International Business Machines Corporation

Tushar Agrawal

Sarbajit Kumar Rakshit

Jeremy R Fox

Atul Mene

Clement Decrop

Martin G Keen

Fang Lu

Craig M Trim

Thomas Jefferson Sandridge

Robert A Balfe

Victoria O Odeyemi

Vivek Verma

Serge Baduk

Sridhar Satuloori

Jason A Smith

Albert T Y Wong

Raghuveer Prasad Nagar

Jennifer M Hatfield

Zachary A Silverstein

Christian Compton

Michael Boone

Reinaldo T Katahira

Albert T Wong

Su Liu

Trudy L Hewitt

Jana Helton Jenkins

Sarvesh S Patel

Kushal S Patel

Vinod A Valecha

Grant Douglas Miller

Uri Kartoun

Jagadesh Ramaswamy Hulugundi

Jill S Dhillon

Alan Chung

A computer-implemented method, a computer system and a computer program product automatically generate product videos based on requested feature sets in stored data. the method includes acquiring a plurality of product features from a server, where each product feature in the plurality of product features is associated with data selected from a group consisting of: text data, audio data, and image data. The method also includes receiving a request for a product video, where a product in the product video comprises a set of product features in the plurality of product features. The method further includes identifying the data associated with each product feature in the set of product features for the request. Lastly, the method includes generating the product video based on identified data for the product.

Generation of product videos using machine learning

A processor may receive an object dataset based on an object. The processor may generate an interactive model of the object to display to a user using the object dataset. The processor may receive an interaction dataset associated with the user interacting with the interactive model. The processor may analyze the interactive data to identify one or more modifications to the one or more parameters of the object, based on the user interacting with the interactive model.

Object modification in a smart environment

A computer-implemented method for displaying an augmented reality (AR) object is disclosed. The computer-implemented method includes identifying a physical object of interest in a user's AR field of view (FOV), determining an amount of time required for the physical object of interest to be displayed within the user's AR FOV, based on the user's relative position and movement with respect to the physical object of interest, determining the physical object of interest is no longer in the user's physical FOV prior to an expiration of the determined amount of time required for the physical object to be displayed within the user's AR FOV, and displaying a digital object corresponding to the physical object of interest in the users AR FOV until the determined amount of time required for the physical object to be displayed within the user's AR FOV has expired.

Velocity based dynamic augmented reality object adjustment

Mitigating GPS inaccuracy is provided. The method comprises identifying a required level of location accuracy for an activity of an autonomous system and determining, according to GPS coordinates, a location of the autonomous system. A determination is made, according to a knowledge base created by historical learning, whether GPS location accuracy at the location of meets the required level of location accuracy for the activity. Responsive to a determination that the GPS location accuracy at the location of the autonomous system does not meet the required level of accuracy, location determination is handed off to an installed infrastructure within proximity of the autonomous system which provides the required level of location accuracy. The autonomous system then performs the activity according to location information provided by the installed infrastructure.

Proactive mitigation of GPS accuracy errors

A system for layer-by-layer three-dimensional (3D) printing of a 3D printed object. The system includes a printing component configured to print a plurality of layers to form the 3D printed object, a surface modification component configured to be co-operable with the printing component and configured to apply an appropriate degree of surface roughness to at least one of the plurality of layers after being printed, and a controller configured to identify parameters of each of the at least one of the plurality of layers and configured to control operation of the printing component and the surface modification component, wherein the parameters include the appropriate degree of surface roughness.

Method and system of three-dimensional printing to prepare surface for gripping of subsequent layers

Described are techniques for appropriately gripping objects by robots. A target object (e.g., circuit board) is identified to be gripped by a robotic arm of a robot, such as a robot in an industrial facility. Upon identifying the target object to be gripped by the robotic arm, the attributes of the target object are determined. One or more pores of the robotic arm may then be selected to be enabled to secrete an adhesive based on the attributes of the target object. Furthermore, the adhesion level corresponding to the amount of the adhesive to be secreted by the selected pores of the robotic arm is determined based on the attributes of the target object. The selected one or more pores of the robotic arm are then activated to secrete the adhesive according to the adhesion level in order to appropriately grip the target object by the robotic arm.

Appropriately gripping objects by robots

A processor may receive first object data associated with a first object to be transported. The processor may receive vehicle data associated with one or more potential vehicles for transportation of the first object. The processor may receive context data associated with a context for the transportation of the first object. The processor may simulate the transportation of the first object utilizing each of the one or more potential vehicles using digital twin simulation. The processor may select a first vehicle of the one or more potential vehicles based on an optimization of an optimization factor associated with an outcome of the digital twin simulation.

