Abstract: Systems, methods, and other embodiments associated with a machine learning system that monitors and detects health and safety risks in electronic correspondence related to a target field are described. In one embodiment, a method includes monitoring email communications over a network to identify an email associated with a target field. A machine learning classifier is initiated that is configured to classify text from the email with a risk as being related to a safety risk or a non-risk. The machine learning classifier generates a probability risk value that the email is related to a safety risk and labels the email as safety risk or non-risk based at least in part on the probability risk value indicating that the email is a safety risk. An electronic notice is generated and transmitted to a remote device in response to the email being labeled as being safety risk to provide an alert.

Abstract: Systems, methods, and other embodiments associated with detecting severity levels of risk in an electronic correspondence are described. In one embodiment, a method includes inputting, into a memory, a target electronic correspondence that has been classified as being litigious by a machine learning classifier. An artificial intelligence rule-based technique is applied to the target electronic correspondence that identifies high and medium risk level keywords. The technique is also configured to generate a litigious score based on a sum of term frequencies-inverse document frequencies using the remaining keywords. An electronic notice is transmitted to a remote computer over a communication network that identifies the target electronic correspondence and the level of litigation risk.

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 a machine learning system that monitors and detects health and safety risks in electronic correspondence related to a target field are described. In one embodiment, a method includes monitoring email communications over a network to identify an email associated with a target field. A machine learning classifier is initiated that is configured to classify text from the email with a risk as being related to a safety risk or a non-risk. The machine learning classifier generates a probability risk value that the email is related to a safety risk and labels the email as safety risk or non-risk based at least in part on the probability risk value indicating that the email is a safety risk. An electronic notice is generated and transmitted to a remote device in response to the email being labeled as being safety risk to provide an alert.

Abstract: Systems, methods, and other embodiments associated with detecting severity levels of risk in an electronic correspondence are described. In one embodiment, a method includes inputting, into a memory, a target electronic correspondence that has been classified as being litigious by a machine learning classifier. An artificial intelligence rule-based technique is applied to the target electronic correspondence that identifies high and medium risk level keywords. The technique is also configured to generate a litigious score based on a sum of term frequencies-inverse document frequencies using the remaining keywords. An electronic notice is transmitted to a remote computer over a communication network that identifies the target electronic correspondence and the level of litigation risk.

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.