Wells Fargo Bank, N.a.

Rameshchandra Bhaskar Ketharaju

Ramanathan Ramanathan

Chris Theodore Kalaboukis

Joon Maeng

Abhijit Rao

Andrew J Garner, Iv

Andres J Saenz

Shanmukeswara Rao Donkada

Naveen Gururaja Yeri

Abhijit Shetti

Ashutosh Verma

Saipavan K Cherala

Shuvam Sengupta

Ajay Kumar Rentala

Vamsi Krishna Geda

Rama Rao Yadlapalli

Sumanth Kumar Charugundla

Nikolai Stroke

Uma Meyyappan

Darius A Miranda

Laura Marie Fontana

Duc M Trinh

Rajiv Ranjan

Sudheendranath R Bhatt

Mabel Oza

Todd Biggs

Ananth Kendapadi

Chandramouli Subramanian

Prasanna Kumar Ammiraju

Joel Eric Boeder

Makarand Shivadevuni

Parul Ghosh

Pradeep R Kumar

Rahul N Jain

Chandrasekaran Sivaraman

Sarath Chava

Mayuri Chaubey

Vinayak Sodar

Manak Suri

Sitara Kumbale

Yang Yang

Vinothkumar Venkataraman

Paul Davis

Matt Hord

Ramya Balasubramanian

Madhu V Vempati

Mohammed Arif

Hemakumar Madabathula

Sajeev Philip

Aditya Kishore Jonnalagadda

Jayasree Iyer

Mathewbabu Edappilly Antony

Amit Tripathi

Ramakrishna Bejjanki

Arun Raj

Tyler Case

Mushnuri Veera Venkata Kiran Kumar

Sai Krishna Madhavi Chitta

Damaraju P Vittal

Ravi Babu Bandla

Hem Shankar Karlapalem

Debashis Ghosh

Daniel S Sumner

Suresh Parpatakam Reddy

Himanshu Baral

Manish Pandey

Dinakar Channakal Krishnappa

Santosh Nichani

Srinivas Vengala

Sri Rajesh Rayudu

Srivathsan Sridharan

Gothandapani Krishnan

Niraj Kumar

Rajesh Padincharekkara Mannachery

Madeshwaran Mohanraj

Jayakrishna Nair

Kalyan Chandra Kothamasu

Parthasarathy Venkatesan

Sridhara Rao Sidhu

Swati Bose

Arshad A Shaik

Gail Elizabeth Stuart

Dibyendu Chatterjee

Kshitindra Kumar Jain

Poornima Mahabaleshwarappa

Ravindar Rao Perala

Dinesh Sangtani

Mohan Gopal Kanala

Prashanth Kumar Merugu

Naveen Saichand Palle

Vasudev Tumkur Umapathi

Ramesh Chinnasamy

Prabal Nandi

Sathya K Balakrishnan

Madhu Gumballi

Dennis Emmanuel Montenegro

Justin Christopher Blackburn

Pankaj Parekh

Suhas Dattatreya Sankolli

Govinda Rajulu Nelluri

Systems, apparatuses, methods, and computer program products are disclosed for device connectivity-based authentication. An example method includes receiving, from a first device, an authorization request associated with a first action. The example method also includes receiving a current connectivity dataset from the first device which indicates one or more devices detected by the first device to be in communication with the first device. The example method also includes comparing the current connectivity dataset with a reference connectivity dataset. The example method also includes determining, based on the comparison, whether the current connectivity dataset and the reference connectivity dataset satisfy a predefined similarity threshold. The example method also includes authorizing, in an instance in which the current connectivity dataset and the reference connectivity dataset satisfy the predefined similarity threshold, the first device to perform the first action.

Systems and methods for device connectivity-based authentication

Systems, apparatuses, methods, and computer program products are disclosed for simultaneous multifactor authentication. An example method includes simultaneously receiving, by communications hardware and in response to a first authentication request, a plurality of authentication factors associated with an individual via a plurality of channels. The example method further includes performing, by an authentication engine, a verification operation that evaluates the plurality of authentication factors in parallel. The example method further includes authorizing, by the authentication engine, the one or more actions based on a successful outcome of the verification operation.

