A method and information handling system configured to store aggregate information handling system interaction telemetry data representing levels of operational activity reported for a user of an information handling system in a monitoring system data repository memory device and for receiving aggregate information handling system interaction telemetry data for a plurality of other users that is crowd-sourced from a population of information handling systems accessed by a plurality of other users. An application processor executes instructions of an information handling system interaction signature platform. The interaction signature platform may apply a supervised learning model algorithm to the aggregate information handling system interaction telemetry data for the user in comparison to the aggregate information handling system interaction telemetry data for the plurality of other users to determine at least one indirect identifier of the interaction telemetry data, wherein a value of the at least one indirect identifier makes the interaction unique enough to identify the user to indicate a learned usage signature of the user. The interaction signature platform constructs a fingerprint profile of operational activity by the user including a usage signature baseline for the at least one indirect identifier.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information handling system comprising: a monitoring system data repository memory device for storing aggregate information handling system interaction telemetry data representing levels of operational activity reported for a user of an information handling system; the monitoring system data repository memory device receiving aggregate information handling system interaction telemetry data for a plurality of other users that is crowd-sourced from a population of information handling systems accessed by a plurality of other users; an application processor executing instructions of an information handling system interaction signature platform: to apply a supervised learning model algorithm to the aggregate information handling system interaction telemetry data for the user in comparison to the aggregate information handling system interaction telemetry data for the plurality of other users to determine at least one indirect identifier of the interaction telemetry data, wherein a value of the at least one indirect identifier makes the interaction unique enough to identify the user to indicate a learned usage signature of the user, and to construct a fingerprint profile of operational activity by the user including a usage signature baseline for the at least one indirect identifier. 2. The information handling system of claim 1 wherein the at least one indirect identifier includes average CPU consumption. 3. The information handling system of claim 1 wherein the at least one indirect identifier includes an LCD average brightness percentage. 4. The information handling system of claim 1 wherein the at least one indirect identifier includes average threadcount. 5. The information handling system of claim 1 wherein the aggregate information handling system interaction telemetry data reported for the user is received from the information handling system repeatedly accessed by the user with user identification credentials during a learning phase. 6. The information handling system of claim 1 further comprising: the application processor to determine a plurality of indirect identifiers for the user; and the application processor to construct a fingerprint profile of operational activity by the user including a plurality of usage signature baseline levels for the plurality of indirect identifiers. 7. The information handling system of claim 1 wherein the supervised learning model algorithm is a random forest classification algorithm. 8. A computerized method of constructing a fingerprint profile of operational activity by a user of an information handling system comprising: storing aggregate information handling system interaction telemetry data representing levels of operational activity reported for a user of an information handling system in a monitoring system data repository memory device; receiving aggregate information handling system interaction telemetry data for a plurality of other users that is crowd-sourced from a population of information handling systems accessed by the plurality of other users via a network adapter; executing instructions of an information handling system interaction signature platform via an application processor to apply a supervised learning model algorithm to the aggregate information handling system interaction telemetry data for the user in comparison to the aggregate information handling system interaction telemetry data for a plurality of other users; determining at least one indirect identifier of the interaction telemetry data, wherein a value of the at least one indirect identifier makes the interaction unique enough to identify the user to indicate a learned usage signature of the user; and constructing a fingerprint profile of operational activity by the user including a usage signature baseline for the at least one indirect identifier. 9. The computerized method of claim 8 wherein the at least one indirect identifier includes average available memory percentage during operation. 10. The computerized method of claim 8 wherein the at least one indirect identifier includes CPU idle time percentage. 11. The computerized method of claim 8 wherein the usage signature baseline for the at least one indirect identifier is a distribution of values for the at least one indirect identifier. 12. The computerized method of claim 8 further comprising: constructing a fingerprint profile of operational activity by determining a learned statistical deviation from the signature value baseline for the at least one indirect identifier. 13. The computerized method of claim 8 further comprising: the application processor to determine a plurality of indirect identifiers for the user; and the application processor to construct a fingerprint profile of operational activity by the user including a plurality of usage signature baseline levels for the plurality of indirect identifiers. 14. The computerized method of claim 8 wherein the supervised learning model algorithm is a Bayes classification algorithm. 15. An information handling system comprising: a monitoring system data repository memory device for storing a fingerprint profile of operational activity by a user including a usage signature baseline for at least one indirect identifier wherein the baseline value of the at least one indirect identifier makes an interaction unique enough to identify the user an application processor executing instructions of an information handling system interaction signature platform: to obtain monitored information handling system interaction telemetry data comprising measured levels of operational activity for the at least one indirect identifier, and to compare the monitored information handling system interaction telemetry data for the at least one indirect identifier with the baseline value for the at least one indirect identifier making the interaction unique enough to identify the user. 16. The information handling system of claim 15 wherein the fingerprint profile of operational activity by the user include usage signature baselines for a plurality of indirect identifiers. 17. The information handling system of claim 15 further comprising: the application processor executing instructions of the information handling system interaction signature platform to detect a match to the fingerprint profile of operational activity by the user if the monitored information handling system interaction telemetry data for the at least one indirect identifier falls within a learned statistical deviation from the baseline value for the at least one indirect identifier. 18. The management information handling system of claim 15 further comprising: the application processor assessing user identification credentials to identify a fingerprint profile of operational activity associated with the user identification credentials; the application processor determining that a non-match has occurred if the monitored information handling system interaction telemetry data for the at least one indirect identifier falls outside a learned statistical deviation from the baseline value for the at least one indirect identifier. 19. The management information handling system of claim 15 further comprising: a network adapter for transmitting a dispatch ticket indicating a non-match with the fingerprint profile of operational activity associated with the user identification credentials. 20. The management information handling system of claim 15 further comprising: the application processor to compare the monitored information handling system interaction telemetry data for the at least one indirect identifier with the fingerprint profiles of operational activity for a set of known users.