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 automatic smart extraction and workflow assignment of action items are described. In one embodiment, a method includes extracting a set of candidate action items from text of a construction project manual; applying static rules to the candidate action items to distinguish valid and invalid candidate action items; evaluating each valid candidate action item with a first machine learning model to label the valid candidate action item either (i) a true action item or (ii) not a true action item; evaluating each true action item with a second machine learning model to allocate each of the true action items to a construction workflow class; and transmitting the set of true action items to a submittal exchange system to populate one or more workflows.

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 automatic smart extraction and workflow assignment of action items are described. In one embodiment, a method includes extracting a set of candidate action items from text of a construction project manual; applying static rules to the candidate action items to distinguish valid and invalid candidate action items; evaluating each valid candidate action item with a first machine learning model to label the valid candidate action item either (i) a true action item or (ii) not a true action item; evaluating each true action item with a second machine learning model to allocate each of the true action items to a construction workflow class; and transmitting the set of true action items to a submittal exchange system to populate one or more workflows.

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: A tool for performing a user authentication utilizing patterns. A first computing device receives a userid and password of a user attempting to access a secure resource using the first computing device. The first computing device determines a pattern. The pattern is determined at the time the userid and password of the user attempting to access the secure resource are received. The first computing device determines that the determined pattern matches a stored pattern of a user authorized to access the secured resource. The first computing device, in response to determining that the pattern matches the stored pattern, sends the userid and password to a second computing device for further authentication towards accessing the secure resource.

Abstract: A tool for performing a user authentication utilizing patterns. A first computing device receives a userid and password of a user attempting to access a secure resource using the first computing device. The first computing device determines a pattern. The pattern is determined at the time the userid and password of the user attempting to access the secure resource are received. The first computing device determines that the determined pattern matches a stored pattern of a user authorized to access the secured resource. The first computing device, in response to determining that the pattern matches the stored pattern, sends the userid and password to a second computing device for further authentication towards accessing the secure resource.