Patent ID: 11972382
Assignee: nan
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 16:
17. A computer-implemented method for use an information technology (IT) environment having a plurality of domains, each domain having key performance indicator (KPI) data, the method comprising:
dynamically monitoring the domains of the IT environment, and reflect one or more changes of one or more of the domains in one or more machine learning (ML) models;
identifying a first KPI related to a technical health issue of one or more of the dynamically monitored domains, the first KPI quantifying a performance of a first information technology (IT) component;
performing a root cause analysis (RCA) for the identified first KPI, including, using a processor operatively coupled to computer memory:
identifying a first KPI classification associated with the first KPI;
accessing a knowledge graph (KG) from a knowledge base stored in the computer memory, the KG corresponding to the identified first KPI classification;
leveraging the KG to selectively identify the root cause corresponding to the first KPI;
if the root cause is selectively identified, leveraging the KG to identify a second KPI associated with classification of the selectively identified root cause of the first KPI classification, and evaluate a strength of a correlation between the first KPI and the second KPI, the second KPI quantifying a performance of a second IT component; and
if the root cause is not selectively identified, leveraging at least one of the one or more ML models using artificial intelligence (AI) to carry out a time series analysis of a series of KPIs over time to selectively identify at least one KPI that is proximally related to the first KPI, identify a classification of the proximally related KPI to diagnose the root cause of the first KPI, and provide a strength of the correlation between the first KPI and the proximally related KPI;
dynamically amending the KG to reflect the diagnosed root cause, wherein a first node of the KG represents the first KPI classification, a second node of the KG represents either the second KPI classification or the proximally related KPI classification, and an edge connecting the first node and the second node represents the correlation; and
generating a diagnosis of the technical health issue within the IT environment based on the strength of the correlation between the first KPI and either the second KPI or the proximally related KPI, and utilizing the correlation to manage the IT environment, including resolving the technical health issue.