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

Abhijit Shetti

Naveen Gururaja Yeri

Saipavan K Cherala

Ashutosh Verma

Rama Rao Yadlapalli

Ajay Kumar Rentala

Vamsi Krishna Geda

Sumanth Kumar Charugundla

Nikolai Stroke

Uma Meyyappan

Darius A Miranda

Laura Marie Fontana

Duc M Trinh

Rajiv Ranjan

Ananth Kendapadi

Todd Biggs

Mabel Oza

Chandramouli Subramanian

Prasanna Kumar Ammiraju

Joel Eric Boeder

Makarand Shivadevuni

Parul Ghosh

Chandrasekaran Sivaraman

Sudheendranath R Bhatt

Vinayak Sodar

Manak Suri

Sarath Chava

Sitara Kumbale

Mayuri Chaubey

Yang Yang

Pradeep R Kumar

Vinothkumar Venkataraman

Rahul N Jain

Matt Hord

Paul Davis

Ramya Balasubramanian

Madhu V Vempati

Mohammed Arif

Sajeev Philip

Hemakumar Madabathula

Aditya Kishore Jonnalagadda

Jayasree Iyer

Mathewbabu Edappilly Antony

Ramakrishna Bejjanki

Amit Tripathi

Arun Raj

Tyler Case

Damaraju P Vittal

Ravi Babu Bandla

Hem Shankar Karlapalem

Daniel S Sumner

Suresh Parpatakam Reddy

Dinakar Channakal Krishnappa

Sri Rajesh Rayudu

Santosh Nichani

Srinivas Vengala

Manish Pandey

Gothandapani Krishnan

Niraj Kumar

Srivathsan Sridharan

Rajesh Padincharekkara Mannachery

Madeshwaran Mohanraj

Jayakrishna Nair

Kalyan Chandra Kothamasu

Parthasarathy Venkatesan

Swati Bose

Arshad A Shaik

Gail Elizabeth Stuart

Dibyendu Chatterjee

Kshitindra Kumar Jain

Ravindar Rao Perala

Poornima Mahabaleshwarappa

Dinesh Sangtani

Mohan Gopal Kanala

Prashanth Kumar Merugu

Naveen Saichand Palle

Vasudev Tumkur Umapathi

Ramesh Chinnasamy

Prabal Nandi

Sai Krishna Madhavi Chitta

Sathya K Balakrishnan

Madhu Gumballi

Lakshmi Sowrirajan

Vinod Mannattil

Satyavathi Divadari

Sridhara Rao Sidhu

Dennis Emmanuel Montenegro

Pankaj Parekh

Himanshu Baral

Suhas Dattatreya Sankolli

Chloe D Cangardel

Anupam Upadhyay

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

Embodiments are generally directed to techniques for query-based binarized point decisions, such as with a system that can provide end-to-end decision making support. Some embodiments are particularly directed to a system that makes point decisions, with traceable reasoning, for queries while factoring in a variety of influences. In many embodiments, the traceable reasoning may refer to the generation and collection of metadata associated with various aspects of the decision making process. For example, the metadata may include, or be used to create, a decision lineage and/or reasoning summary that is generated for each point decision. Some embodiments may ensure various influences do not derail the query from the overall objective. In several such embodiments, the point decisioner may confirm or deny that a decision was made with aspect to a certain objective.

Query-based binarized point decisions

A distributed computing system includes a blockchain comprising a plurality of blockchain services comprising an oracle service, a predictive analytics service, a loan service, and a consensus service. The oracle service configured to receive input data comprising at least one of financial data associated with at least one of the plurality of syndicate participants, macroeconomic data, geo-location data, disaster data, or public health data. The predictive analytics service configured to utilize the input data to determine whether a first syndicate participant of the plurality of syndicate participants has an excess of funds and whether a second syndicate participant of the plurality of syndicate participants has a lack of funds where the first syndicate participant is different than the second syndicate participant. The loan service configured to facilitate a loan agreement between the first syndicate participant and the second syndicate participant. The consensus service enabling a consensus process used to agree on a validity of data transactions that are recorded by the blockchain such that the consensus service maintains a replicating ledger within the distributed computing system.

