Bank of America Corporation

Abb Technology Ltd

Pictory, Corp

Shailendra Singh

Sandeep Kumar Chauhan

Maneesh Kumar Sethia

Vasuki Anand

Saurabh Gupta

Abhijit Behera

Saurabh Garg

Rama Rao Gaddam

Satyajeet Priyadarshi

Ashutosh Tripathi

Durga Prasad Kutthumolu

Saurabh Arora

Nandini Rathaur

Sneha Padiyar

Amardeep Singh Talwar

Kamlesh Kanayalal Gangaramani

Amit Kumar Bidhanya

Swadeep Mekala

Gowri Sundar Suriyanarayanan

Sandeep Verma

Amit Mishra

Prashant Khare

Puneetha Polasa

Krithika Viswanathan

Govinda Rajulu Nelluri

Nimish Ravindra Deshpande

Sheetal Bhatia

Saurabh Uprit

Savitri Jaganath Podal

Swathi Bussa

Saravanan Balasubramanian

Bhagat Kumar Allugubelly

Vinod Maghnani

Zhanwei Tu

Santosh Kumar Miryala

Suryanarayana Adivi

Divya Sharma

Ramarao Gaddam

Moses Salagala

Pushkar Taneja

Ajay Kumar Gowni

Suresh Loganathan

Govinda Ramam Gonna Satya Sai

Ankit Dholakiya

Tushar Agarwal

Arup Francis

Pushpa Neelakantan

Subbiahthever Dukkaiappan

Vikram Chalana

Vishal Chalana

Vaddi Sreenivasulu Reddy

Ashish Kumar Dwivedi

Salma Umme

Sarabjeet Kaur

Gaurav Sachdeva

Sukanya V

Kavitha Hariharan

Raj Kiran Akkera

Jai Issrani

Sharvani Ganachary

Charles Joseph Fernandez

Shyam Prasad Rokula

Abid Ali Mohammed

Anu Joy

Amaresh Gunasekar

Arul Victor Benjamin

Pavan Kumar Gauji

Divya Nagarajan

A method is provided herein for evaluating a physical building, utilizing an automated image capture apparatus, a spatial computing apparatus, a machine learning model, a blockchain network, and a computer processor, the method including capturing and analyzing images of the building, dividing the building into sections, identifying defective characteristic(s) in and assigning a status to each section, sharing the images and status identifiers with a remote user, the remote user reviewing the images and identifiers, and receiving feedback from the remote user. When a section lacks sufficient images to determine whether a defect is present, the section may be flagged, and a supplemental image(s) may be captured and provided by the remote user. When no additional images are needed, the section may be indicated as completed. Otherwise, the section may retain its flagged status, and the aforementioned steps may be repeated as necessary.

Methods and systems for evaluating buildings

Apparatus and methods for securing a communication link. The apparatus may include a microprocessor. The apparatus may include a memory cell. The apparatus may include a photovoltaic circuit. The apparatus may include a radio frequency transceiver circuit. The apparatus may include an organic light emitting diode display circuit. The microprocessor may be embedded in an information card. The memory cell may be embedded in the information card. The photovoltaic circuit may be embedded in the information card. The radio frequency transceiver may be embedded in the information card. The organic light emitting diode display may be embedded in the information card. The display circuit may include an array of separately excitable diode fields. The display circuit may include a display controller that is in electronic communication with each of the fields.

Mobile phone pairing for dynamic spatial activation of communication device

A registered lower orbit satellite-powered generative artificial intelligence ('AI')-based lost active smart card (“ASC”) tracking and reporting mechanism. The methods and systems may include detecting a threshold distance between a lost ASC and a user device. The lost ASC may initiate a reporting, may include a microwave communication transceiver, and may be associated with an originating entity. The methods and systems may include determining a threshold time that the lost ASC was located at least the threshold distance from the user device. The methods and systems may include tracking the geolocation of the lost ASC by submitting instructions to the lost ASC, instructing the lost ASC to emit a beeping noise, and reporting lost ASC geocoordinates to the originating entity. The lost ASC may emit the beeping noise in response to receipt of the instructions from a satellite network.

