# US 12,181,999 B1 · Machine learning modeling of candidate models for software installation usage > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,181,999 B1 - Document ID: US12181999B1 - Status: Granted - Kind: B1 - Application number: 17/589,534 - Filed: 2022-01-31 - Granted: 2024-12-31 - Earliest claimed: 2021-01-29 - Assignees: Cisco Technology, Inc. - Applicants: Splunk Inc. - Inventors: Yanpei Chen; Archana Ganapathi - Web page: https://patentgrep.com/patent/US12181999B1 ## CPC - G06F 11/3438: {monitoring of user actions (tracking the activity of the user H04L67/535)} - G06F 8/61: Installation - G06N 20/00: Machine learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06Q 30/0201: Market modelling; Market analysis; Collecting market data - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract This document discloses methods and systems for modeling product usage. In one practical application, the systems and methods may be utilized to model product usage based on large volume, machine generated product usage data to optimize product pricing and operations. Specifically, the systems and methods described herein may utilize methods with key components to select the maximum number of dimensions that can be modeled based on the number of data points, use a logarithm kernel function to normalize machine data with long-tailed statistical distributions on different numerical scales, compare a large number of candidate models with different candidate dimensions and different structures, and quantify the amount of change and drift in models over time. ## Claims ### Claim 1 A computing device, comprising: one or more hardware processors; and a non-transitory computer-readable medium having stored thereon instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations including: generating a first number (N) of normalized data points from N data points, each data point of the N data points having data for a second number (C) of dimensions, by taking a logarithm of the data for each dimension of the C dimensions for each data point of the N data points, and wherein the N data points relate to product usage data; determining, based on a predetermined threshold and the N normalized data points, a third number (D) of dimensions to use for modeling the N normalized data points; selecting a plurality of dimension sets, wherein each dimension set includes a different combination of a plurality of dimensions and includes no greater than D dimensions; for each dimension set in the plurality of dimension sets, generating, via a machine learning model, a candidate model based on the different combination of the plurality of dimensions in the dimension set, wherein each candidate model models product usage; evaluating each candidate model and generating respective model testing results for each candidate model; selecting a model from the candidate models based on a plurality of model quality measures from model testing results associated with the candidate models. ### Claim 2 (depends on 1) The computing device of claim 1, wherein the operations further comprise: receiving a request to identify a predicted value for an additional data point using the selected model, the additional data point including data on dimensions corresponding to the dimension set corresponding to the selected model; and based on the additional data point and the generated model, responding to the request with the predicted value. ### Claim 3 (depends on 1) The computing device of claim 1, wherein the operations further comprise: determining, for a candidate number of dimensions representing the D dimensions, a number of bits of information (B) per dimension based on the candidate number of dimensions and the first number; and comparing B to the predetermined threshold. ### Claim 4 (depends on 3) The computing device of claim 3, wherein the determining of B uses B = 1 D ⁢ log ⁢ 2 ⁢ ( N ) . ### Claim 5 (depends on 3) The computing device of claim 3, wherein the operations further comprise: based on a result of the comparing, rejecting the candidate number of dimensions. ### Claim 6 (depends on 1) The computing device of claim 1, wherein the generating of the candidate model for each dimension set comprises generating a polynomial model. ### Claim 7 (depends on 1) The computing device of claim 1, wherein the operations further comprise: generating the N data points by linking customer relational information from a customer relationship management (CRM) system and the product usage data using shared identifiers. ### Claim 8 (depends on 7) The computing device of claim 7, wherein the operations further comprise: accessing CRM data that indicates a parent-subsidiary relationship between a parent account and a subsidiary account; and accessing first product usage data that is linked to both the parent account and the subsidiary account; wherein the generating of the N data points comprises generating a data point that links the first product usage data to the subsidiary account. ### Claim 9 (depends on 8) The computing device of claim 8, wherein the generating of the N data points excludes generating a second data point that links the first product usage data to the parent account. ### Claim 10 A computer-implemented method, comprising: generating a first number (N) of normalized data points from N data points, each