Systems and methods for simultaneous multifactor authentication

Systems, apparatuses, methods, and computer program products are disclosed for automated model development. An example method includes parsing, by configuration circuitry, a configuration file, and generating, by an execution engine and based on the parsed configuration file, model code for training and testing a machine learning model. The example method further includes generating, by the execution engine, a machine learning pipeline, wherein the machine learning pipeline comprises the model code and a data processing engine, and instantiating, by a monitoring driver, a monitoring engine to monitor the machine learning pipeline. The example method further includes causing execution, by the execution engine, of the machine learning pipeline, and during execution of the machine learning pipeline, generating model performance data by the monitoring engine. The example method further includes receiving, by from the execution engine, model output data, wherein the model output data comprises a trained model and model performance data.

Systems and methods for universal machine learning pipeline execution

Systems, apparatuses, methods, and computer program products are disclosed for generation of a user biosignature. An example method includes extracting user data from a data environment, wherein the user data is associated with a user. The example method further includes analyzing the user data, wherein the user data comprises one or more data types, wherein the one or more data types comprise at least metadata and biometric data. The example method further includes determining a character classification associated with each of the one or more data types based on the analyzed user data, wherein the character classification comprises a primary character classification and a secondary character classification. The example method further includes generating a user biosignature based on the determined character classification, and outputting the generated user biosignature.

Systems and methods for generating a blended user biosignature using multimodal AI

Systems, apparatuses, methods, and computer program products are disclosed for providing interoperability between private and public blockchains. An example method for providing interoperability between private and public blockchains includes, by a key exchange controller (KEC): receiving access key generation instructions; generating, based on the key generation instructions, an access key comprising access credentials; transmitting the access key to an access key target on a public blockchain network; receiving, from the access key target, a access request including the access credentials and information indicating a private blockchain on a private blockchain network; and providing, in response to receiving the access request, access for the access key target to access the private blockchain of the private blockchain network.

Systems and methods for data lineage authentication

Systems, apparatuses, methods, and computer program products are disclosed for regulating data communication. An example method includes receiving, by transaction circuitry, a communication request and identifying, by data collection engine and based on the communication request, a set of security parameters. The example method further includes determining, by security analysis engine and based on the set of security parameters, a security score associated with the communication request and in an instance in which the security sore fails to satisfy a security requirement, determining, by the security analysis engine, one or more security actions based on the security score and performing, by the transaction circuitry, the one or more security actions. The example method further includes routing, by data regulation engine and based on a configuration table, a transaction associated with the communication request to a compliant communication channel.

Systems and methods for regulating data communication

The innovation discloses systems, methods and computer program products that reduce complexity and associated measures that are taken to ensure trust in secured transactions as necessitated by requirements present only in an ecommerce environment that do not have a parallel with traditional bricks and mortar commercial transactions. The innovation enables a multitude of mobile wallet applications to engage and obtain services from ecommerce applications wherein backend processing by a mobile wallet server obtains and delivers funds to ecommerce applications without sharing a subset of sensitive data, associated with a mobile wallet application user for the secured transactions. The innovation further provides for a wallet network to be initiated and engaged in which the secured transactions can take place, adopting and adapting social network application connectivity.

Trust-based application to application connectivity

Dynamic allocation of cash. Dispensing of cash by an automatic teller machine (ATM) can be prioritized, e.g., for customers of the financial institution that operate the ATM over non-customers. A request by a customer to hold cash at an automatic teller machine (ATM) for future withdrawal is analyzed using an architecture of artificial intelligence. If the cash hold request is approved, in some examples the requested amount of cash is moved from a first cash rack of the ATM to a second cash rack of the ATM. The requested amount of cash cannot be moved from the second cash rack unless the customer withdraws it from the ATM or unless a time limit for the customer to withdraw the requested amount from the ATM is exceeded.

