Abstract: A system for detecting security threats in a computing device receives a first set of signals from components of the computing device. The first set of signals includes intercommunication electrical signals between the components of the computing device and electromagnetic radiation signals propagated from the components of the computing device. The system extracts baseline features from the first set of signals. The baseline features represent a unique electrical signature of the computing device. The system extracts test features from a second set of signals received from the component of the system. The system determines whether there is a deviation between the test features and baseline features. If the system detects the deviation, the system determines that the computing device is associated with a particular anomaly that makes the computing device vulnerable to unauthorized access.

Abstract: Aspects of the disclosure relate to intelligent RPA bot development. A computing platform may identify user interface field information from one or more applications based on tracking eye movements of the user. The computing platform may identify device events associated with the eye movements of the user. The computing platform may generate a sequence log associating the eye movements of the user with the device events. The computing platform may derive, using a machine learning model, based on the sequence log, a cognitive model of the transaction by the user. The computing platform may generate a workflow for an autonomous bot by identifying and assembling robotic process automation components based on the derived cognitive model of the transaction by the user. The computing platform may send the workflow for the autonomous bot to a user computing device.

Abstract: Embodiments of connector-less modules and associated platforms employing the module. The module employs a Land Grid Array (LGA) comprising an array of LGA pins on the underside of the module PCB that are configured to engage respective pads patterned on a motherboard or system board PCB by applying a downward force to the module PCB. A novel clip assembly is provided to apply the downward force, while also aligning the module PCB (and its LGA) to the motherboard or system board PCB. A heat shield is provided that is configured to be disposed over the module PCB to facilitate enhanced thermal spreading and lowering thermal resistance both towards the heat shield and the PCBs. Example modules include a WWAN module and an NVMe SSD module. The module PCB may employ an M.2 form factor or other form factors.

Abstract: A wireless device includes a voltage regulator circuit configured to generate a voltage signal of a first input voltage, and a wireless baseband processing circuitry (WBPC) coupled to the voltage regulator circuit to receive the voltage signal. The WBPC is configured to process signals for transmission or reception using wireless technology. The WBPC includes a sub-system processor circuit configured to detect a wireless bandwidth of an application executing on an application processor of the wireless device; determine a second input voltage based on the wireless bandwidth of the application and a maximum voltage supported by the WBPC; and encode a feedback signal for communication to the voltage regulator circuit. The feedback signal causes adjustment of the voltage signal to the second input voltage.

Abstract: A computing device includes a plurality of baseband processing circuitries. Each baseband processing circuitry of the plurality of baseband processing circuitries is configured to process signals for reception or transmission using a communication standard of a corresponding plurality of communication standards. The computing device further includes an application processor coupled to the plurality of baseband processing circuitries. The application processor is configured to determine a bandwidth of an application executing on the application processor of the computing device, determine a plurality of latencies associated with each baseband processing circuitry of the plurality of baseband processing circuitries, and select a baseband processing circuitry of the plurality of baseband processing circuitries to process transmit or receive data of the application based on the bandwidth and the plurality of latencies.

Abstract: A system for intelligent data transfer is provided. The system is configured to: collect sensor data from a plurality of user devices, the plurality of user devices being connected to a device gateway in an edge layer of the network; combine the collected sensor data with contextual data stored in a contextual device database, wherein the contextual data comprises device usage data and user data; generate a data transfer rule set for governing data transfer from the plurality of user devices over the network based on the combined data; calculate a data configuration flow for the plurality of user devices based on the data transfer rule set; and execute the data configuration flow to control a flow of the sensor data transferred from the device gateway to an application server in a platform layer.

Abstract: Apparatus and methods for a smart glasses device are provided. The smart glasses device may execute a prediction model on video data captured by the smart glasses device to retrain the prediction model. The prediction model may be retrained in response to a data synchronization between an output of the prediction model and a gesture captured by the smart glasses device.

Abstract: Apparatus and methods for a payment instrument are provided. The apparatus may include a primary chip positioned on an outer face of the payment instrument and a power source. The apparatus may include a first supplementary chip, a second supplementary chip, and a third supplementary chip. Each of the first, second and third supplementary chips may be positioned within a thickness of the payment instrument and configured to draw power from the power source.

Abstract: Methods for supporting a development of application source code using input from a first smart glasses of a first user and a second smart glasses of a second user is provided. Methods may include retrieving the application source code from an internal development platform and displaying the application source code on an augmented reality (“AR”) display of the first smart glasses and an AR display of the second smart glasses. Methods may include receiving, from the first smart glasses, a command to edit the application source code and in response, deactivating each input device of the second smart glasses. Methods may include receiving input of one or more edits on the first smart glasses and updating the application source code to include the input. Methods may include displaying the updated application source code on the AR displays and reactivating each input device of the second smart glasses.

Abstract: A device configured to obtain group information from a database, to identify a first set of clusters based on the group information, and to determine a first cluster quantity that identifies a number of clusters within the first set of clusters. The device is further configured to obtain user interaction data for user devices, to input the user interaction data into a machine learning model, to receive a second set of clusters from the machine learning model based on the user interaction data, and to determine a second cluster quantity that identifies a number of clusters within the second set of clusters. The device is further configured to determine the second cluster quantity is greater than the first cluster quantity, to identify a cluster that is not present in the first set of clusters, and to modify settings on a user device from within the cluster.