data point of the N data points having data for a second number (C) of dimensions, by taking a logarithm of the data for each dimension of the C dimensions for each data point of the N data points, and wherein the N data points relate to product usage data; determining, based on a predetermined threshold and the N normalized data points, a third number (D) of dimensions to use for modeling the N normalized data points; selecting a plurality of dimension sets, wherein each dimension set includes a different combination of a plurality of dimensions and includes no greater than D dimensions; for each dimension set in the plurality of dimension sets, generating, via a machine learning model, a candidate model based on the different combination of the plurality of dimensions in the dimension set, wherein each candidate model models product usage; evaluating each candidate model and generating respective model testing results for each candidate model; selecting a model from the candidate models based on a plurality of model quality measures from model testing results associated with the candidate models, wherein the selected model models product usage. ### Claim 11 (depends on 10) The computer-implemented method of claim 10, further comprising: receiving a request to identify a predicted value for an additional data point using the selected model, the additional data point including data on dimensions corresponding to the dimension set corresponding to the selected model; and based on the additional data point and the generated model, responding to the request with the predicted value. ### Claim 12 (depends on 10) The computer-implemented method of claim 10, further comprising: determining, for a candidate number of dimensions representing the D dimensions, a number of bits of information (B) per dimension based on the candidate number of dimensions and the first number; and comparing B to the predetermined threshold. ### Claim 13 (depends on 12) The computer-implemented method of claim 12, wherein the determining of B uses B = 1 D ⁢ log ⁢ 2 ⁢ ( N ) . ### Claim 14 (depends on 12) The computer-implemented method of claim 12, further comprising: based on a result of the comparing, rejecting the candidate number of dimensions. ### Claim 15 (depends on 10) The computer-implemented method of claim 10, wherein the generating of the candidate model for each dimension set comprises generating a polynomial model. ### Claim 16 (depends on 10) The computer-implemented method of claim 10, further comprising: generating the N data points by linking customer relational information from a customer relationship management (CRM) system and the product usage data using shared identifiers. ### Claim 17 (depends on 16) The computer-implemented method of claim 16, further comprising: accessing CRM data that indicates a parent-subsidiary relationship between a parent account and a subsidiary account; and accessing first product usage data that is linked to both the parent account and the subsidiary account; wherein the generating of the N data points comprises generating a data point that links the first product usage data to the subsidiary account. ### Claim 18 A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to perform operations including: generating a first number (N) of normalized data points from N data points, each data point of the N data points having data for a second number (C) of dimensions, by taking a logarithm of the data for each dimension of the C dimensions for each data point of the N data points, and wherein the N data points relate to product usage data; determining, based on a predetermined threshold and the N normalized data points, a third number (D) of dimensions to use for modeling the N normalized data points; selecting a plurality of dimension sets, wherein each dimension set includes a different combination of a plurality of dimensions and includes no greater than D dimensions; for each dimension set in the plurality of dimension sets, generating, via a machine learning model, a candidate model based on the different combination of the plurality of dimensions in the dimension set, wherein each candidate model models product usage; evaluating each candidate model and generating respective model testing results for each candidate model; selecting a model from the candidate models based on a plurality of model quality measures from model testing results associated with the candidate models, wherein the selected model models product usage. ### Claim 19 (depends on 18) The non-transitory computer-readable medium of claim 18, wherein the operations further comprise: receiving a request to identify a predicted value for an additional data point using the selected model, the additional data point including data on dimensions corresponding to the dimension set corresponding to the selected model; and based on the additional data point and the generated model, responding to the request with the predicted value. ### Claim 20 (depends on 18) The non-transitory computer-readable medium of claim 18, wherein the operations further comprise: determining, for a candidate number of dimensions representing the D dimensions, a number of bits of information (B) per dimension based on the candidate number of dimensions and the first number; and comparing B to the predetermined threshold.