American Express Travel Related Services Company, Inc.

Yogaraj Jayaprakasam

Jordan Meyerowitz

Jane E Cook

Srinivas K Kumandan

Daniel Reznik

Abhishek Jain

Vincent Julien David

Hector Flores

Robin Jain

Sachin Jadhav

Deepak Narayanan

Sanjay Madaan

Rahul Shaurya

Michelle Geller

Abhishek Kachhara

Hemant Bhatia

Travis Brown

Ashwin Nagalla

Systems and methods for optimizing user task schedules in a customer relationship management (CRM) platform is disclosed. In one example, a system comprising a computing device and a cache. The computing device is configured to generate child user task schedules and calculate task win probabilities of tasks for the child user task schedules using a machine learning system. The machine learning system is used to determine the plurality of task win probabilities. The computing device is also configured to calculate total task win probabilities for the child user task schedules based on the task win probabilities and is configured to determine an optimized user task schedule by selecting a respective user task schedule having a greatest total task win probability from a subset of the plurality of child user task schedules stored in the distributed cache.

Optimizing user task schedules in a customer relationship management platform

Systems and methods for optimizing user task schedules in a customer relationship management (CRM) platform is disclosed. The system may optimally input tasks into time slots in a user schedule to generate the optimized user task schedule. The system may generate a plurality of user task schedules and calculate a total task win probability for each of the user task schedules. The system may comprise the total task win probabilities and select the user task schedule having the greatest total task win probability. The system may also perform a genetic processing analysis of the user task schedules to further optimize task placement in the user task schedule.

A system, method, and computer readable medium (collectively, the 'system') is disclosed for generating real-time insights from a big data storage platform. The system may process transaction data stored in a big data management system (BDMS) to generate characteristic data associated with a plurality of demographics. The system may also model an account associated with a corporate ID to identify a demographic from the plurality of demographics associated with the corporate ID. The system may further compare the user data associated with the corporate ID to the characteristic data of the demographic associated with the corporate ID to generate a view responsive to a request. The system may also transmit the view in response to the request, for example, to an interface for use by a program administrator.

Insights on a data platform

A system for generating and delivering custom data sets in a big data environment may receive a preselected schema that identifies a plurality of columns from a plurality of data sources for inclusion in an output data file. The system reads data from the data sources to generate a data file containing a big data table. The system monitors the plurality of data sources to detect that the data sources have been ingested into a data storage system. The data file is read and a column is filtered from the data file to generate the output data file in response to the preselected schema excluding the column. The output data file is transferred to a client device.

Systems and methods for a self-services data file configuration with various data sources

Systems, methods, and articles of manufacture for evaluating colliding data records are provided. The system may ingest one or more data inputs from one or more data sources. The system may parse the data inputs and determine whether the data is preexisting in the system. In response to the data input and stored data at least partially conflicting (e.g., colliding), the system may generate a candidate dataset based on the data input and the stored data. The candidate dataset may comprise two or more data candidates comprising permutations of the data values in the data input and the stored data. The system may evaluate each data candidate, score each date candidate based on the evaluation, and determine the data candidate having the greatest score.

Evaluating colliding data records

A data enrichment environment using blockchain is disclosed. A client may interact with the data enrichment environment to generate an enrichment smart contract and deploy the enrichment smart contract to the blockchain. The enrichment smart contract may comprise data indicating the enrichment inquiry. One or more solvers may interact with the data enrichment environment to submit proposed enrichment solutions based on the enrichment inquiry. One or more observers may interact with the data enrichment environment to review and vote on the proposed enrichment solutions. The data enrichment environment may evaluate the proposed enrichment solutions based on contract conditions.

Data enrichment environment using blockchain

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Phoenix, AZ, United States of America

Yogaraj Jayaprakasam