Abstract: According to one embodiment, a method, computer system, and computer program product for utilizing a pricing engine is provided. The present invention may include analyzing pricing factors of one or more products in an online listing; recommending a best price proposal to list the one or more products based on the analyzed pricing factors; analyzing buyer factors of a potential buyer; and recalculating the best price proposal based on the analyzed buyer factors.

Abstract: Computer technology that delivers simulation of crop growth, with the simulation accounting for various “influencing factors.” Artificial intelligence type software makes various recommendations to the farmers growing the agricultural crops. Some embodiments are directed to forestry and/or livestock instead of crops. Some embodiments include simulation of damaging natural forces within computer simulations of forestry and/or agriculture.

Abstract: Dynamic logging includes generating parsed event data by at least one natural language processor responsive to event data of a log for transactions of a target application. In response to the parsed event data, a first classifier classifies context states of the respective transactions of the target application. In response, a second classifier classifies trouble prone states of the respective transactions, wherein the trouble prone states are for respective hierarchical levels. When a logic module determines, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a higher trouble prone level than for a transaction immediately preceding the current transaction, the logic module sends an increased log detail selection to the target application, so that a greater amount of log detail is logged for at least a next transaction after the current transaction.

Abstract: Dynamic logging includes generating parsed event data by at least one natural language processor responsive to event data of a log for transactions of a target application. In response to the parsed event data, a first classifier classifies context states of the respective transactions of the target application. In response, a second classifier classifies trouble prone states of the respective transactions, wherein the trouble prone states are for respective hierarchical levels. When a logic module determines, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a higher trouble prone level than for a transaction immediately preceding the current transaction, the logic module sends an increased log detail selection to the target application, so that a greater amount of log detail is logged for at least a next transaction after the current transaction.

Abstract: Disclosed are techniques for classifying and displaying electronic data sets relevant to a collaboration context using head mounted displays. Context information corresponding to a collaboration event is leveraged to identify participants for the collaboration event, where natural language processing is applied across the contents of electronic data sets presently available to the participants across their computer devices and network-based storage environments to collect electronic data sets relevant to the collaboration event. The collected data sets are then classified, from the perspective of individual participants, into classes including previously known and previously unknown data sets. The collected data sets are then displayed based on their classes to the participants wearing head mounted displays such that each participant views the same overall set of data sets through their head mounted display, but the data sets displayed within each class may differ between them.

Abstract: Disclosed are techniques for classifying and displaying electronic data sets relevant to a collaboration context using head mounted displays. Context information corresponding to a collaboration event is leveraged to identify participants for the collaboration event, where natural language processing is applied across the contents of electronic data sets presently available to the participants across their computer devices and network-based storage environments to collect electronic data sets relevant to the collaboration event. The collected data sets are then classified, from the perspective of individual participants, into classes including previously known and previously unknown data sets. The collected data sets are then displayed based on their classes to the participants wearing head mounted displays such that each participant views the same overall set of data sets through their head mounted display, but the data sets displayed within each class may differ between them.