# US 2018/0025286 A1 · Detecting trends in evolving analytics models > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0025286 A1 - Document ID: US20180025286A1 - Status: Published application - Kind: A1 - Application number: 15/218,316 - Filed: 2016-07-25 - Published: 2018-01-25 - Earliest claimed: 2016-07-25 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Leonid Gorelik; Nancy L. Navarro; Srinivasan Parthasarathy; Alexander Pyasik; Yifat Yulevich - Web page: https://patentgrep.com/patent/US20180025286A1 ## CPC - G06N 20/00: Machine learning - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A computer-implemented method includes receiving data representing pre-existing instances of an analytics model developed over time; detecting changes in state of the analytics model over time to detect trends; generating a new instance of the analytics model that has been modified based on detected trends in the analytics model; generating new training data based on discovered trends of the analytics model over time; comparing a coverage of the new instance of the analytics model and coverages of the pre-existing instances of the analytics model with the new training data; and determining whether new instance of the analytics model have better coverage than the pre-existing instances of the analytics model with the new training data. A corresponding computer program product and system are also disclosed. ## Claims ### Claim 1 A method for detecting trends in an analytics model comprising: receiving data representing pre-existing instances of an analytics model developed over time; detecting changes in state of the analytics model over time to detect trends; generating a new instance of the analytics model that has been modified based on detected trends in the analytics model; generating new training data based on discovered trends of the analytics model over time; comparing a coverage of the new instance of the analytics model and coverages of the pre-existing instances of the analytics model with the new training data; and determining whether new instance of the analytics model have better coverage than the pre-existing instances of the analytics model with the new training data. ### Claim 2 (depends on 1) The method of claim 1, wherein the analytics model comprises behavioral data. ### Claim 3 (depends on 2) The method of claim 2, wherein the analytics model is modified so as to reflect changes in the behavioral data. ### Claim 4 (depends on 2) The method of claim 2, wherein the analytics model further comprises an analytic component, the analytic component being associated with metadata, wherein the metadata comprises a description of an analytic technique used by the analytics model, assumptions required for the analytic technique to be valid, constraints on the analytics model, sensitivities of the analytics model, a definition of a type of data on which the analytics model operates, and a definition of an output the analytics model produces. ### Claim 5 (depends on 1) The method of claim 1, wherein the coverage of the new instance of the analytics model is compared with the coverage of at least one other instance of the analytics model using a statistical test. ### Claim 6 (depends on 5) The method of claim 5, wherein the statistical test is an F-test. ### Claim 7 (depends on 1) The method of claim 1, further comprising: identifying one or more training sets, the one or more training being a part of a current model checkpoint object; identifying one or more over-time model trends; and wherein the new training data is generated by using data generator functions to combine the one or more training sets with one or more over-time model trends. ### Claim 8 A computer system for detecting trends in an analytics model, the system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform: receiving data representing pre-existing instances of an analytics model developed over time; detecting changes in state of the analytics model over time to detect trends; generating a new instance of the analytics model that has been modified based on detected trends in the analytics model; generating new training data based on discovered trends of the analytics model over time; comparing a coverage of the new instance of the analytics model and coverages of the pre-existing instances of the analytics model with the new training data; and determining whether new instance of the analytics model have better coverage than the pre-existing instances of the analytics model with the new training data. ### Claim 9 (depends on 8) The computer system of claim 8, wherein the analytics model comprises behavioral data. ### Claim 10 (depends on 9) The computer system of claim 9, wherein the analytics model is modified so as to reflect changes in the behavioral data. ### Claim 11 (depends on 9) The computer system of claim 9, wherein the analytics model further comprises an analytic component, the analytic component being associated with metadata, wherein the metadata comprises a description of an analytic technique used by the analytics model, assumptions required for the analytic technique to be valid, constraints on the analytics model, sensitivities of the analytics model, a definition of a type of data on which the analytics model operates, and a definition of an output the analytics model produces. ### Claim 12 (depends on 8) The computer system of claim 8, wherein the coverage of the new instance of the analytics model is compared with the coverage of at least one other instance of the analytics model using a statistical test. ### Claim 13 (depends on 12) The computer system of claim 12, wherein the statistical test is an F-test. ### Claim 14 (depends on 8) The computer system of claim 8, further comprising computer program instructions to perform: identifying one or more training sets, the one or more training being a part of a current model checkpoint object; identifying one or more over-time model trends; and wherein the new training data is generated by using data generator functions to combine the one or more training sets with one or more over-time model trends. ### Claim 15 A computer program product for detecting trends in an analytics model, the computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer, to cause the computer to perform a method comprising: receiving data representing pre-existing instances of an analytics model developed over time; detecting changes in state of the analytics model over time to detect trends; generating a new instance of the analytics model that has been modified based on detected trends in the analytics model; generating new training data based on discovered trends of the analytics model over time; comparing a coverage of the new instance of the analytics model and coverages of the pre-existing instances of the analytics model with the new training data; and determining whether new instance of the analytics model have better coverage than the pre-existing instances of the analytics model with the new training data. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the analytics model comprises behavioral data. ### Claim 17 (depends on 16) The computer program product of claim 16, wherein the analytics model is modified so as to reflect changes in the behavioral data. ### Claim 18 (depends on 16) The computer program product of claim 16, wherein the analytics model further comprises an analytic component, the analytic component being associated with metadata, wherein the metadata comprises a description of an analytic technique used by the analytics model, assumptions required for the analytic technique to be valid, constraints on the analytics model, sensitivities of the analytics model, a definition of a type of data on which the analytics model operates, and a definition of an output the analytics model produces. ### Claim 19 (depends on 15) The computer program product of claim 15, wherein the coverage of the new instance of the analytics model is compared with the coverage of at least one other instance of the analytics model using a statistical test. ### Claim 20 (depends on 19) The computer program product of claim 19, wherein the statistical test is an F-test.