Registered lower orbit satellite-powered generative artificial intelligence ('AI')-based lost card tracking and reporting mechanism

A system includes a memory and a processor that validates an interaction between an initiating and a receiving device. An encrypted request is received to perform the interaction and the processor extracts an initiating device identification, a receiving device identification, and one or more numerical values associated with the interaction. Using this extracted information, the processor produces a verification key by applying a hash function and a verification checksum value using a checksum function. The verification key and verification checksum value are then compared with an authentication key and authentication checksum value that were produced in a same manner prior to the interaction being initiated. If it is determined that either the verification key and the authentication key do not have the same value, or verification checksum value and authentication checksum value do not have the same value, then the interaction is reversed.

System and method for validating an interaction using an agreement key

A method for modifying smart card properties of a smart card leveraging a spatial computing device is provided. The spatial computing device may be in electronic communication with an entity server. The method may include identifying the smart card within a field of view of the spatial computing device. The method may include establishing a wireless communication between the smart card and the spatial computing device. The method may further include generating a digital copy of the smart card and displaying the digital copy of the smart card on a display of the spatial computing device. The method may further include displaying a plurality of selectable options for modifying card properties of the smart card and in response to a detection of a selection of one of the selectable options, transmitting a request to the entity server to modify a property of the smart card based on the selected option.

Leveraging spatial computing to modify smart card properties

A method for providing a QR code. The method includes generating a map of an environment surrounding a spatial computing device. The map may be converted into a video. The video may be stored in a database. The video may be divided into multiple frames. A pattern may be arranged for placement of the frames. The frames may be placed inside the QR code in accordance with the pattern. The QR code may be scanned. The pattern may be decoded. The video frames may be retrieved, and the video may be restored. A request associated with the QR code may be executed.

Spatial computing device powered lidar technology enabled dynamic quick response ('QR') code generation mechanism

An apparatus may comprise a memory communicatively coupled to a processor. The memory may be configured to store user profiles and security information. The processor may be configured to receive a device profile from a user device. Further, the processor may be configured to validate the device profile against a user profile; determine an entitlement associated with the user profile; generate an initial token indicating that the device profile is entitled to access the entitlement; transmit the initial token to a decentralized network; and receive an updated token from the decentralized network. The updated token may indicate a firewall configuration based at least in part upon the initial token. The firewall configuration may be configured to enable the user device to access the entitlement. The processor may be configured to transmit the firewall configuration to the user device to implement a firewall at the user device.

System and method for updating a firewall security configuration in a decentralized network

A request is transmitted to a second computing node of a second blockchain network to perform a data interaction between a first computing node of a first blockchain network and the second computing node. Upon receiving an acknowledgement of the request, a first smart contract, an encrypted first network address and a first authentication code is generated. The first smart contract is encrypted using the first authentication code to generate an encrypted first smart contract. The encrypted first smart contract, the encrypted first network address and the first authentication code is transmitted to the second computing node. A digital signature is received indicating an approval of the first smart contract. Thereafter, data objects are transmitted to the second computing node as agreed in the first smart contract.

System and method for exchanging data between blockchain networks

A card device receives a first sensor data from sensors associated with the card device and extracts a set of user features from the first sensor data, where the user features indicate physical attributes of the user. The card device receives a second sensor data from the sensors. The card device extracts a set of viewer features from the second sensor data, where the viewer features indicate physical attributes of the viewer. The card device compares at least one of the user features with a counterpart feature from among the viewer features. The card device determines that the user is not the viewer. The card device determines a distance between the viewer and the card device. The card device determines that the distance between the viewer and the card device is less than a threshold distance. In response, the card device dynamically masks the information displayed on the card device.

Altering card device attributes in response to detecting a proximity intrusion

Provided herein is a spatial computing system. The system may include a spatial computing apparatus configured to receive an anchoring instruction and consequently anchor a virtual object to a physical object; an onboarding module configured to interface with an existing spatial computing software platform; a spatial telemetry extraction module configured to extract positioning metadata associated with the physical object; a spatial analyzer module configured to detect an anomaly in the anchoring instruction, wherein the anomaly may indicate a suspected attempt to glean personal information from an image of the physical object; an anchor validation module configured to monitor changes in anchoring instructions; a session management module configured to terminate a spatial computing session, upon receiving an indication of an anomaly; a spatial security rule orchestration module configured to block implementation of the anchoring instruction; and a deep learning module.

