International Business Machines Corporation

Akash U Dhoot

Shailendra Moyal

Venkata Vara Prasad Karri

Sarbajit Kumar Rakshit

Nitin S Jadhav

Nitika Sharma

Partho Ghosh

Saraswathi Sailaja Perumalla

Niteen D Lakhe

Shilpa Bhagwatprasad Mittal

Sri Harsha Varada

Shanthan Chamala

Abdullah Q Chougle

Gautam Zalpuri

Pavan Kumar Penugonda

Rojan Jose

Madhu Akhilesham

Hariharan Krishna

Bhavuk Srivastava

Venkata Ratnam Alubelli

Sowjanya Rao

Vamsi Vasireddy

Amit Kumar Senapaty

Keerthi Samireddy

Madhukar Hari Kishan Gobbi

Poorna Vadada

Khader Saheb Shaik

Pavan K Manda

Lakshmojee Rao Vulli

Manali Tanpure

Thomas Chang

Balatripurasundari Nookala

Arpita Girish Gudihal

Rajesh Prabhakar Dhagare

Divyesh Garg

Kamal Somani

Subir Ghosh

Brian M Arthur

Joseph Korting

Karan K Navani

Murali Pasala

Sayyad Baji Shaik

Gowri Pruthvi

Rajasekhar Chaganti

A method for parallel connection for device transactions includes stablishing a first connection to perform a transaction by a client device. The method also includes identifying a plurality of available connections supported by the client device to perform the transaction. The method also includes initializing the plurality of available connections to perform the transaction, wherein each available connection represents a parallel connection to the first connection. The method also includes identifying at least one potential connectivity issue with the first connection based on a comparison between historical network data for the first connection and current network data for the first connection. The method also includes performing a handoff of the transaction between the first connection and a second connection from the plurality of available connections.

Parallel connection for device transactions

Computer technology that receives a query and a desired level of precision and/or accuracy (herein, a 'p/a value') associated with the received query, determines whether the p/a value exceeds a predetermined threshold, if the p/a value exceeds the predetermined threshold, then the virtual assistant system applies digital twin simulation to a digital twin data set in determining the query response to improve precision and/or accuracy of the query response, and outputs the query response to the querying party (for example, output as sound data in a voice assistant system). If the p/a value is below the threshold value, then the digital twin simulation is not performed, which makes the response quicker and puts less computational power demands on the virtual assistant system. In some embodiments, the virtual assistant system calculates the p/a value based on context.

Computer technology for controlling digital twin simulation in voice assistant devices

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 approach for providing movement settings for a smart bed associated with an immobile patient is disclosed. The approach includes retrieving patient data and retrieving environmental data. The approach analyses patient profile and creates a digital twin of patient. In the digital twin environment, the approach models the patient movement and send patient movement setting based on desired outcome. Finally, the approach sends instructions to the smart patient bed.

AI based context aware multidirectional patient movement

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 a method for robotic assistance continuity to a pedestrian using gait recognition. The method includes determining a gait pattern of the pedestrian based on analysis of visual data obtained at a first position. The pedestrian is provided assistance based on a query at the first position. The pedestrian is identified at a subsequent position by identifying the gait pattern from analysis of visual data obtained at the subsequent position. The need for additional pedestrian assistance is determined at the subsequent position.

Robotic assistance continuity using pedestrian gait recognition

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

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

In an approach to security for displayed confidential holographic objects, one or more computer processors receive a request from a user to display a holographic object in association with a device. One or more computer processors identify content to be moved to the holographic object. One or more computer processors determine the content includes confidential information. One or more computer processors request authentication from the user. One or more computer processors receive the authentication from the user. One or more computer processors create a holographic boundary, where the holographic boundary blocks a view of the holographic object from one or more unauthorized users. One or more computer processors display the holographic boundary. One or more computer processors display the holographic object in association with the device.

Security for displayed confidential holographic objects

A critical path based proactive optimization that includes collecting data on the tasks of contextual situation for performing a process and training a twin computing simulation model using the collected data for each task in the process. A contextual situation simulation is run using the simulation models for each task in the process to determine a critical path that causes delay in the process. An optimized task is determined from the tasks of the contextual situation using machine learning employing the collected data, wherein the optimized task mitigates delay in the process from the critical path.

Scheduling project activities using twin computing simulation

An embodiment for controlling a boundary of an AI system based on an environmental context is provided. The embodiment may include receiving data input from one or more sources. The embodiment may also include identifying one or more contextual situations in a surrounding environment. The embodiment may further include identifying one or more input data collection boundaries and a span of control boundary for an AI-enabled device. The embodiment may also include connecting the AI-enabled device to one or more data generation modules within the one or more input data collection boundaries. The embodiment may further include in response to determining none of the one or more data generation modules are unable to connect, identifying an action to be performed by the AI-enabled device. The embodiment may also include executing the action in a first set of one or more machines within the span of control boundary.

Boundary control of an AI system based on environmental context

An embodiment for dynamically creating a vehicle fence is provided. The embodiment may include receiving GPS data from one or more vehicles and a real-time video feed of one or more intersections along a first roadway. The embodiment may also include identifying a number of autonomous vehicles and one or more routes of the autonomous vehicles traveling on the first roadway. The embodiment may further include in response to determining an emergency vehicle is traveling on the first roadway, identifying a route and speed of the emergency vehicle. The embodiment may also include estimating a width of a second roadway intersecting the first roadway at a nearest intersection. The embodiment may further include identifying a moving direction of one or more objects along the second roadway. The embodiment may also include deploying a required number of autonomous vehicles to block the one or more objects from entering the nearest intersection.

Dynamic creation of vehicle fence for emergency vehicle movement

A computer-implemented dynamic supporting base creation method that interacts with a three-dimensional (3D) printer that prints an object, the method including providing a physical support, via a first robotic gripper, for an object during three-dimensional (3D) printing using a printing head of the 3D printer and transferring the object to a second robotic gripper to provide a physical support at a different location on the object.

Multi-directional three-dimensional printing with a dynamic supporting base

A processor may receive object data associated with an object in an environment. The processor may generate one or more simulations associated with the object using the object data. The processor may analyze the one or more simulations to identify one or more potential issues associated with the object. The processor may deploy the one or more smart devices to the object to capture issue data associated the one or more potential issues.

Automatic issue identification and prevention

A computer-implemented method, system and computer program product for seamlessly adjusting the utilization of computational resources of a computing device. A pattern of usage of computational resources of the computing device over a timeframe is determined. An artificial intelligence model uses such a pattern of usage and/or any identified contextual needs for upgrading or downgrading the computational resources of the computing device to determine the predicted amount of computational resources needed to be utilized by the computing device. Based on such a predicted amount, a blockchain transaction may be initiated and recorded to upgrade/downgrade the computational resources of the computing device. The rules on a smart contract, which defines a subscription for utilizing an allocated portion of the computational resources by the user, associated with the computing device are then updated based on the blockchain transaction which adjusts the amount of computational resources available to be utilized by the user.

Upgrading or downgrading hardware by seamlessly adjusting usage of computational resources on computing device

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Pune, India

Akash U Dhoot