Jpmorgan Chase Bank, N.a.

Brett Sanford

Sean Moran

Fanny Silavong

Rob Otter

Antonios Georgiadis

Gaurav Singh

Sanat Saha

Ganesh Chandrasekar

Andy Alexander

Marco Pistoia

Shaohan Hu

Bill Moriarty

Hargun Kalsi

Sae Young Moon

Systems and methods for counteracting data-skewness for locality sensitive hashing via feature selection and pruning are disclosed. In one embodiment, a method for feature selection for counteracting data skewness on locality sensitive hashing (LSH)-based search may include: (1) ingesting, by an ingestion computer program and from a plurality of data sources, data; (2) extracting, by the ingestion computer program, a plurality of features from the ingested data; (3) transforming, by the ingestion computer program, each of the plurality of features into a feature vector; (4) selecting, by the ingestion computer program, a subset of the plurality of features; and (5) for each selected feature vector: computing, by the ingestion computer program, a random hash function for the selected feature; and inserting, by the ingestion computer program, an output of the random hash function into a hash table with the selected feature.

Systems and methods for counteracting data-skewness for locality sensitive hashing via feature selection and pruning

Systems and methods for automated code analysis and tagging are disclosed. In one embodiment, a method for automated code analysis and tagging may include: (1) receiving, by a code annotation computer program executed by a computer processor, a training code snippet from a training codebase; (2) parsing, by the code annotation computer program, the training code snippet into a data structure; (3) quantifying, by the code annotation computer program, the data structure, (4) parsing, by the code annotation computer program, a docstring associated with the training code snippet into a plurality of keywords; (5) quantifying, by the code annotation computer program, the plurality of keywords; and (6) training, by the code annotation computer program, a code annotation model based on a similarity between the quantified data structure and a smoothing parameter for a Dirichlet prior smoothing estimate.

Systems and method for automated code analysis and tagging

Systems and methods for federated secure vocabulary learning. The method may include communicating, by a first client network and a second client network and to a global federation terminal, a first set of encrypted tokens and a second set of encrypted tokens. The method may further include generating, by the global federation terminal, a consolidated vocabulary based on the first set of encrypted tokens and the second set of encrypted tokens. The method may include determining, a first set of weights and a second set of weights for the consolidated vocabulary. The method may include receiving by the global federation terminal the first set of weights and the second set of weights. The method may further include aggregating the first set of weights and the second set of weights. The method may further include distributing, to the first client network and the second client network, an aggregated set of weights.

Systems and methods for federated secure vocabulary learning

A global partitioning-based method for anonymizing a dataset of biometric data may include an anonymization computer program: (1) receiving a value k representing a number of records to hide a biometric datum among, a value t that represents a t-closeness parameter for a t-close distribution, a weight parameter, and a first number of features to retain for determining an attribute of interest; (2) receiving the attribute of interest; (3) calculating a distribution of the attribute of interest in a biometric dataset; (4) splitting the biometric dataset into a plurality of k-sized clusters that satisfy the t-close distribution; (5) anonymizing each biometric datum in the plurality of k-sized clusters using a weighted average of landmarks for the biometric datums in k-sized clusters using the weight parameter; (6) adding each anonymized biometric datum into an anonymized biometric dataset; and (7) persisting the anonymized biometric dataset.

Systems and methods for anonymizing a dataset of biometric data while retaining data utility

Systems and methods for generating a fusion score between electronic documents. The method includes receiving a first electronic document by a document management system. The method further includes extracting a first set of features from the first electronic document including at least one feature type indicating the hierarchical structure of the first electronic document. The method also includes receiving a second electronic document by the document management server. The method further includes extracting a second set of features from the second electronic document including at least one feature type indicating the hierarchical structure of the second electronic document. The method further includes generating a fusion score based on a comparison of the first set of features and the second set of features.

Linear late-fusion semantic structural retrieval

Growing community of inventors

Tunbridge Wells, United Kingdom

Brett Sanford