International Business Machines Corporation

Kyndryl, Inc.

Sri Harsha Varada

Venkata Vara Prasad Karri

Sarbajit Kumar Rakshit

Pavan Kumar Penugonda

Saraswathi Sailaja Perumalla

Hemant Kumar Sivaswamy

Akash U Dhoot

Manjit Singh Sodhi

Subha Kiran Patnaikuni

Prerna Agarwal

Poorna Vadada

Manish Anand Bhide

Shanthan Chamala

Nitika Sharma

Sampath Kumar Pulupula Venkata

Venkata Ratnam Alubelli

Rachana Vishwanathula

Pavan K Manda

Sunita Rani Nayak

Lakshmi Soumya Suggala

Thomas Chang

Mahendra Asapu

Nagaraju Ampavalli

Pradeep Venkata Sudindra Pusuluri

Shridhar Gurunath Domanal

Avadhanula Kishore

Srikanth Ganivada

Sri Srikanth Magapu

Tirumala Vasu Padisetti

Ramesh Naidu Yamalapalli

Kondala Rao Radha Ravi Shankara Mantripragada

Karthik Adatrow

Hari Krishna Prasad Bheemavarapu

Sravani Madem

According to one embodiment, a method, computer system, and computer program product for preventing malware propagation. The embodiment may include identifying a malware scan report of an Internet-of-Things (IoT) device within an automated material handling (AMH) environment. The embodiment may include determining whether the IoT device is malware affected. In response to determining that the IoT device is malware affected, the embodiment may include disabling the IoT device. The embodiment may include instructing an industrial robot to transport the IoT device to a destination location within the AMH environment.

Secure material movement to prevent malware propagation

Provided are techniques for enforcing security rules along a trajectory of data movement. Confidentiality levels of source data and of processed data at different points of an existing trajectory path in an application landscape are identified by analyzing source metadata of the source data and processed metadata of the processed data. One or more trajectory paths that meet security levels that correspond to the confidentiality levels are identified by analyzing microservice generated logs and an application landscape description with reference to the confidentiality levels of the source data and the processed data. A trajectory path is selected from the one or more trajectory paths. A workflow is generated to move the source data and the processed data through the selected trajectory path, and the workflow is executed to move the source data and the processed data through microservices of the selected trajectory path while enforcing the security levels.

Enforcing security rules along a trajectory of data movement

VR enabled IoT device resolution methods are described for receiving user request data (URD) corresponding to a user request comprising a natural language utterance (NLU) to resolve a problem with IoT devices in a user IoT environment from a first user device (UD), determining a problem identifier based on the NLU, determining access restrictions based on the URD, identifying resolution personnel based on the problem identifier, transmitting resolution personnel data comprising a prompt to receive user authorization to access the user IoT environment to the first UD, receiving user authorization data; establishing a remote access session (RAS) between the first UD and a second UD granting the resolution personnel access to the user IoT environment, transmitting rendering instructions to the second UD causing the second UD to render a VR environment in a second UD user interface based on the user IoT environment, the IoT devices, and the access restrictions.

Virtual reality enabled internet-of-things device resolution

Provided are techniques for performing edge processing by selecting edge devices based on security levels. A security policy is identified for a job to determine a security level for the data. A number of the edge devices that are to be included in a participating group to execute the job is identified. A subset of edge devices that meet the security level are identified by comparing security data of each of the edge devices with the security level. In response to determining that the subset of edge devices includes that number of edge devices, the edge devices from the subset are added to the participating group. The job is executed using the edge devices in the participating group and the one or more cloud nodes, while maintaining the security level in processing the data and in communications across the edge devices and the one or more cloud nodes.

Perform edge processing by selecting edge devices based on security levels

Computer technology for: (i) determining which version of a software service to use depending upon whether the user requesting the service is a beta tester, and (ii) if the user status of the user requesting a software service is beta tester then: (a) that user will have the newer, testing-phase version of the software service used to fulfill the software service request, and (b) the user will be given an opportunity to provide feedback on the effectiveness and/or performance of the service to effect the beta testing of the new version of the software service under test.

Labelling web traffic routing to support software testing

Provided are computer program product, system, and method for using a machine learning module to determine a group of execution paths of program code and a computational resource allocation to use to execute the group of execution paths. Information on activity steps in program code and a system load of a system in which the program code is executed are provided as inputs to a resource allocation machine learning module. The resource allocation machine learning module processes the provided inputs to output computational resource allocations for execution paths of activity steps in the program code to execute in parallel, including memory and processing resource allocations optimized according to an optimization criteria. The outputted computational resource allocations are allocated to execute the activity steps in the execution paths in parallel.