Digital twin simulation for transportation

Managing a package delivery system deploying an unmanned vehicle including an inflatable unit for reducing package vibration in a transportation vehicle. Package data is received at a computer, and the package data includes package descriptions. Spatial positioning of the packages in the transport space is tracked to determine, spatial positioning changes between the packages in the transport space based on the package data received at the computer and the transport. One or more unmanned vehicles are delivered to the transport space based on the spatial positioning changes in the transport space, and the unmanned vehicles including inflatable units. The inflatable units are deployed in the transport space by inflating the inflatable units at locations in the transport space based on the spatial positioning changes to discourage package movement in the transport space.

Reducing package vibration during transportation by initiating mobile vehicles based on computer analysis

A digital model is generated of a debris location where the digital model includes an augmented reality overlay of the debris location. Digital data from sensors at a debris field is received at a computer, and the digital data depicts debris in the debris field. Materials in the debris field are detected and identified based on the detected materials in the debris field. Layers of materials in the debris field are identified. A digital model is generated of the materials in the debris field including in the layers. Using the computer and an augmented reality (AR) device, an augmented reality overlay is generated based on the digital model of the materials in the debris fields. The augmented reality overlay is superimposed over the digital model of the materials in the debris field to indicate the materials in the debris field and the materials in the layers of the debris field.

Augmented reality overlay of digital model of debris location to detect environmental risks

According to one embodiment, a method, computer system, and computer program product for selectively recording meetings is provided. The embodiment may include initiating a digital meeting. The embodiment may also include determining two or more rule sets, including one or more local rule sets, for selectively recording the digital meeting, wherein each rule set includes one or more rules. The embodiment may further include recording one or more recordings of the digital meeting according to each local rule set.

Selective recording of meeting content

An embodiment for engaging with an electronic map for an effective vehicle-to-infrastructure (V2I) network is provided. The embodiment may include receiving real-time and historical data from one or more IoT devices and an electronic map of one or more roadways. The embodiment may also include identifying one or more characteristics associated with the one or more roadways. The embodiment may further include identifying one or more driving conditions of each roadway in the electronic map and capabilities of one or more vehicles. The embodiment may also include executing a digital twin simulation of a digital twin model of each vehicle driving along the one or more roadways. The embodiment may further include in response to determining at least one vehicle is unable to make a driving decision, recommending a relative location of one or more infrastructure devices on the one or more roadways.

Electronic map engagement for effective vehicle-to-infrastructure network

In an approach to improve the management of operating vehicles through smart contracts, embodiments generate a priority score based on received sensor feeds from a vehicle, co-located vehicles, and a smart contract rule. Further, embodiments assign the priority score to the vehicle and the co-located vehicles and the co-located vehicles and identify that the assigned priority score of the vehicle is higher than the co-located vehicles. Additionally, embodiments manage an operation of the co-located vehicles through a computing device within the co-located vehicles based on the smart contract rule and the priority score and create a priority lane for the vehicle by repositioning the co-located vehicles.

Computer analysis for assisting the operations of vehicles

Aspects of the present disclosure relate to collaborative rulesets for vehicles. A collaborative ruleset for a set of vehicles can be received, the collaborative ruleset defining relative positions each vehicle is required to maintain with respect to other vehicles within the set of vehicles. Sensor data indicating current positions of each vehicle within the set of vehicles while the set of vehicles are operating can be received. A command to display, on an augmented reality (AR) device of a user of a first vehicle within the set of vehicles, AR content for complying with the collaborative ruleset can be issued.

Collaborative ruleset management for vehicles

An embodiment for augmented reality (AR)-based visualization of an activity with collaboration amelioration is provided. The embodiment may include receiving real-time and historical data relating to an activity. The embodiment may also include identifying each step of the activity and a time to complete each step. The embodiment may further include executing a plurality of digital twin simulations of avatars of different types of workers performing each step of the activity. The embodiment may also include in response to determining a current distribution of human and robotic workers is not optimized, prompting a manager with an AR overlaid slider bar. The embodiment may further include generating a knowledge corpus including an optimized distribution. The embodiment may also include creating a visual animation of the optimized distribution. The embodiment may further include displaying the visual animation to at least one human worker via an AR device.

AR-based visualization of activity with collaboration amelioration

In an approach to improve risk assessment and management, embodiments monitor a predetermined area by collecting, by one or more sensing devices, data related to environmental conditions and spatial relationships between one or more objects in the predetermined area. Further, embodiments determine, based on a model and the monitoring, that an accidental event is likely to occur within a predetermined distance or within the predetermined area. Additionally, embodiments identify a predetermined mitigation action from a knowledge corpus to apply to the accidental event; and proactively implementing the identified mitigation action to prevent or manage the accidental event.

Growing community of inventors

West Fargo, ND, United States of America

Tushar Agrawal