Abstract: Systems, methods, and other embodiments associated with a machine learning system that monitors and detects risk in electronic correspondence related to a construction project are described. In one embodiment, a method includes monitoring email communications over a network to identify an email; tokenizing text from the email into a plurality of words and initiating a machine learning classifier configured to identify construction terminology and to classify text with a risk as being litigious or non-litigious. The machine learning classifier processes the words from the email by at least corresponding the words to a set of defined litigious vocabulary and defined non-litigious vocabulary. The email is labeled as litigious or non-litigious. An electronic notice is generated and transmitted to a remote device in response to the email being labeled as being litigious to provide an alert in near-real time in relation to receiving the email over the network.

Abstract: Systems, methods, and other embodiments associated with anomaly detection are described. In one embodiment, a method monitoring an on-going project that comprises a plurality of processes and process activities that occur during the process. A machine learning model is applied that identifies a group of projects that are a similar type as the on-going and generates an expected level of process activities that are expected to occur. Based on a snap shot of the on-going project at a first time period, observed levels of process activities are determined that occurred in each process. The machine learning model compares for each of the processes, the observed levels of process activities to the expected levels of process activities in a corresponding time period. If the observed levels of process activities fail to fall within a range of the expected levels of process activities, an anomaly alert is generated and displayed.

Abstract: Systems, methods, and other embodiments associated with a machine learning system that monitors and detects risk in electronic correspondence related to a construction project are described. In one embodiment, a method includes monitoring email communications over a network to identify an email; tokenizing text from the email into a plurality of words and initiating a machine learning classifier configured to identify construction terminology and to classify text with a risk as being litigious or non-litigious. The machine learning classifier processes the words from the email by at least corresponding the words to a set of defined litigious vocabulary and defined non-litigious vocabulary. The email is labeled as litigious or non-litigious. An electronic notice is generated and transmitted to a remote device in response to the email being labeled as being litigious to provide an alert in near-real time in relation to receiving the email over the network.

Abstract: Systems, methods, and other embodiments associated with anomaly detection are described. In one embodiment, a method monitoring an on-going project that comprises a plurality of processes and process activities that occur during the process. A machine learning model is applied that identifies a group of projects that are a similar type as the on-going and generates an expected level of process activities that are expected to occur. Based on a snap shot of the on-going project at a first time period, observed levels of process activities are determined that occurred in each process. The machine learning model compares for each of the processes, the observed levels of process activities to the expected levels of process activities in a corresponding time period. If the observed levels of process activities fail to fall within a range of the expected levels of process activities, an anomaly alert is generated and displayed.