Abstract: One or more embodiments describe techniques for proactively connecting members of an organization together based on detected interest in a particular topic. The system analyzes a profile of a member to detect a particular topic associated with the member, and based on evaluating a set of interactions that another member of the organization had regarding the particular topic, generates an overall connection score for rating a connection between the second member and the particular topic. Responsive to determining that the overall connection score meets a threshold value, the system transmits a communication to generate a connection between the two members, and any other members whose overall connection scores meet the threshold value, for initiating collaboration on the particular topic amongst the various connected members.

Abstract: One or more embodiments describe techniques for proactively connecting members of an organization together based on detected interest in a particular topic. The system analyzes a profile of a member to detect a particular topic associated with the member, and based on evaluating a set of interactions that another member of the organization had regarding the particular topic, generates an overall connection score for rating a connection between the second member and the particular topic. Responsive to determining that the overall connection score meets a threshold value, the system transmits a communication to generate a connection between the two members, and any other members whose overall connection scores meet the threshold value, for initiating collaboration on the particular topic amongst the various connected members.

Abstract: Techniques are disclosed for using a trained machine learning model to generate a career progression pathways that are evaluated in view of employment conditions and compromises (trade-offs) that are acceptable to an employee. The system trains the machine learning model using employee profiles. The employee profiles include employment histories, skills, credentials, and professional activities. Once trained, the system applies the machine learning model to an employee's profile to generate ML-based career progression paths for reach a target employment goal. Each ML-based career progression path defines one or more interim objectives for reaching the target employment goal. The system compares the interim objectives, as defined by the ML-based career progression paths, with new employment conditions that are acceptable to an employee. The system recommends a subset of the ML-based career progression path(s) with interim objectives that are compatible with the acceptable employment conditions.

Abstract: One or more embodiments describe techniques for proactively connecting members of an organization together based on detected interest in a particular topic. The system analyzes a profile of a member to detect a particular topic associated with the member, and based on evaluating a set of interactions that another member of the organization had regarding the particular topic, generates an overall connection score for rating a connection between the second member and the particular topic. Responsive to determining that the overall connection score meets a threshold value, the system transmits a communication to generate a connection between the two members, and any other members whose overall connection scores meet the threshold value, for initiating collaboration on the particular topic amongst the various connected members.

Abstract: A mobile application enables employees to manage their goals and track their performance at any time, and from any place. A mobile device such as a tablet computer or a smart phone can download goal information over the Internet from a remote server. The mobile device can provide user interfaces through which its user can work on the goal. The mobile device can upload the result of the work performed on the mobile device to the remote server. The mobile application also enables an employee to augment his or her goal and performance data using data from third-party systems. The data from the third party systems can be incorporated into the goal and performance data as evidence of the employee progressing toward his goals. For example, such data from a third-party system might include tweets from a Twitter feed, or stock prices from a NASDAQ ticker feed.

Abstract: A mobile application enables employees to manage their goals and track their performance at any time, and from any place. A mobile device such as a tablet computer or a smart phone can download goal information over the Internet from a remote server. The mobile device can provide user interfaces through which its user can work on the goal. The mobile device can upload the result of the work performed on the mobile device to the remote server. The mobile application also enables an employee to augment his or her goal and performance data using data from third-party systems. The data from the third party systems can be incorporated into the goal and performance data as evidence of the employee progressing toward his goals. For example, such data from a third-party system might include tweets from a Twitter feed, or stock prices from a NASDAQ ticker feed.