Patent ID: 11893508
Assignee: DIGITAL.AI SOFTWARE, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A method for performing scalable predictive analysis for analytics as a software service in multi-tenant environment, the method comprising:
obtaining a first input data associated with a first tenant;
determining first predictive analysis parameters associated with the first tenant by analyzing the first input data for selected entities;
determining at least one of source schema changes or data updates on the first predictive analysis parameters over a period of time by comparing the first input data with a third input data associated with the first tenant, to refine the computer-generated first predictive model based on the third input data;
identifying at least one of (a) one or more parameters, from the first predictive analysis parameters, that are missing in the third input data, (b) one or more new parameters that are present in the third input data, (c) one or more modified parameters, or (d) one or more unchanged parameters that are present in the first input data and the third input data, based on the comparison of the first input data with the third input data, wherein the one or more new parameters, the one or more modified parameters and the one or more unchanged parameters from the third input data are analyzed using data analysis techniques, wherein data associated with at least one of the one or more modified parameters or the one or more unchanged parameters are compared to determine whether the data distribution associated with the one or more modified parameters or the one or more unchanged parameters has significant changes; and
refining the computer-generated first predictive model by incorporating the source schema changes and the data updates associated with the third input to obtain a refined first predictive model for the first tenant, wherein the incorporating of the source schema changes and the data updates comprises at least one of (a) removing the one or more missing parameters from the first predictive analysis parameters, or (b) incorporating, in the computer-generated first predictive model, at least one of (i) the one or more new parameters that are present in the third input data on receiving a fourth input, (ii) the one or more modified parameters that have significant changes, or (iii) the one or more unchanged parameters that have significant changes.