Abstract: A device configured to identify a first set of clusters based on the group information and to determine a first cluster quantity that identifies a number of clusters within the first set of clusters. The device is further configured to obtain user interaction data for user devices, to input the user interaction data into a machine learning model, to receive a second set of clusters from the machine learning model based on the user interaction data, and to determine a second cluster quantity that identifies a number of clusters within the second set of clusters. The device is further configured to determine the second cluster quantity is greater than the first cluster quantity, to identify a cluster that is not present in the first set of clusters, and to modify settings on a user device from within the cluster.

Abstract: A device configured to obtain a first user interaction data at a first time instance for user devices, to obtain a first set of clusters from a machine learning model based on the first user interaction data, and to determine a first cluster quantity for the first set of clusters. The device is further configured to obtain a second user interaction data at a second time instance for the user devices, to obtain a second set of clusters from the machine learning model based on the second user interaction data, and to determine a second cluster quantity for the second set of clusters. The device is further configured to determine the second cluster quantity is greater than the first cluster quantity, to identify a cluster that is not present in the first set of clusters, and to modify settings on a user device from within the cluster.

Abstract: Systems, methods and apparatus are provided for a Dynamic Group Session Data Access Protocol. The system may monitor participant input in a group interactive session. The system may be trained to monitor and understand the group environment and predict intent of the participant discussion and may predict relevant data. The system may be used by a single participant or by multiple participants. The system may determine the access level of the participants. The system may determine the access level of the data. The system may compare the access level of the participants with the access level of the data. The system may dynamically mask the data if the access level of the participants does not match the access level of the data. The system may create customized views of the data for each participant based on the participant's access level and the access level of the data.

Abstract: Systems and methods for leveraging smart glasses for identifying vulnerabilities in application source code is provided. The smart glasses may be configured to scan the code from a user interface (“UI”) linked to an internal development platform and project the scanned code on a display of the smart glasses. Using deep learning, the smart glasses may be enabled to identify one or more vulnerabilities within the scanned application source code. The smart glasses may link a vulnerability classification code for each identified vulnerability. The smart glasses may generate a vulnerability report file including the scanned application source code, each identified vulnerability and its linked vulnerability classification code and transfer the vulnerability report file to a source code repository within the internal development platform. The scanned application source code may be matched to the original application source code and further separate the marched original application source code for assessment.

Abstract: Apparatus and methods disclosed herein provide technical solution for on-demand manufacturing of a payment instrument that includes an integrated circuit chip. Apparatus and methods provide technical solutions for securely validating and activating the manufactured payment instrument. The customer may submit a request to manufacture a payment instrument through an automated teller machine (“ATM”), online banking channel or mobile banking channel. Apparatus and methods allow a customer to manufacture a payment instrument at home using a 3D printer or use a 3D printer installed at an ATM. Using a secure validation methods, the newly manufactured payment instrument may be activated at an ATM.

Abstract: Techniques for wireless audio device selection are disclosed. In the illustrative embodiment, a compute device uses visual training to recognize headphones worn by a user. When the user puts on the headphones, the compute device may recognize the headphones and automatically route audio output to the headphones. In some embodiments, when selecting from several possible wireless audio output devices, the compute device may determine a distance to each of the wireless audio output devices and select one of the wireless audio output devices based on the distance to the wireless audio output device.

Abstract: An automated teller machine (ATM) receives a first set of signals from components of the ATM. The first set of signals includes intercommunication electrical signals between the components of the ATM and electromagnetic radiation signals propagated from the components of the ATM. The ATM extracts baseline features from the first set of signals. The baseline features represent a unique electrical signature of the ATM. The ATM extracts test features from a second set of signals received from the component of the ATM. The ATM determines whether there is a deviation between the test features and baseline features. If the ATM detects the deviation, the ATM determines that the ATM is associated with a particular anomaly that makes the ATM vulnerable to unauthorized access.

Abstract: A system for detecting security threats in automated teller machines (ATMs) extracts baseline features from a first set of signals received from a first ATM, when the first ATM is initiated to operate. The baseline features represent a unique electrical signature of the first ATM. The system extracts test features from a second set of signals received from the first ATM, when the first ATM is in operation. The system determines whether there is a deviation between the test features and baseline features. If the system detects the deviation, the system determines that the first ATM is associated with a particular anomaly that makes the first ATM vulnerable to unauthorized access. The system determines that a second ATM is associated with the particular anomaly if the system detects the deviation between baseline features and test features associated with the second ATM.

Abstract: A system for detecting security threats in a computing device receives a first set of signals from components of the computing device. The first set of signals includes intercommunication electrical signals between the components of the computing device and electromagnetic radiation signals propagated from the components of the computing device. The system extracts baseline features from the first set of signals. The baseline features represent a unique electrical signature of the computing device. The system extracts test features from a second set of signals received from the component of the system. The system determines whether there is a deviation between the test features and baseline features. If the system detects the deviation, the system determines that the computing device is associated with a particular anomaly that makes the computing device vulnerable to unauthorized access.

Abstract: Systems, computer program products, and methods are described herein for dynamically orchestrating API calls using quantum computing. The present invention may be configured to receive API calls from clients, determine whether each API call is acceptable using a first machine learning model, and determine whether a subset or multiple subsets of the acceptable API calls are combinable using a second machine learning model to obtain API call categories. The present invention may be configured to determine whether to route each API call category to a cache or to an endpoint using a third machine learning model and obtain data from the endpoint that includes responses of the endpoint. In some embodiments, the first machine learning model, the second machine learning model, and the third machine learning model may be trained using one or more quantum computing engines.