International Business Machines Corporation

Bank of America Corporation

Globalfoundries Inc.

Daedalus Blue LLC

Maharaj Mukherjee

George Albero

Jonathan Lenchner

Shang Qing Guo

Mark Alan Lavin

Utkarsh Raj

Alan Edward Rosenbluth

Kafai Lai

Gregg M Gallatin

Shlomo Shlafman

Dov Ramm

Emanuel Gofman

Canturk Isci

Jinna Zevulun Kim

Rahul Gupta

Elvis Nyamwange

Carl M Benda

Suman Roy Choudhury

Monimala Mukherjee

David B Kumhyr

James A Culp

Prashant Thakur

Manu Jacob Kurian

Shikhar Kwatra

Hari Haranath Madduri

Joanna W Ng

Scott Marshall Mansfield

Andrew Aaron

Deborah Mayers

Benjamin D Moores

Sarbajit Kumar Rakshit

Rhonda L Childress

Gregory J Boss

Jeremy R Fox

Lars W Liebmann

William C Leipold

Heather Roseann Dolan

Daniel Nelson Maynard

Jeffrey Mark Achtermann

Kevin A Delson

Joel W Branch

David L DeMaris

Emmanuel Tong-Viet

Arnaud Airy Mathieu

Veerandra S Srivastava

Gandhi Sivakumar

Christopher J Hardee

Siten Sanghvi

Elijah Clark

Bette L Bergman Reuter

Daniel A Mazzella

Rick A Hamilton, Ii

Lisa Seacat DeLuca

Michael Bender

Eren Kursun

David Bruce Lection

John Maxwell Cohn

Anna Wanda Topol

Brian Paul Gaucher

Faried Abrahams

Ioana C Graur

Naga Vamsi Krishna Akkapeddi

William August Stahlhut

Amol Ashok Dhondse

Morgan Stewart Allen

Anand Pikle

Olga Kocharyan

Kyle A Mayers

John C Nelson

Karen E Davis

Colin Murphy

Vidya Srikanth

Liam S Harpur

Jonathan D Dunne

Kelley Anders

Komminist S Weldemariam

John David Wilson

Mark B Stevens

Michael Ogrinz

Jayant R Kalagnanam

Paul N Krystek

Vijay Kumar Yarabolu

Rama Nand Singh

Farrokh Pourmirzaie

Lalit Agarwalla

Donald J Samuels

Timothy G Dunham

James J Siekman

Lewis William Dewey, Iii

Dustin Paul Stocks

Lloyd Alan Treinish

Rashida A Hodge

Sanjay Arjun Lohar

Chieh-Yu Lin

Rodrigo Alberto Rey

Christopher P Jones

Zachary Baum

Naeem Altaf

Leucir Marin, Jr

John S Murdock, Jr

Aspects of the disclosure relate to an automated model generation platform for recursive model building. A computing platform may receive a request for automated machine learning model building, and may identify a service offering corresponding to the request. Based on the identified service offering and using machine learning algorithms, the computing platform may select machine learning models and a corresponding sequence of model application (e.g., machine learning model information). The computing platform may store the machine learning model information along with a corresponding indication of the identified service offering. The computing platform may receive a request for model information corresponding to a service access request, and may identify that the service access request corresponds to a problem within the identified service offering. In response, the computing platform may send the machine learning model information, which may cause the enterprise service host system to generate a service output interface.

Automated model generation platform for recursive model building

Embodiments of the present invention provide a system for analyzing operational parameters of electronic and software components associated with entity applications to detect anomalies. The system is configured for extracting one or more historical images associated with resiliency status of electronic and software components associated with an entity application, analyzing the one or more historical images to generate a pixel wise average of the one or more historical images, generating similarity scores between the one or more historical images, determining a distribution of the similarity scores, receiving a real-time image associated with a current resiliency status of the electronic and software components associated with the entity application, generating a real-time image similarity score for the real-time image, and comparing the real-time image similarity score with the distribution to detect presence of an anomaly.

