International Business Machines Corporation

Cristian Vecchiola

Timothy Michael Lynar

Ermyas Abebe

James R Kozloski

John Michael Wagner

Dileban Karunamoorthy

Kent C B Steer

Andrew Lawrence Frenkiel

Alessio Bonti

Peter Ilfrich

Brendan P Haesler

Automatically learning and providing software development team structure and methodologies. A software development knowledgebase repository is generated by mining for software processes data over a network of computer systems. A team structure specification and project requirement associated with a target project is received. A software development methodology is selected from the software development knowledgebase repository based on the team structure specification and project requirement associated with a target project, a team members knowledgebase, a teams knowledgebase, and a past projects knowledgebase. A machine learning module automatically learns a software development methodology to select. Based on the software development methodology, a software development environment infrastructure for the target project is built.

Agile team structure and processes recommendation

Contextualized selection of components in one aspect may include generating a developer profile associated with a team member, for example, for all team members designated to work on a computer-implemented development project, and generating a component profile associated with a software component, for example, for all software components identified based on received requirement specification associated with the project. Applicability of a software component to the team members may be computed based on developer profiles and a component profile associated with the software component. Based on the applicability, components are selected and may be downloaded to build a team project component repository.

Contextualized software component selection and repository generation

A computer-implemented method includes detecting a name occurring in a component of a media playing on a first user endpoint device. The component is contained within an electronic signal. An entity in the media that is associated with the name is identified, and stored data that is contextually related to the entity is retrieved. The data is then formatted as supplemental content for display to a user of the first user endpoint device.

Enhancing digital media with supplemental contextually relevant content

Automatic driver modeling is used to integrate human-controlled vehicles into an autonomous vehicle network. A driver of a human-controlled vehicle is identified based on behavior patterns of the driver measured by one or more sensors of an autonomous vehicle. A model of the driver is generated based on the behavior patterns of the driver measured by the one or more sensors of the autonomous vehicle. Previously stored behavior patterns of the driver are then retrieved from a database to augment the model of the driver. The model of the driver is then transmitted from the autonomous vehicle to nearby vehicles with autonomous interfaces.

Automatic driver modeling for integration of human-controlled vehicles into an autonomous vehicle network

A system configured to manage battery energy of a mobile device includes a primary mobile device and at least one peer device. The primary mobile device includes a power unit, a main communication module to electrically communicate with a peer device, and a peer assisted module in electrical communication with the power unit and the main communication module. The peer assisted module determines an energy level of the energy unit, and determines a task to be executed by the peer device. The peer device receives the task transmitted from the primary mobile device. The peer device further includes a peer process management module that executes at least one computation that completes the task, and communicates a completed task to the primary mobile device.

Growing community of inventors

Southbank, Australia

Cristian Vecchiola