Dynamic allocation and dispensing of cash

Systems, methods, and computer program products are disclosed for issuing one or more blockchain tokens for property rights. An example method includes receiving an indication of user input including attributes of a property and, and generating a primary blockchain token, where the primary blockchain token includes metadata that correspond to the attributes of the property. The example method further includes broadcasting, over a blockchain network, a transfer of the primary blockchain token to the primary party, and generating one or more secondary blockchain tokens, where each of the one or more secondary blockchain tokens includes respective secondary metadata that correspond to unique subdivided property rights regarding the property, where the secondary metadata include a link to the primary blockchain token. The example method further includes broadcasting, over the blockchain network, transfer of the one or more secondary blockchain tokens to one or more secondary parties.

Systems and methods for issuing blockchain tokens for property rights

A method for preventing the double-spending of digital currency that transfers between multiple distributed ledger technology (DLT) networks. The method includes receiving a message including a smart contract and executing the smart contract. The method further includes detecting a transaction request to transfer the first digital currency from the first DLT network to the second DLT network. The method further includes destroying the digital currency and creating, based on the unit of fiat currency, a second digital currency of a second type on the second DLT network. The method further includes transferring, responsive to detecting the transaction request, the first digital currency from the first DLT network to the second DLT network.

Bank-driven model for preventing double spending of digital currency transferred between multiple DLT networks using a trusted intermediary

Systems, apparatus, and computer program products are disclosed for generating a verification non-fungible token. An example method includes receiving an event generation request and in response to receiving the event generation request, capturing an asset position and a timestamp. The method further includes performing one or more authentication operations on the event generation request based on the asset identifier and the one or more authentication credentials. The method further includes, in an instance in which the one or more authentication operations authenticates the event generation request, generating the verification non-fungible token for the asset identifier and storing the verification non-fungible token in an associated distributed ledger.

Proof of presence using non-fungible tokens

Systems, apparatuses, methods, and computer program products are disclosed for mitigating threats using artificial intelligence. An example method includes: obtaining, via communications hardware of a threat manager, a piece of data; determining, by a threat identification engine of the threat manager, that the piece of data is a threat; and causing, by a remediation engine of the threat manager and in response to the determination, execution of a threat remediation action. The threat remediation action comprises at least one of: publishing an emergency remediation statement addressing the threat, transmitting a takedown request to remove the threat from a threat source, or ignoring the threat.

Systems and methods for threat mitigation using artificial intelligence

Systems, apparatuses, methods, and computer program products are disclosed for dynamically generating a new data rule. An example method includes receiving a data rule including an operation to be applied to a data entry and determining regarding whether the data rule belongs to a set of previously-reviewed data rules. The example method further includes, when the data rule does not belong to the set of previously-reviewed data rules, providing the data rule to a user for human validation and receiving human validation. The example method further includes, causing an update, by rule generation circuitry, of an element of a machine learning model, where the element corresponds to the data rule where the update changes a weight value based on the validation and generating the new data rule based on the updated machine learning model. The example method further includes replacing the data rule with the new data rule.

Systems and methods for dynamically generating new data rules

An example method includes receiving and preprocessing a query request prior to execution of a query. Through preprocessing, a probability that execution of the query would yield a result set similar to at least a portion of results stored in a repeated query result repository is obtained. In response to the probability satisfying a probability threshold, a portion of results is retrieved from the repository. The example method further includes causing transmission of a response to the query request, the response comprising the portion of results.

Systems and methods for reducing computational resource usage in a query-based data access system via a repeated query results repository

Systems, apparatuses, methods, and computer program products are disclosed for managing operation of a distributed system that provides computer-implemented services. An example method includes collecting, via an out-of-band stream, sensor data from a plurality of temperature sensors positioned to infer temperatures of components of a distributed system. The example method further includes inferring, using the inferred temperatures, power consumption rates for performing respective actions with each of the components. The method further includes obtaining an action request and selecting, based on the inferred power consumption rates, a particular component of the components to perform an action to service the action request, the selection being made with a preference to reduce power consumption for performance of the action. The method further includes performing, with the particular component, the action to service the action request, the action resulting in updated computer-implemented services being provided by the distributed system.

Systems and methods for managing power consumption of a distributed system

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Hyderabad Telangana, India

Rameshchandra Bhaskar Ketharaju