System and method for analyzing operational parameters of electronic and software components associated with entity applications to detect anomalies

Aspects of the disclosure relate to message validation. A computing platform may receive a message, sent by a first device and directed to a second device, from an electronic messaging server. The computing platform may verify, based on message sender details, an identity of a sender of the message. The computing platform may generate, after verifying the identity of the sender of the message, an authentication token for the message, and may inject the authentication token into the message. The computing platform may route, to the electronic messaging server and after injecting the authentication token into the message, the message. The computing platform may receive a request to validate the message, which may include the authentication token and details of the message. The computing platform may identify, using the authentication token and the details of the message, that the message is authenticated.

Email verification using injected tokens for message authentication

User authentication/login occurs prior to a user accessing a network, application, web site/service or coming in contact with a computing device. Pre-authenticating/logging-in of users in advance of the user visiting or accessing the computing technology. A single device passcode that unlocks the device and a passcode vault storing the user's passcodes. Unlocking the passcode vault provides for the passcodes stored therein to be automatically applied to the corresponding entities requiring authentication to grant users access without requiring any user input/actions. Machine-Learning (ML) models that are trained to learn user login patterns are implemented to identify login pattern(s) and subsequently passcodes are retrieved for the entities making up the login pattern(s) from the passcode vault. Prior to the user accessing the entities that make up the login pattern, the present invention accesses the entities and applies the retrieved passcodes to pre-authenticate the user.

Pre-access automated login to computing devices, applications and web sites/services based on machine-learning determined user access patterns

A single device-level passcode serves to unlock a passcode vault, such that, automated login/access to other computing devices, applications, networks and/or websites/web services, which have the user's passcodes stored in the vault, occurs. Specifically, in the case of other computing devices, short-range wireless communication between the initial logged-in computing device and another computing device triggers retrieval of the other computing devices passcode from the passcode vault and automated user log-in at other computing device. The passcode may be mapped to previously-captured user characteristics, such that multi-factored authentication including user characteristic verification may occur at the time the passcode vault is unlocked (i.e., when the single passcode is entered), continuously throughout the device session and/or when the passcode vault is accessed to retrieve a passcode for another device, network, application or web site/service.

Single device-level passcode that provides automated access to multiple computing devices and passcode-protected applications and web sites/services

A computing platform may train, using historical telemetry state images, an image comparison model to identify matches between telemetry state images. The computing platform may generate a plurality of system alerts corresponding to a period of time. The computing platform may access telemetry data corresponding to the period of time. The computing platform may generate, based on the telemetry data and for a time corresponding to each of the plurality of system alerts, a telemetry state image. The computing platform may input, into the image comparison model, the telemetry state images to identify whether or not any of the plurality of telemetry state images match. Based on detecting a match, the computing platform may consolidate system alerts corresponding to the matching telemetry state images, which may produce a single system alert and may send, to a user device, the single system alert.

System and method for consolidation of alerts and events using image matching of heatmap descriptions of infrastructure status

Alternative distribution/communication of and access to One-Time Passwords/Passcodes (OTPs). Triggering events are detected that result in the OTP being communicated to a communication device different from the device designated by the user for receiving OTPs. The triggering event may be determination that the user's designated device (i) is not currently in possession of the user, (ii) is located in a geo-location designated for No-OTP receipt, and/or (iii) is currently in an inoperable state. The different communication device may be a device other than one in possession/control of the user and may be a device designated by the user or the device may be one selected, based on proximity of location to the user, from a network of trusted devices/individuals.

Alternative distribution and accessing of one-time passwords (OTPs)

A computing platform may train, using historical call information, a prompt generation model to identify, for an initiated call between a first individual and a second individual, one or more security prompts to validate an identity of the first individual. The computing platform may detect and temporarily pause a call. The computing platform may input, into the prompt generation model, information of the call, which may cause the prompt generation model to output the security prompts, which may be customized CAPTCHA tests based on the information. The computing platform may send, while the call is paused and to a user device of the first individual, the security prompts. The computing platform may receive, while the call is paused and from the user device, responses to the one or more security prompts. The computing platform may validate, while the call is paused, the responses, and resume the call.

