Bank of America Corporation

Other

Patrick Nicholas Lawrence

John Christopher Checco

Joseph Benjamin Castinado

Suki Ramasamy

Jisoo Lee

William August Stahlhut

Daniel Joseph Serna

Marcus Raphael Matos

Brad Romano

Lekha Ananthakrishnan

Christopher Lee Danielson

Pankaj Panging

Victoria Lynn Dravneek

Hitesh Shah

Jesse Strickland Newsom, Iii

Manu Jacob Kurian

Paul Roscoe

Saritha Vrittamani

Kenneth Loafman

Tulasi Bhavani Nekkanti

Floyd Franklin Rock, Jr

Sunil Bhashetty

Babar Mahmood Bhatti

Nizam Sayeed

Rajeev Kolappapillai

A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to input the user activity information into a first machine learning model that is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a second machine learning model that is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Threat detection and prevention for information systems

A device that is configured to receive user activity information from a network device. The user activity information includes information about user interactions with the network device for a plurality of users. The device is further configured to input the user activity information into a machine learning model. The machine learning model is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The set of bad actor candidates identifies one or more users from among the plurality of users. The device is further configured to receive the set of bad actor candidates from the machine learning model and to output the set of bad actor candidates.

Detecting bad actors within information systems

A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to receive a set of bad actor candidates that identifies one or more users from among the plurality of users. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a machine learning model. The machine learning model is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Threat detection within information systems

Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Automated event processing computing platform for handling and enriching blockchain data

A system that includes a first edge node and a second edge node in signal communication with one or more interior nodes. The first edge node is configured to receive a correlithm object from a first device outside of the network, to identify an input correlithm object from the first node table with the shortest distance, to fetch a corresponding correlithm object from the first node table, and to send the correlithm object to the one or more interior nodes. The second edge node is configured to receive a correlithm object from the one or more interior nodes, to identify an input correlithm object from a second node table with the shortest distance, to fetch a corresponding correlithm object from the second node table, and to send the correlithm object to a second device outside of the network.

Computer architecture for communications in a cloud-based correlithm object processing system

A system includes a memory and a node. The memory stores first and second log string correlithm objects. The node receives first and second real-world numerical values, and identifies a first sub-string correlithm object from the first log string correlithm object that corresponds to the first real-world numerical value. The node aligns the first and second log string correlithm objects such that the first sub-string correlithm object aligns with a sub-string correlithm object from the second log string correlithm object representing the logarithmic value of one. The node identifies a second sub-string correlithm object from the second log string correlithm object that corresponds to the second real-world numerical value, and determines which sub-string correlithm object from the first log string correlithm object aligns with the second sub-string correlithm object from the second log string correlithm object. The node outputs the determined sub-string correlithm object.

Computer architecture for performing multiplication using correlithm objects in a correlithm object processing system

A system for classifying a data item to communicate to authorized users extracts features from the data item, where the features comprise a responsibility feature and a sensitivity feature. The responsibility feature indicates a job responsibility associated with the data item. The sensitivity feature indicates a sensitivity level of the data item. The system determines, based on the responsibility feature, that the data item belongs to a particular responsibility class. The system determines, based on the sensitivity feature, that the data item belongs to a particular sensitivity class. The system determines whether a user to whom the data item is directed belongs to the particular responsibility class and sensitivity class to which the data item belongs. The system sends the data item to the user, if is it determined that the user belongs to the particular responsibility class and sensitivity class to which the data item belongs.

System and method for routing data to authorized users based on security classification of data

A system includes a memory and a node. The memory stores first and second log string correlithm objects. The node receives first and second real-world numerical values, and identifies a first sub-string correlithm object from the first log string correlithm object representing the first real-world numerical value and a second sub-string correlithm object from the second log string correlithm object representing the second real-world numerical value. The node aligns the first and second log string correlithm objects such that the first sub-string correlithm object aligns with the second sub-string correlithm object. The node identifies a sub-string correlithm object from the second log string correlithm object representing the logarithmic value of one. The node determines which sub-string correlithm object from the first log string correlithm object aligns with the identified sub-string correlithm object from the second log string correlithm object. The node outputs the determined sub-string correlithm object.

Computer architecture for performing division using correlithm objects in a correlithm object processing system

A device receives a document that includes a request and supporting data associated with the request. The device determines that the received request is a first request type. A first set of supporting data is determined that is needed to determine a verification status for requests of the first request type. Based on a comparison of the received supporting data to the first set of supporting data, the device determines that the supporting data includes the data needed to determine the verification status for requests of the first request type. The device then extracts document data from the received document. The extracted document data is compared to the supporting data. Based on this comparison, a verification status is determined for the request.

Document verification system

A device that includes a model training engine implemented by a processor. The model training engine is configured to select a first sub-string correlithm object and a second sub-string correlithm object from a set of sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to compare the Hamming distance to a bit difference threshold value. The model training engine is further configured to determine that the Hamming distance is less than the bit difference threshold value and to compute an average of the first sub-string correlithm object and the second sub-string correlithm object in the n-dimensional space in response to the determination. The model training engine is further configured to train the machine learning model to define the average as a centroid for the first cluster.

Computer architecture for identifying centroids using machine learning in a correlithm object processing system

A device comprising a cluster engine implemented by a processor. The cluster engine is configured to obtain a reference correlithm object and compute a set of Anti-Hamming distances between the reference correlithm object and the set of correlithm objects. The cluster engine is further configured to identify a subset of correlithm objects from the set of correlithm objects that are associated with an Anti-Hamming distance that is greater than a first bit threshold value. The cluster engine is further configured to compute a set of Hamming distances between the reference correlithm object and the subset of correlithm objects and to identify correlithm objects associated with a Hamming distance that exceeds a second bit threshold value. The cluster engine is further configured to remove the identified correlithm objects that are associated with a Hamming distance that exceeds the second bit threshold value and generate the cluster.

Computer architecture for generating hierarchical clusters in a correlithm object processing system

A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to generate a set of gradients by dividing separation distances by an average separation distance and to compare each gradient to a gradient threshold value. The model training engine is further configured to identify a boundary in response to determining a gradient exceeds the gradient threshold value, to determine a number of identified boundaries, and to determine a number of clusters based on the number of identified boundaries. The model training engine is further configured to train the machine learning model to associate the determined number of clusters with the feature vector.

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Plano, TX, United States of America

Patrick Nicholas Lawrence