American Express Travel Related Services Company, Inc.

Vinod Yadav

Sandeep Bose

Madhu Sudhan Reddy Gudur

Ravneet Ghuman

Anand Bhushan

Mario Fragoso

Sanjay S Gwalani

Harish R Naik

Satish Thenthiruperai Narayanan

Curtis T Merryweather

Ravi Varma

Madhu Sudan Reddy Gudur

Suraj Madnani

Ravi Batra

Amber G Gupta

Karan Anil Kumar

Ajay Kumar Punia

Hui-Ping W Chao

Shashank Kapoor

Rahul Panwar

Nikhil Jain

Nivedita Singh

Systems and methods for identifying data of interest are disclosed. The system may retrieve unstructured data from an internet data source via an alert system or RSS feed. The system may input the unstructured data into various models and scoring systems to determine whether the data is of interest. The models and scoring systems may be executed in order or in parallel. For example, the system may input the unstructured data into a Naïve Bayes machine learning model, a long short-term memory (LSTM) machine learning model, a named entity recognition (NER) model, a semantic role labeling (SRL) model, a sentiment scoring algorithm, and/or a gradient boosted regression tree (GBRT) machine learning model. Based on determining that the unstructured data is of interest, a data alert may be generated and transmitted for manual review or as part of an automated decisioning process.

Identifying data of interest using machine learning

Disclosed are various embodiments for using decision trees for machine-learning when data is missing from a data set. A first attribute for splitting a plurality of records is identified. Then, the plurality of records are split into a first subset of records and a second subset of records. The first subset of records can include each of the plurality of records where a value is present for the first attribute and the second subset of records comprising each of the plurality of records where the value is absent for the first attribute. Finally, a node can be added to a decision tree that reflects the split of the plurality of records into the first subset of records and the second subset of records.

Decision tree learning with missing data

Disclosed are various embodiments for generating relevant notifications of content generated by third party data sources. In some embodiments, a system comprises a computing device and machine readable instructions. The computing device includes a processor and a memory. The machine-readable instructions can be stored in the memory that, when executed by the processor, cause the computing device to receive content from a third party data source based at least in part on a keyword topic. An embedding for the content is generated. The system can classify a portion of the content as associated with the keyword topic. Organizations can be identified from the portions of the content. The system can generate a list from the organizations identified in the content and transmit a notification to a client device regarding the content.

Relevant notifications of third party content

Systems and methods for identifying data of interest are disclosed. The system may retrieve unstructured data from an internet data source via an alert system or RSS feed. The system may input the unstructured data into various models and scoring systems to determine whether the data is of interest. The models and scoring systems may be executed in order or in parallel. For example, the system may input the unstructured data into a Na&#xef;ve Bayes machine learning model, a long short-term memory (LSTM) machine learning model, a named entity recognition (NER) model, a semantic role labeling (SRL) model, a sentiment scoring algorithm, and/or a gradient boosted regression tree (GBRT) machine learning model. Based on determining that the unstructured data is of interest, a data alert may be generated and transmitted for manual review or as part of an automated decisioning process.

A system and method may allow for improved accuracy for address matching. The system may receive an address input and preprocess the address input. The address input may be standardized to create a standardized address input. The standardized address input may be compared to a stored address. The system may calculate a first address matching score based on the comparison. The system may reinvestigate the standardization of the address input and calculate a second address matching score based on a second comparison. The system may compare the first address matching score to the second address matching score to improve accuracy in address matching.

Systems and methods for address matching

A distributed file system may store a plurality of entity attributes. A node consolidating system may identify similarities between entity attributes for different entities. The node consolidating system may generate an entity graph which displays relationships and similarities between entities in a graphical user interface.

Network node consolidation

Systems and methods of improving the operation of a transaction network and transaction network devices is disclosed. A lightning KNN host may comprise various modules and engines as discussed herein wherein lookalike records may be identified whereby the speed of the lightning KNN network may be enhanced and the accuracy and precision of results improved whereby the transaction network more properly functions according to approved parameters.

Retaining a set of accountholders within a ceiling number radius

A system and method may allow for improved accuracy for name matching. The system may receive a name input and preprocess the name input. The name input may be tokenized to create a name token. The name token may be compared to a stored name. The system may calculate a first name matching score based on the comparison. The system may permute the name token to form a second level permutation name, compare the second level permutation name with the stored name, and calculate a second name matching score based on the comparison. The first name matching score may be compared with the second name matching score to improve accuracy in name matching.

Systems and methods for name matching

Known nearest neighbor (KNN)

System and method for data analytics

Growing community of inventors

Phoenix, AZ, United States of America

Vinod Yadav