Automated vishing detection to prevent deepfake and chatbot attacks

Aspects of the disclosure relate to energy optimization. A computing platform may receive a cryptocurrency request indicating a gas price and KPIs of the cryptocurrency request. The computing platform may input the gas price and the KPIs into a mining optimization model, which may cause the mining optimization model to output an energy mix for use in processing the cryptocurrency request. The energy mix may indicate energy sources, capable of providing an amount of energy needed to process the cryptocurrency request and used to support operation of a data center configured to process cryptocurrency requests, and a cost of the amount of energy from the energy mix may be less than a cost of the amount of energy from other available energy sources. The computing platform may send commands directing the data center to process the cryptocurrency request using the energy mix, which may cause it to do so.

Energy optimization platform for cryptocurrency mining

Aspects of the disclosure relate to machine learning and quantum computing. A computing platform may receive historical information, which may include feature information and rate of change information. The computing platform may train, a ML model, by inputting the feature information and the rate of change information, which may make the ML model stable against data drift. The computing platform may receive a first query, input the first query into the ML model, to identify a solution to the first query, which may include identifying a first solution, perturbing the first solution a number of times, and ultimately identifying a second solution, more accurate than the first solution. The computing platform may send this second solution to the user device.

Model optimization and stabilization using quantum computing

Systems, computer program products, and methods are described herein for enhanced encryption orchestration and application integration frameworks. The system performs steps of initializing a broker application to act as a data security broker within a computing environment; providing an application programming interface (API) for external application interaction; receiving and processing requests via the API for access to encrypted code and data; containerizing the code and data within an isolated environment, subject to predefined rules; monitoring usage to ensure adherence to these rules; initiating self-destruction of the container upon reaching a predetermined usage limit or condition; and securely erasing all sensitive data and code after usage completion.

System and method for enhanced encryption orchestration and application integration framework

A device for performing a secured operation is disclosed. The device is embedded with a predefined data item and verification information. The verification information is uniquely associated with the user of the device. The verification information is linked to the identity of the user. The verification information is configured to be extracted in conjunction with the secured operation being performed. The secured operation is performed in response to the verification information being verified. A system and method for performing the secured operation are also disclosed.

System, device, and method for performing secured operations

Methods for detecting non-linear data dependencies in machine learning using hybrid quantum computing. Methods include receiving a selection of an accuracy metric. Methods include receiving a data set comprising a plurality of data elements for processing by a machine learning model operating on a machine learning system. Methods include identifying a plurality of data elements within each data set. Methods include identifying one or more features for each data element. Methods include determining a total number of features for the data set. Methods include reducing, by a quantum annealing method, based on the accuracy metric, the total number of features to a reduced number of features. Methods include inputting the reduced number of features into the machine learning model. Methods include outputting a result from the machine learning model.

Non-linear data dependency detection in machine learning using hybrid quantum computing

A computing device may configure a plurality of device partitions. The computing device may send, to a one time password (OTP) server and via an interface of a first partition of the plurality of device partitions, a login request. The computing device may receive, at a second partition of the plurality of device partitions, an OTP. The computing device may access, by validating authentication credentials corresponding to the second partition of the plurality of device partitions, the OTP. The computing device may send, to the OTP server, the OTP. The computing device may access, upon receiving confirmation of validation of the OTP, services hosted by the OTP server.

Enhanced security with multiple factor authentication at devices using partitions

Aspects of the disclosure relate to outage prevention. A computing platform may train, using historical parameter information and historical outage information, an outage prediction model. The computing platform may receive, from at least one system, current parameter information, and may normalize the current parameter information. The computing platform may convert, using a CNN of the outage prediction model, the normalized current parameter information to a frequency domain. The computing platform may input, into at least one RNN of the outage prediction model, the frequency domain information, to produce a likelihood of outage score. The computing platform may compare the likelihood of outage score to a predetermined outage threshold. Based on identifying that the likelihood of outage score meets or exceeds the predetermined outage threshold, the computing platform may direct the at least one system to execute a performance modification to prevent a predicted outage.

Hybrid neural network for preventing system failure

Growing community of inventors

Poughkeepsie, NY, United States of America

Maharaj Mukherjee