International Business Machines Corporation

Venkata Vara Prasad Karri

Sarbajit Kumar Rakshit

Shailendra Moyal

Saraswathi Sailaja Perumalla

Akash U Dhoot

Sri Harsha Varada

Hemant Kumar Sivaswamy

Partho Ghosh

Kamal Kiran Trood Yamala

Shanthan Chamala

Sampath Kumar Pulupula Venkata

Shikhar Kwatra

Abhishek Jain

Girish Padmanabhan

Nitika Sharma

Abhijit Deshmukh

Gautam Zalpuri

Sowjanya Rao

Subha Kiran Patnaikuni

Ramprasad Bhat

Vamsi Vasireddy

Naga Srinivasa Reddy Padala

Amit Kumar Senapaty

Hariharan Krishna

Pavan Kumar Penugonda

Kiruthikalakshmi Periasamy

Divya Mantha

Krishna Reddy Venkata Batchu

Khader Saheb Shaik

Poorna Vadada

Sekhar Reddy Dandu Reddy

Ratnakumar Vadapalli

Madhukar Hari Kishan Gobbi

Chaitanya Korupolu

Ram Kumar Vadlamani

Chandra Sekhar Kalyan Devulapalli

Seema Nagar

Rajesh Patil

Aparna Seshadri

Sirisha Akella

Ravi T Vadlamani

Shilpa Bhagwatprasad Mittal

Narendra Reddy Tippala

Niteen D Lakhe

Lakshmi Soumya Suggala

Raghupatruni Nagesh

Vaibhav Telang

Neela Vauhini Komaragiri

Ravi Sankar Parvathina

Keerthi Samireddy

Pardeep Singh

Pavan K Manda

Rojan Jose

Madhu Akhilesham

Balatripurasundari Nookala

Sushil Bhaskar Ravi

Ramalingeswara Naidu Saragadam

Tirumala Vasu Padisetti

Maithree Challapalli

Pritam Chopdar

Sri Srikanth Magapu

Arpita Girish Gudihal

Suresh Nagulakonda

Avadhanula Kishore

Dasavatara Yadav Golagani

Ravindra Kumar Venkata Konatham

Venkata Suresh Babu Golla

Danana Sandhya

Rakesh Chinhara

Pradeep Venkata Sudindra Pusuluri

Nagaraju Ampavalli

Swetha Sukeerthi Chettupalli

Sheema Farheen

Mahendra Asapu

Annyasha Lahiri

Sairam Telukuntla

Sandeep Kaur

Swamy Subramanya

Manali Tanpure

Rajesh Gudikoti

Rajasekhar Chaganti

Srikanth Vankara

Sayyad Baji Shaik

Subir Ghosh

Ryan Jackson

Murali Pasala

Tanvi Tayal

Kamal Somani

Afroz Khan, I

Lalitha Komarapuri

Kondala Rao Radha Ravi Shankara Mantripragada

Ramesh Naidu Yamalapalli

Dilip Kumar Ronanki

Sudeep Brajabasi

Asha Velayudham

Lakshmi Kiran Nagireddi

Gayaz Shaik

Abhijeet Sange

Rajesh Prabhakar Dhagare

An approach for dynamically restoring light-based network communication (e.g., Li-Fi, etc.) between Li-Fi devices is disclosed. The approach includes identifying environmental situation such as location of Li-Fi devices, automaton robotics with moving parts, determining risk of obstacles by simulating (via machine learning) one or more scenarios that could potentially be created by moving parts. The approach selects the optimal scenario and based on the selected scenario, the approach executes the scenario. The executed action can include optimizing the light path of the Li-Fi devices by instructing moving parts to follow a new path or leverage a new light path created by secondary Li-Fi device and/or mirror assembly to ensure no interruption to the Li-Fi network.

Dynamically restoring Li-Fi communication

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

Allocating processing activities among multiple computing devices can include identifying multiple computing activities of a computer-executable process and, for each computing activity identified, estimating in real time the computing resources needed. The identifying can be in response to detecting a computer-executable instruction executed by one multiple communicatively coupled computing devices, and the computer-executable instruction can be associate with the computer-executable process. A current condition and configuration of each of the computing devices can be determined in real time. For each computing device an effect induced by executing one or more of the plurality of activities can be predicted, the predicting based each computing device's current condition and configuration and performed by a machine learning model trained using data collected from prior real-time processing of example process activities. Based on the predicting, computing activities can be allocated in real time among the computing devices.

Multi-device processing activity allocation

An augmented reality (AR) container orchestration system includes computer hardware including an AR system and a container orchestration platform. The AR system is configured to perform identifying a container to be deployed, displaying, to a user, a grid having a plurality of cells each of which are configured to receive a representation of the container, and redisplaying, using the AR system and to the user, the grid based upon a movement of the representation of the container to a particular position within the grid. The container orchestration platform is configured to perform identifying possible configurations for the container, and configuring the container based upon the movement of the representation of the container to the particular position. The includes a plurality of axes, and each axis of the plurality of axis represents a different configuration-related parameter of the configurations for the container.

Intelligent container configuring using augmented reality

Enhancing data-driven decision making can include generating a contextual insight data structure using a first artificial intelligence system operating on information data structures selected based on a context of a programmatically defined action. A perception data structure can be generated using a second artificial intelligence system that performs natural language processing of programmatically defined action communications. The perception data structure and the contextual insight data structure can be compared, and in response to detecting a structural variance, the perception data structure can be populated with one or more data items selected from the information data structures. A delta can be determined between the contextual insight data structure and the perception data structure based on a predetermined metric that measures a quantitative difference between the contextual insight object and the perception object. An electronic recommendation based on the delta can be generated and delivered to a user via a user device.

