Swetha Gayam

David Keith Love

Amarnath E Mahendran

Govind Sharma

Paramjit Singh

Anna Korsakova Bain

Thomas J Katavic

Mark T Horton

Nishit Bandi

Jillian Smythe

Nazish Amir-Siddiqi

Xiaoyang Chen

Swatee Singh

Atul R Dalmia

Kavita Gupta

Cong Liu

Yan Zhang

Jennifer Tam

Karin Fourie

George Kassabov

Tami Sue Sargeant

Siva Nidamarthy

Pratiti Shrivastava

Jaime Amanda Spahr

Amartya Roy

Disclosed are various embodiments facilitating a holistic engagement with a user across multiple communication channels of an organization or an enterprise based at least in part on a determined user intent. As users interact with various services associated with the organization through one or more communication channels, interaction data can be captured and stored. In various examples, the interaction data that is stored by the various services can be obtained and organized according to a predefined schema. The organized interaction data can be applied to a trained intent model that outputs a user intent based at least in part on observations of other users with similar histories. The predicted intent can be provided to the different services such that subsequent interactions between the user and the organization can be based at least in part on the intent in a consistent manner, regardless of the communication channel associated with the interaction.

Holistic user engagement across multiple communication channels

At least some embodiments are directed to a system that receives a profile values associated from new user profiles of a computer network or system. A machine learning system determines a set of existing profiles that share at least one common profile value with the new user profile. A second machine learning model determines a set of existing user entitlements associated with the set of existing profiles. The new user profile is processed by a natural language processing engine to determine a set of new user entitlements from the set of existing user entitlements. The system provides the new user with access to electronic resources of the computer network. The system tracks the new user computer network or system activities and updates the new user profile based on the set of new user entitlements and the new user activity on the computer network or system.

Computer-based systems for dynamic persona-based access to computer network resources based on machine learning techniques and methods of use thereof

Systems and methods of the present disclosure may read source data corresponding to a source variable and apply a transformation to the source variable to generate an output variable. Accordingly, one such method comprises receiving a request to access an output variable by applying one or more data transformations to input data; before applying an actual data transformation to the input data, identifying that a match exists with a database of prior data transformations that have been applied to source variables to generate derived variables; identifying that a source variable in the matching one or more data transformations is flagged as sensitive data; and processing the request to access the output variable subject to an access restriction in response to the identification of the source variable that is flagged as sensitive data.

Lineage data for data records

Systems and methods of the present disclosure may read source data corresponding to a source variable and apply a transformation to the source variable to generate an output variable. Accordingly, one such method comprises receiving a request to generate an output variable by applying one or more data transformations to a source variable; before applying an actual data transformation to the source variable, checking a database of prior data transformations that have been applied to source variables to generate existing output variables for a match with the requested one or more data transformations to the source variable; determining that a match exists in the database, wherein the match is associated with an existing output variable; and/or responding to the request by providing the existing output variable associated with the match that exists in the database of prior data transformations.

A system may receive a request for access to a first variable. The request may include a requested action and a variable identifier for the first variable. The request may also have a syntax that is incompatible with first data storage format. The system may parse the request to capture the variable identifier from the request. The system may also look up a location of the first variable in a catalog using the variable identifier. The location of the first variable may include the first data storage format. The system may generate a query to access the first variable. The syntax of the query may be compatible with the first data storage format. The system may then submit the query to access the first data storage format. The query may be configured to complete the requested action.

Querying in big data storage formats

The method includes receiving historical data from a first data source; analyzing the historical data for a desired characteristic; determining a representative value for the desired characteristic of the historical data; determining a first data expectation for the historical data based on the representative value; transmitting the first data expectation to a first data recipient; receiving first incoming data from the first data source; analyzing the desired characteristic of the first incoming data; determining a first incoming data value for the desired characteristic for the first incoming data; comparing the first incoming data value and the representative value; determining a first difference between the first incoming data value and the representative value; and/or comparing the first difference to a difference threshold which indicates whether a difference between an incoming data value and the representative value is significant.

Data management system

Disclosed are various embodiments for tracking the flow of data through a network environment. A monitor can detect that a data transaction event has occurred. Then, the monitor can identify data involved in the data transaction event. Next, a trace identifier can be assigned to the data involved in the data transaction event. Subsequently, a transaction data subset representing a subset of the data involved in the data transaction event that is subject to a common data processing event can be identified. Then, a span identifier can be assigned to the transaction data subset. Next, a correlation identifier can be link to a combination of the span identifier and the trace identifier. Finally, a transaction event record can be written to a distributed ledger, the transaction event record comprising the span identifier and the transaction data subset.

Enhanced tracking of data flows

A system may retrieve a pattern from a pattern database with the pattern identifying a type of sensitive data. The system may also retrieve data identified by a variable from a big data management system. The system may then match the data to the pattern to detect the type of sensitive data in the data. An output may be generated in response to the data matching the pattern. A variable access permission may be retrieved for the variable from a permissions repository, a sensitive data permission may be retrieved for the type of sensitive data from the permissions repository, and the variable access permission may be compared to the sensitive data permission to detect a discrepancy.

Systems and methods for tracking sensitive data in a big data environment

A system may read source data corresponding to a source variable and apply a transformation to the source variable to generate an output variable. The transformation may include logic, and the output variable may be configured for ingestion into a big data storage format. The system may record lineage data of the output variable that comprises the transformation and/or the source variable. The system may also receive a request to generate a requested output variable. The requested output variable may be generated from a second transformation that is the same as the first transformation. The system may thus match the first transformation to the second transformation using the lineage data. In response to matching the first transformation to the second transformation, the system may deny the request. The original output variable may be returned in response to the matching the first transformation to the second transformation.

A system for applying access permissions to read requests may write a file to a storage media. The file may contain data, an embedded flag, and an embedded verification datum of the embedded flag. The embedded flag may indicate an access permissions requisite for file access. The system may also receive a request to access the file from a user and retrieve an access permission for the user. The system may also compare the embedded flag to the access permission for the user to determine the user has permission to access the file. A verification datum may be calculated, and the calculated verification datum may be compared to the embedded verification datum to determine the embedded flag has not changed.

Permissions for writing data to a file

A system may store a plurality of first records comprising a first variable in a first data storage format. The system may also store a plurality of second records comprising a second variable in a second data storage format. The first data storage format may have a different data structure than the second data storage format. The system may then generate a catalog including a first location of the first variable and a second location of the second variable. The first location identifies the first data storage format, which is where the first variable is stored. The second location identifies the second data storage format, which is where the second variable is stored. The system may then receive a request to access at least one of the first variable or the second variable and access the first variable and/or the second variable in response to the request.

Catalog with location of variables for data

A system may receive a request to derive an output variable from a source variable. The request may include proposed logic to derive the output variable from the source variable. The system may then compare the proposed logic to existing logic to determine the proposed logic is new. In response to the proposed logic being new, the system may generate transformation code configured to execute the proposed logic. The system may further schedule the transformation code for execution at a predetermined time, and then execute the transformation code to generate data for the output variable.

Data transformations with metadata

Growing community of inventors

Brooklyn, NY, United States of America

Jeremy David Seideman