Apparatus and method to monitor and prevent malicious anchoring in a spatial computing environment

Systems, methods, and apparatus are provided for implementing a dynamically customized ATM payload. An ATM may extract a user identifier and home region during a session. The ATM may be a node on a distributed network and securely publish data to a distributed hash table. Extracted data may trigger smart contract logic including a transaction rule associated with the user and a transmission rule restricting network switches for payload delivery outside the home region. AI algorithms may generate a custom payload including the transaction rule and an interface adapted for the user home region. The payload may be pushed to the ATM via a set of spine-leaf switches based on the transmission rule. The payload may be deployed at the ATM during the session to modify a default ATM interface and a default ATM transaction rule. The ATM may purge the payload from memory when the session is terminated.

Dynamic custom automated teller machine (ATM) payload

Innovative distributed tracing system(s) and process(es) integrate automated code instrumentation, AI-driven quantum computing, particularly Photonic Quantum Computing, and real-time analysis algorithms. This system autonomously identifies and monitors network components such as gateways, service meshes, message queues, databases, and proxy servers, seamlessly capturing trace data across various communication protocols. It features dynamic scaling capabilities to handle fluctuating network loads and employs automatic tagging for precise identification of each network element and transaction. Utilizing advanced machine learning algorithms, the system efficiently analyzes trace data, identifying patterns, dependencies, and anomalies. It is equipped to intervene in operations by halting transactions under specific conditions. The incorporation of Photonic Quantum Computing allows for unparalleled anomaly detection and data analysis speed and accuracy. This system enhances trace data analysis, improves anomaly detection, and optimizes overall system performance, offering a robust, secure, and efficient approach for managing complex distributed systems.

Distributed data tracing

Apparatus and methods for a li-fi payment network are provided. A customer's device may communicate, directly or through intermediate devices and computer systems, over a li-fi network with a point-of-sale terminal. A bill may be transmitted to the customer's device over the li-fi network. The customer may select an option to pay the bill. The selection, along with information to authorize and process the payment, may be transmitted over the li-fi network to the POS terminal. The POS terminal may process the payment and generate a receipt. The receipt may be transmitted to the customer's device over the li-fi network.

Li-fi payment network

Arrangements for leveraging quantum computing to optimize cash recycling at ATMs are provided. Current status data including location data, cash depletion rate data, and the like, may be received from a plurality of ATMs. Telemetry data from one or more cash replenishment vehicles may be received. The telemetry data may include GPS-based location data from a computing device associated with each vehicle, speed data, and the like. A machine learning model may be executed using the ATM status data and telemetry data as inputs to output an optimized replenishment scenario. In some examples, quantum computing may be used to analyze the data and identify the optimized replenishment scenario for execution. Executing the optimized replenishment scenario may include generating one or more instructions that causing one or more ATMs to activate or deactivate cash recycling functionality, causing one or more cash replenishment vehicles to modify a replenishment route, or the like.

Leveraging quantum computing to optimize cash recycling

A decentralized swarm intelligence algorithm over a network of paired ATMs to prevent or control surface attacks on ATMs. The ATMs may be seeded with an initial swarm intelligence model to identify suspicious activity. An ATM may relay alerts about suspicious activity at that ATM to other paired ATMs. The ATMs use machine learning to update the model and perform swarm intelligence autonomously at the paired ATMs. A bank may provide the initial swarm intelligence model, receive alerts about and analyze the attacks, and provide updated swarm intelligence models to prevent or limit future attacks. Smart contracts may be used to specify rules for performing swarm intelligence using the paired ATMs. Records regarding swarm intelligence models, attempted suspicious attacks including payment instruments that may have been used, actions taken in response to the attacks, and smart contracts may be recorded on a blockchain distributed ledger.

Controlling ATM surface attacks leveraging decentralized swarm intelligence using ATMS

Growing community of inventors

Maharashtra, India

Shailendra Singh