Using a machine learning module to determine a group of execution paths of program code and a computational resource allocation to use to execute the group of execution paths

A processor may receive first sensor data associated with detected movement of a user in an environment. The processor may determine, using an AI model, based on the first sensor data, that the movement of the user is associated with a first object. The processor may retrieve sensory data associated with the first object from a repository. In some embodiments, the sensory data may include at least one of auditory, olfactory, and haptic data associated with historical user interactions with the first object. The processor may provide a first sensory signal to the user to indicate a location of the first object relative to a first location of the user.

Navigation assistance device

A method, a computer program product, and a computer system position a plurality of sensors in an area. The method includes receiving data from the sensors positioned at respective first locations where the data is indicative of weather and environmental parameters. The method includes determining weather conditions and an environmental condition based on the weather and environmental parameters. The method includes determining an impact to the environmental condition by the weather conditions. The method includes determining a second location for the sensors based on the impact to the environmental condition. The sensors are configured to generate further environmental parameters with an increased granular level at the second location. The method includes transmitting instructions to a delivery robot to move the sensors to the second location.

Context aware dynamic positioning of sensors

According to one embodiment, a method, computer system, and computer program product for cloud service brokerage. The embodiment may include receiving a data set and user defined contextual parameters relating to a machine learning (ML) problem of a user to be performed on the data set. The embodiment may include identifying a resource requirement of the ML problem and available resources. The embodiment may include enabling user configuration of the contextual parameters in an interactive augmented reality (AR) view. The embodiment may include identifying a set of clusters upon which to execute computing tasks of the ML problem. The set of clusters is identified out of the available resources. The embodiment may include implementing a ML evaluation process to determine an optimized load distribution model for execution of the computing tasks within the set of clusters. The embodiment may include implementing the optimized load distribution model.

Interactive augmented reality based optimization of machine learning model execution on hybrid cloud

The present specification describes a computer-implemented method. According to the method, an object within a range of vision of a user is identified via a capture device on a wearable augmented reality (AR) system worn by the user. A user interaction with an identified object within the range of vision of the user is monitored. A notification related to the identified object is generated and presented on a display device of the wearable AR system.

Augmented reality object interaction and notification

The exemplary embodiments disclose a method, a computer program product, and a computer system for managing a consumption of food. The exemplary embodiments may include collecting data of a user and of one or more food items, extracting one or more features from the collected data, and determining a food consumption recommendation based on the extracted one or more features and one or more models.

Smart refrigerator for smart consumption of food

Aspects of the present invention disclose a method for configuring security settings of a defined area that includes internet of things (IoT) devices for contextual situations utilizing virtual reality (VR) and gamification. The method includes one or more processors identifying an IoT device within a defined area. The method further includes collecting data of the IoT device corresponding to the defined area. The method further includes generating a first VR interactive scene in a VR environment corresponding to physical attributes of the defined area, wherein the VR interactive scene is based on a set of conditions of a knowledge corpus corresponding to the collected data of the IoT device. The method further includes identifying a security threat in the VR environment. The method further includes configuring a security rule of the defined area that modifies functions of the IoT device based on the set of conditions.

Virtual reality gamification-based security need simulation and configuration in any smart surrounding

A computer-implemented method for automatically pairing two devices is disclosed. The computer-implemented method includes detecting a first type of movement from a first device. The computer-implemented method further includes determining that the first device is located within a threshold distance of a second device when the first type of movement from the first device is detected. The computer-implemented method further includes determining whether an auto-pairing policy matches the first type of movement from the first device and the threshold distance between the first and second devices. The computer-implemented method further includes, responsive to determining that the auto-pairing policy matches the first type of movement from the first device and the threshold distance between the first and second devices, automatically pairing the first device and the second device.

Movement pattern based auto-pairing

According to one embodiment, a method, computer system, and computer program product for using mobile devices for anomaly detection in a vehicle. The present invention may include a computer receives sensor data from at least one mobile device associated with the vehicle, where the mobile device having one or more sensors. The computer analyzes data from the one or more sensors to identify an anomaly associated with the vehicle. The computer identifies a message associated with the anomaly. The computer determines an urgency value of the message based on the anomaly. The computer transfers the message with the urgency value to the vehicle and causes the vehicle to notify the message using a vehicle notification device.

Anomaly detection for vehicle in motion using external views by established network and cascading techniques

A parallel presentation system includes computer hardware including a parallel presentation platform (PPP) connected to an extended reality (XR) system associated with a user. The computer hardware is configured to perform the following operations. A lookup point associated with the user is identified by the PPP. The PPP determines that the user has shifted focus from a first data stream to a second data stream. A shift time associated with the shift along with an identification of the first data stream is stored by the PPP in association with the user. A summarization of the first data stream is generated using a summarization engine of the PPP and the summarization is based upon the shift time and the lookup point. The summarization is forwarded by the PPP to the XR system. Information regarding the summarization is configured to be parallel-presented by the XR system associated with the user.

Growing community of inventors

Vizianagaram, India

Sri Harsha Varada