Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for forecasting cloud service capacity and demand are presented. A cloud capacity forecast comprising a plurality of projections for a future timeframe may be generated via application of a predictive simulation model to one or more cloud service supply variables. A cloud demand forecast comprising a plurality of projections for the future timeframe may be generated via application of a predictive simulation model to a plurality of cloud service demand variables. A determination may be made as to a number and/or percentage of the projections for which the cloud capacity exceeds the cloud demand A confidence score that cloud service demand can be met may be calculated. In some examples, one or more prophylactic actions may be automatically performed if the confidence score is below a threshold value.

Abstract: An “Engagement Predictor” provides various techniques for predicting whether things and concepts (i.e., “nuggets”) in content will be engaging or interesting to a user in arbitrary content being consumed by the user. More specifically, the Engagement Predictor provides a notion of interestingness, i.e., an interestingness score, of a nugget on a page that is grounded in observable behavior during content consumption. This interestingness score is determined by evaluating arbitrary documents using a learned transition model. Training of the transition model combines web browsing log data and latent semantic features in training data (i.e., source and destination documents) automatically derived by a Joint Topic Transition (JTT) Model. The interestingness scores are then used for highlighting one or more nuggets, inserting one or more hyperlinks relating to one or more nuggets, importing content relating to one or more nuggets, predicting user clicks, etc.

Abstract: A method is disclosed for facilitating electronic communication between an external contact and an internal point person and between the internal point person and one or more internal team members. An internal collaboration network is created based on a first e-mail received from or sent to the external contact. The membership of the internal collaboration network is based on recipient information from the first e-mail and includes the internal point person but excludes the external contact. A message from the internal point person is automatically sent to the members of the internal collaboration network for display in a private collaboration view of an app on a computing device. Later-sent e-mails from the external contact that have subjects matching the subject of the first e-mail are automatically sent to the membership for display in a customer view of the app.

Abstract: A method is disclosed for facilitating electronic communication between an external contact and an internal point person and between the internal point person and one or more internal team members. An internal collaboration network is created based on a first e-mail received from or sent to the external contact. The membership of the internal collaboration network is based on recipient information from the first e-mail and includes the internal point person but excludes the external contact. A message from the internal point person is automatically sent to the members of the internal collaboration network for display in a private collaboration view of an app on a computing device. Later-sent e-mails from the external contact that have subjects matching the subject of the first e-mail are automatically sent to the membership for display in a customer view of the app.

Abstract: An “Engagement Predictor” provides various techniques for predicting whether things and concepts (i.e., “nuggets”) in content will be engaging or interesting to a user in arbitrary content being consumed by the user. More specifically, the Engagement Predictor provides a notion of interestingness, i.e., an interestingness score, of a nugget on a page that is grounded in observable behavior during content consumption. This interestingness score is determined by evaluating arbitrary documents using a learned transition model. Training of the transition model combines web browsing log data and latent semantic features in training data (i.e., source and destination documents) automatically derived by a Joint Topic Transition (JTT) Model. The interestingness scores are then used for highlighting one or more nuggets, inserting one or more hyperlinks relating to one or more nuggets, importing content relating to one or more nuggets, predicting user clicks, etc.