Abstract: Apparatus, systems and methods for providing a four-part, virtual reality (VR) customer service interaction between a customer service avatar operating on a VR platform and a customer using both a mobile device and a VR device are provided. Methods may include activating a VR session between the customer service avatar and the customer. Methods may include validating the VR session between the customer service avatar and the customer. Methods may include processing a co-browsing VR session request. Methods may include initiating the VR session between the customer service avatar and the customer.

Abstract: Apparatus, systems and methods for providing a four-part, virtual reality (VR) customer service interaction between a customer service avatar operating on a VR platform and a customer using both a mobile device and a VR device are provided. Methods may include activating a VR session between the customer service avatar and the customer. Methods may include validating the VR session between the customer service avatar and the customer. Methods may include processing a co-browsing VR session request. Methods may include initiating the VR session between the customer service avatar and the customer.

Abstract: A system includes a memory and a processor. The memory stores a baseline record associated with a device, which includes a set of parameters that were extracted from the device. The processor receives a request from the device to initiate a communication. In response to receiving the request to initiate the communication, the processor determines that an update to the device has modified the set of parameters of the device. In response to determining that that the update has modified the set of parameters, the processor extracts the modified set of parameters from the device, and stores a new record associated with the device in the memory, which includes the modified set of parameters extracted from the device. The processor additionally uses the new record to validate the request. In response to validating the request, the processor transmits the communication.

Abstract: A system includes a memory, and a processor. The processor receives a request to register a device. In response, the processor extracts a set of parameters from the device, and stores a baseline record associated with the device, which includes the set of parameters, in the memory. The processor determines that an update to the device modified the set of parameters. In response, the processor extracts the modified set of parameters from the device, and stores a new record associated with the device, which includes the modified set of parameters, in the memory. The baseline record was used to validate an identity of the device prior to the processor determining that the update modified the set of parameters. The new record is used to validate the identity of the device subsequent to the processor determining that the update modified the set of parameters.

Abstract: A system includes a memory and a processor. The memory stores a plurality of records associated with a device and a machine learning algorithm. The plurality of records includes a first record and a second record. The first record includes a first set of parameters extracted from the device at a first time. The second record includes a second set of parameters extracted from the device at a second time, with a difference between the first set of parameters and the second set of parameters resulting from of an update to the device. The machine learning algorithm is configured to determine a probability that the update was unauthorized. The processor executes the machine learning algorithm, and determines that the probability is greater than a threshold. In response to determining that the probability is greater than the threshold, the processor rejects a request transmitted by the device.

Abstract: A system includes a production computing environment including a plurality of components, a centralized data repository that receives and stores data feeds relating to one or more components as a data log and at least one processor configured to obtain the data log of each component, generate a current state vector for the component based on the data log, compare the current state vector to a normal state vector of the component, determine that the current state vector deviates from the normal state vector, and in response, predict an anomaly associated with the component using an iterative machine learning method. The at least one processor may be configured to correct the predicted anomaly by taking at least one pre-configured action corresponding to the predicted anomaly.

Abstract: A system includes a production computing environment including a plurality of components, a centralized data repository that receives and stores data feeds relating to one or more components as a data log, a plurality of cloud infrastructures and at least one processor configured to receive the data feed relating to the components, predict an anomaly associated with a component based on the data feed relating to the component, identify a system configuration needed to run a current workload associated with the component, search each cloud infrastructure for a cloud instance that can support the identified system configuration, identify a cloud instance based on the search, initiate the identified cloud instance and switch the workload from an original system to the initiated cloud instance.

Abstract: Aspects of the disclosure relate to computing hardware and software for ML model selection and AR. A computing platform may select a ML model. The computing platform may send, to an AR client device, an AR representation of the ML model that illustrates operation of the ML model. The computing platform may receive, from the AR client device, consent information indicating whether or not consent is provided to apply the ML model. Once consent is received, the computing platform may: 1) write, to a distributed ledger, the consent information and a identifier of the ML model, 2) apply the ML model to produce a ML output customized based on a user of the AR client device, and 3) send, to the AR client device, commands directing the AR client device to display the ML output, which may cause the AR client device to display the ML output.

Abstract: Systems, computer program products, and methods are described herein for implementing federated containerization platform using LiFi (Light Fidelity). The present invention is configured to electronically receive, from a container orchestration engine, a request to allocate one or more resources to one or more applications to execute a first task; retrieve one or more resource requirements associated with the one or more applications; determine, using a machine learning model, one or more resources to be allocated to the one or more applications; retrieve, from a resource repository, the one or more resources to be allocated to the one or more applications; and allocate the one or more retrieved resources to the one or more applications.

Abstract: Systems, methods, and apparatus for smart glasses based configuration of programming code are provided. A smart glasses device may capture a sequence of user eye positions using one or more sensors. A user interface may display one or more selectable programming functions. Each programming function may be pre-associated with a segment of programming code implementing the function. The user eye positions captured by the smart glasses may be filtered based on the degree of visual focus. A calibration engine may map each user eye position to one of the selectable programming functions displayed on the user interface. The selected programming functions may be ordered based on the sequence of user eye positions. A software application may be configured by assembling the segments of programming code associated with each programming function.

Abstract: Systems, computer program products, and methods are described herein for enhanced data security in a virtual reality environment. The present invention is configured to initiate an adversarial testing protocol on the virtual reality platform; transmit one or more adversarial inputs to one or more trained machine learning models associated with the virtual reality platform; receive one or more predicted class labels for the one or more adversarial inputs; compare the one or more predicted class labels with one or more observed class labels associated with the one or more adversarial inputs; determine an error rate of the one or more trained machine learning models; determine a first exposure score for the virtual reality platform based on at least the error rate; transmit control signals configured to cause the computing device associated with the user to display the first exposure score for the virtual reality platform.

Abstract: A system and method are provided for dynamically updating a program. An anomaly related to a User Interface (UI) screen of a software application is detected, wherein the program is configured to extract information from a first spatial position on the UI screen. The information includes at least one of text, a computer icon or an image. It is determined, based on the software application, that the anomaly is caused by the information being moved from the first spatial position to a second spatial position on the UI screen. In response, the program is modified to cause the program to extract the information from the second spatial position. The modified program is deployed to extract the information from the second spatial position.