Data-driven decision enhancement

A computer hardware system associated with a hybrid cloud including a private cloud and a public cloud has a hardware processor configured to perform the following executable operations. A plurality of data sets of unprocessed data is randomly sampled to generate a plurality of random samples respectively associated with each of the plurality of data sets. The plurality of random samples is pre-processed, using a machine learning engine, to identify hidden correlations contained therein. The hidden correlations are evaluated against a confidentiality model to characterize each of the plurality of segments as to confidentiality. The data sets are segmented by assigning the data sets to one of the private cloud or the public cloud based upon the evaluating. Data sets assigned to the private cloud based upon the segmenting are processed using the private cloud.

Segmentation of data in hybrid cloud

Disclosed are techniques to determine navigation paths for mobile points of consumption of edge computing resources where the edge computing resources are at least partially hosted on satellite devices. Datasets corresponding to a set of edge computing satellites are received describing their positions, orbital paths, and edge computing resources. Further datasets are received corresponding to mobile points of consumption of edge computing resources. Using both datasets, predictions are determined corresponding to demand for edge computing resources of the edge computing satellites. When a new mobile point of consumption of edge computing resources queues up a transit route to a destination, that transit route and the accompanying requirement for edge computing resources is compared with the determined predictions of resource availability. Where sufficient edge computing resources are unavailable for a transit route, a new route is generated to transit through regions where sufficient edge computing resources are predicted to be available.

Transit routes with orbital edge computing resource availability for mobile edge computing consumption points

A system, program product, and method for automatic collection, generation, and presentation of real time information to vehicular occupants are presented. The method includes determining a first driver profile for a first driver of a first vehicle. The method also includes determining, subject to the first driver profile determination, a color assignment for the first driver. The color assignment is at least partially indicative of the driver profile and the color assignment is at least a portion of a representation of the first driver. The method further includes communicating the color assignment to one or more of one or more potential occupants of the first vehicle and one or more occupants of the first vehicle.

Automatic real time information to vehicular occupants

A processor may receive a first command and a second command from one or more users. A processor may analyze the first command and the second command. A processor may determine command information associated with the first command and the second command. A processor may display the command information to at least one of the one or more users on a display screen. A processor may receive a response from the at least one of the one or more users associated with the command information on the display screen. A processor may execute the first command and the second command based, at least in part, on the command information and the response.

Multiuser voice command visualization

The present disclosure includes digitizing user symptoms. A processor may receive user provided data from one or more data collection devices. A processor may access a historical database. The historical database may include historical user provided data. A processor may generate a user data packet based, at least in part on, the user provided data and the historical database. A processor may display the user data packet to one or more authorized user and one or more other correlating information.

Digitizing disease symptoms

A method for managing self-configuration of sensors based on geofencing and context awareness includes determining a position for each object from a plurality of objects within a location. The method also includes segmenting the plurality of objects based on a plurality of parameters, where each parameter provides context to each object and at least one sensor on each object with respect to the location. In response to determining a plurality of geofence areas for the segmentation of the plurality of objects, the method also includes determining a plurality of sensor configurations for the plurality of objects, wherein each sensor configuration is for the at least one sensor on each object. In response to determining an action is performable based on a first sensor configuration from the plurality of sensor configurations, the method also includes performing the first sensor configuration on a first object from the plurality of objects.

Geofencing and context based sensor self-configuration

One or more computer processors derive a base corpus by capturing time series haptic data from an artisan performing an artisanal skill comprising a plurality of artisanal actions, wherein each artisanal action in the plurality of artisanal actions is associated with a time interval. The one or more computer processors train a multi-agent reinforcement learning (MARL) model utilizing the derived base corpus, wherein the MARL model outputs a vector representing one or more predicted artisanal actions required to complete the artisanal skill. The one or more computer processors generate a training avatar utilizing a trained generated adversarial network fed with one or more user parameters identified from a user requesting guidance for the artisanal skill and the output vector of the trained MARL model. The one or more computer processors guide the user with the generated training avatar in an execution of the artisanal skill.

Artisan learning system

According to one embodiment, a method, computer system, and computer program product for contextual route guidance is provided. The embodiment may include receiving user information related to a current travel route. The embodiment may further include identifying current status information associated with a transportation hub through IoT-collected data. The embodiment may also include generating a navigation path for a user to reach a destination within the transportation hub based on the received user information and the identified current status information. The embodiment may further include presenting the navigation path to the user.

Creating a time bounded and goal driven network for passenger guidance

Methods and apparatus, including computer program products, implementing and using techniques for providing online media content. Online audiovisual media to be displayed to a user is provided to a client device. In response to detecting that the user is inattentive relative to the displayed audiovisual media, the provisioning of the audiovisual media and/or the display of the audiovisual media on the client device are automatically paused. In response to detecting that the user's attention has returned to the audiovisual media, a modified version of the audiovisual media is provided to the client device, to be displayed to the user.

System and methods for providing and consuming online media content

An approach for a computer program to retrieve historical incident data associated with a contaminated site to predict a future incident at the contaminated site. In the approach, the computer program evaluates the historical incident data to determine a deployment of a plurality of unmanned vehicles to form a virtual fence around the contaminated site. Furthermore, in the approach, the computer program determines instructions for the deployment of each of the plurality of unmanned vehicles to form the virtual fence around the contaminated site. In the approach, the computer program sends the instructions to form the virtual fence to each of the plurality of unmanned vehicles.

Virtual fencing of a contaminated area

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Visakhapatnam, India

Venkata Vara Prasad Karri