Geolocation-based mesh automatic lending network

A system and methods include a negative certificate authority for distributed management of negative certificates. An authorization restriction is associated with an untrusted user. A negative certificate generated for the untrusted user includes a public key associated with the untrusted user and an authorization restriction. The authorization restriction includes at least one global restriction, which is applicable to each consortium member that subscribes to the negative certificate. The authorization restriction includes at least one local restriction, which allows individual consortium members to further define their own locally applicable restrictions using the negative certificate authority. The negative certificate is accessible to each member of the consortium to enforce the authorization restriction against a transaction request. A secure contributor record including a unique cryptographically generated address is generated for each contributor. In some embodiments, the transaction request is a blockchain-based value exchange transaction, such as a payment transaction using fiat or math-based currency.

Systems and methods for distributed management of negative certificates for blockchain-based value exchange transactions

In one example, a method includes at a first network infrastructure computing device of a plurality of networked infrastructure computing devices receiving, from a user computing device, an authentication token indicating a user using the user computing device is authenticated with respect to an electronic service, based on the user being authenticated, executing a computing task associated with the electronic service for the user computing device, the computing task including computing task data, predicting a set of possible future locations of the user based on an output of a movement prediction model executing on the first network infrastructure computing device, identifying a set of network infrastructure computing devices correlated with the set of possible future locations, and transmitting the authentication token and the computing task data to the set of network infrastructure computing devices.

Computing task transfers between cellular infrastructure devices

An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes scheduling, by context analysis circuitry and based on the occurrence of the triggering condition, retraining of a model. The example method also includes generating, by data grafting circuitry and in response to scheduling the retraining of the model, a context-relevant training data set based on a target context vector. The example method also includes retraining, by model training circuitry, the model using the context-relevant training data set to mitigate deterioration of performance of the model.

Systems and method for data grafting to enhance model robustness

A method includes using a distributed computing system to form a syndicate, generate a unique identifier for each of a plurality of participants of the syndicate, receive a verification request comprising the unique identifier from each of the plurality of participants, verify that a respective entity is a node on the distributed computing system, receive loan terms templates from each of the plurality of participants, receive a plurality of data from each of the plurality of participants, identify a first subset of participants having an excess of funds and a second subset of participants having a lack of funds, and verify each of the first subset of participants and the second subset of participants. The method includes using a syndicate computing system to automatically execute at least one loan agreement between at least one of the first subset of participants and at least one of the second subset of participants.

Systems and methods for data grafting to enhance model robustness

A method may include, receiving, at a server device and from a requesting computing device associated with a requesting user, a request for a data item. The data item configured may be configured as a data structure and include an encrypted data payload; metadata identifying a source of the payload and when the data payload was captured; and a token identifying an owner of the data item and access rights to the data item. The method may include parsing the token to identify the owner of the data item; transmitting the request for the data item to a computing device associated with the owner; receiving a response from the computing device. The response may indicate the requesting user may access the data item, and in response, transmit a decryption key to the requesting computing device for the encrypted data payload. The method may include updating the token.

System for data access token management

Systems, apparatuses, methods, and computer program products are disclosed for mitigating deterioration of modeling system performance. An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes determining, by context analysis circuitry and in response to detecting the occurrence of the triggering condition, a destination modeling solution based on a regime strategy for the modeling system. The example method also includes switching, by regime deployment circuitry and based on the regime strategy, from a source modeling solution to a destination modeling solution.

Systems and methods for using regime switching to mitigate deterioration in performance of a modeling system

Growing community of inventors

Hyderabad Telangana, India

Rameshchandra Bhaskar Ketharaju