Bank of America Corporation

Prabhakaran Balasubramanian

Subburathinam Krishnan

Durga Prasad P Khuttumolu

Yugandhar Jaini

Anantharajan Srinivasarangan

Abhishek Arya

Amit Mishra

Durga Prasad Kutthumolu

Kiran Pulla

Vamshi Krishna Jutur

Siva Kumar Peesapati Venkata

Medapati Venkat Kishore Reddy

Jenita Mercy

Suriyanath S

Aspects of the disclosure relate to a real-time dynamic container optimization computing platform. The real-time dynamic container optimization computing platform may receive a request to create a first processing block and first data associated with the first processing block. The real-time dynamic container optimization computing platform may utilize a plurality of models to select a first computing device for the first processing block. The real-time dynamic container optimization computing platform may generate and deploy a container to the first computing device. The real-time dynamic container optimization computing platform may monitor execution of the container on the first computing device. The real-time dynamic container optimization computing platform may migrate the container to the second computing device if an issue with execution of the container on the first computing device is detected.

Real-time dynamic container optimization computing platform

Systems and methods are disclosed to provide a security framework for the authentication of digital content and the prevention of unauthorized modifications, especially targeting the vulnerabilities introduced by deepfake technologies. It innovates by merging reverse engineering with expression manipulation detection to discern genuine from altered digital media. The process involves comparing historical data against synthetic or real-time content, generating a 'video mesh' that enables precise manipulation identification. Enhanced by Smart Contracts for each content piece to record and verify changes, this system advances digital identity and transaction security significantly beyond current methodologies. Furthermore, it employs Generative AI within the Identity Intelligent Clip Reviewer to scrutinize blockchain-secured data for manipulation signs, issuing a Proof of Digital Manipulation (PODM) for verified authenticity. This comprehensive method ensures the integrity and trustworthiness of digital interactions across various platforms, marking a significant step forward in the protection against sophisticated cyber threats and unauthorized data alterations.

Spurious less data authentication by method mesh engineering using digital GenAI with proof of digital manipulation (PODM)

A processor detects a first failure incident associated with a failure of a data interaction relating to a transfer initiated by a user device of data objects from a source data file associated with the user device to a target data file. The processor obtains a first interaction log associated with the data interaction, compares it with a plurality of second interaction logs associated with a plurality of known failure incidents, and determines a correlation probability. If the correlation probability equals or exceeds a threshold probability, the processor determines a first known failure incident that is associated with the first failure incident. The processor obtains a first known solution that corresponds to the first known failure incident and processes the data interaction based on the first known solution by performing at least one corrective action specified in the first known solution.

Resolving failed data interactions performed by an internet of things (IoT) device

Virtual assistant devices and related methods are described that allow users having special needs to communicate with the virtual assistant device. The virtual assistant devices are augmented with additional intelligent sensors, including, but not limited to, three-dimensional optical sensors that are used to capture optical signals of user's non-verbal communication (e.g., sign language, lip movements, gestures or the like). Additionally, the virtual assistant device includes an Artificial Intelligence (AI)-based engine that includes one or more Machine Learning (ML) models trained on user-specific data and used to determine the non-verbal communication of the user (i.e., sign language, lip movements or other gestures) based on inputs derived from the sensors. Moreover, the virtual assistant devices may be configured to generate and display visual information, such as three-dimensional floating avatars of the user and/or the virtual assistant.

Multi-dimensional extrasensory deciphering of human gestures for digital authentication and related events

A processor records in a memory a plurality of data interactions conducted using a first user device associated with a first user and determines a behavior log associated with the first user, wherein the behavior log stores events associated with the plurality of data interactions conducted using the first user device. The processor monitors a computing network for future events that are to occur in the computing network and determines based on the monitoring that a first future event is to occur in the computing network. The processor compares the first future event with the behavior log and determines that a first data interaction was previously conducted using the first device in relation to a second event that is associated with the first future event. The processor initiates the first data interaction in relation to the first future event using the first user device.

Selecting data interactions to be performed by an internet of things (IoT) device

A system for generating an expandable pool of virtual agents to provide solutions in a virtual environment comprises a call center server in communication with a metaverse server. The call center server generates the virtual agents with corresponding skills of each real-world agent using a machine learning model based on historical observation operations performed by each particular real-world agent and a historical rule set to address the service issue. The metaverse server identifies the virtual agents in the virtual environment. The call center server routes a service inquiry with a service issue from a user equipment of a user to the metaverse server when determining that a particular real-world agent is not available. The metaverse server identifies a first virtual agent with a first skill and conducts a first interaction between the first virtual agent and a user avatar to generate the customized solution to address the service issue.

System and method for generating an expandable pool of virtual agents to provide solutions in a metaverse

In response to receiving a tagging request to map a first non-fungible token (NFT) to a first user device, a processor transmits an authorization request to a second user device to map the first NFT to the first user device. In response to receiving an approval of the request, the processor transmits a request to a minting server of an NFT blockchain network to generate the first NFT for the first user device. The processor stores a token ID of the generated first NFT in a memory. In response to receiving a second authorization from the first user device to perform a data interaction, the processor requests the NFT blockchain network based on the token ID, verification of the first user device. In response to receiving an indication that the first user device is verified, the processor processes the data interaction.

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Vellore, India

Prabhakaran Balasubramanian