# US 12,770,204 B2 · Systems and methods for software as a service management using machine learning automation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,770,204 B2 - Document ID: US12770204B2 - Status: Granted - Kind: B2 - Application number: 18/217,447 - Filed: 2023-06-30 - Published: 2025-01-02 - Granted: 2026-10-20 - Earliest claimed: 2023-06-30 - Assignees: Certinia Inc. - Applicants: Certinia Inc. - Inventors: Jessica Treadwell; Daniel Christian Brown; Heidi Renee Minzner - Web page: https://patentgrep.com/patent/US12770204B2 ## CPC - G06Q 50/18: Legal services - G06N 20/00: Machine learning - 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 - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems and methods for software as a service management using machine learning automation are disclosed herein. An example method includes determining current software-as-a-service (SaaS) applications being used by an organization, obtaining criteria for each of the current SaaS applications including at least a cost, a renewal date, uplift, and cybersecurity, generating a machine learning model for the organization that defines a functional fit based at least in part on the criteria, evaluating replacement SaaS applications when one of the current SaaS applications does not comply with the functional fit, the replacement SaaS applications also having criteria, and automatically generating new contract terms for any of the replacement SaaS applications. ## Claims ### Claim 1 A computer implemented method for assessing software-as-a-service (SaaS) applications to address cybersecurity vulnerabilities and determining contract terms for replacement SaaS applications, the method comprising: determining current cybersecurity SaaS applications being used by an organization on a network, the determining comprising scanning the network for Application Program Interface (API) calls; generating a machine learning model for the organization that defines a functional fit based at least in part on criteria of the organization; monitoring, using a processor, the cybersecurity SaaS applications utilizing APIs by tracking data traffic associated with the cybersecurity SaaS applications, to address the cybersecurity vulnerabilities including security holes or attack points; determining the organization's cybersecurity SaaS applications utilization over an interval of time based on the data traffic associated with the cybersecurity SaaS applications; obtaining criteria for each of the current cybersecurity SaaS applications, the criteria comprising data security requirements, the data security requirements including data protocols configured to implement encryption; inputting an updated functional fit into the machine learning model to evaluate cybersecurity replacement SaaS applications, using the processor, the updated functional fit being based on current needs of the organization, the organization's cybersecurity SaaS applications utilization, a SaaS budget, and the criteria; evaluating, using a processor, cybersecurity replacement SaaS applications by the machine learning model when one of the current cybersecurity SaaS applications does not comply with the updated functional fit according to the cybersecurity SaaS applications current utilization and the cybersecurity replacement SaaS applications also having the criteria; and automatically generating new contract terms by the machine learning model for any of the cybersecurity replacement SaaS applications. ### Claim 2 (depends on 1) The method according to claim 1, further comprising generating a dynamic playbook that is used to optimize a selection process for replacing or retaining the cybersecurity SaaS applications. ### Claim 3 (depends on 2) The method according to claim 2, wherein at least a portion of the criteria of the current cybersecurity SaaS applications are defined by a user of the organization. ### Claim 4 (depends on 3) The method according to claim 3, wherein generating the dynamic playbook includes generating weighted metrics for the criteria of the cybersecurity replacement SaaS applications and the criteria of the current cybersecurity SaaS applications. ### Claim 5 (depends on 1) The method according to claim 1, further comprising activating an automated workflow application configured to procure a cybersecurity replacement SaaS application in accordance with an algorithmic workflow. ### Claim 6 (depends on 5) The method according to claim 5, wherein activating the automated workflow application comprises: identifying end users configured to approve to select a vendor of the cybersecurity replacement SaaS application; and receiving data representing approvals to select the vendor of the cybersecurity replacement SaaS application from the end users, wherein at least one approval is performed automatically. ### Claim 7 (depends on 1) The method according to claim 1, wherein the criteria include at least one key performance indicator (“KPI”) metric. ### Claim 8 (depends on 7) The method according to claim 7, wherein the criteria include data representing terms in an electronic document constituting requirements associated with an exchange of data. ### Claim 9 (depends on 1) The method according to claim 1, further comprising: monitoring performance data for the current cybersecurity SaaS applications to calculate utilization thereof; and selecting one of the cybersecurity replacement SaaS applications based on utilization data. ### Claim 10 (depends on 1) The method according to claim 1, further comprising: monitoring performance data; predicting data based on the performance data during an interval of time to form forecasted data; and selecting one of the cybersecurity replacement SaaS applications as a function of the forecasted data. ### Claim 11 (depends on 1) The method according to claim 1, wherein the contract terms include renewal dates, uplift, and vendor. ### Claim 12 (depends on 1) The method according to claim 1, wherein the data protocols include PCI DSS protocols and ACH protocols. ### Claim 13 A computer system for assessing and determining terms for replacement cybersecurity software-as-a-service (SaaS) applications to address cybersecurity vulnerabilities and determining contract terms for cybersecurity replacement SaaS applications, the system comprising: a processor; and memory for storing instructions, the processor executing the instructions to: determine current cybersecurity SaaS applications being used by an organization on a network, the determining comprising scanning the network for Application Program Interface (API) calls; generate a machine learning model for the organization that defines a functional fit based at least in part on criteria of the organization; monitor the cybersecurity SaaS applications utilizing an APIs that tracks data traffic associated with the cybersecurity SaaS applications, to address the cybersecurity vulnerabilities including security holes or attack points; determine the organization's cybersecurity SaaS applications utilization over an interval of time based on the data traffic associated with the cybersecurity SaaS applications; obtain criteria for each of the current cybersecurity SaaS applications, the criteria comprising data security requirements, the data security requirements including data protocols configured to implement encryption; input an updated functional fit into the machine learning model to evaluate cybersecurity replacement SaaS applications, the updated functional fit being based on current needs of the organization, the organization's cybersecurity SaaS applications utilization, a SaaS budget, and the criteria of each of the current cybersecurity SaaS applications; evaluate cybersecurity replacement SaaS applications by the machine learning model when one of the current cybersecurity SaaS applications does not comply with the updated functional fit according to the cybersecurity SaaS applications current utilization based on API traffic analysis and the cybersecurity replacement SaaS applications also having the criteria; generate a dynamic playbook that is used to optimize a selection process for replacing or retaining the cybersecurity SaaS applications; and generate a dashboard that indicates recommendations of the current cybersecurity SaaS applications that should be replaced based on the dynamic playbook. ### Claim 14 (depends on 13) The system according to claim 13, wherein at least a portion of the criteria of the current cybersecurity SaaS applications are defined by a user of the organization. ### Claim 15 (depends on 14) The system according to claim 14, wherein the processor is configured to generate the dynamic playbook by generating weighted metrics for the criteria of the cybersecurity replacement SaaS applications and the criteria of the current cybersecurity SaaS applications. ### Claim 16 (depends on 13) The system according to claim 13, wherein the processor is configured to activate an automated workflow application configured to procure a cybersecurity replacement SaaS application in accordance with an algorithmic workflow. ### Claim 17 (depends on 16) The system according to claim 16, wherein the processor is configured to activate the automated workflow application to: identify end users configured to approve to select a vendor of the cybersecurity replacement SaaS application; and receive data representing approvals to select the vendor of the cybersecurity replacement SaaS application from the end users, wherein at least one approval is performed automatically. ### Claim 18 (depends on 13) The system according to claim 13, wherein the processor is configured to: monitor performance data for the current cybersecurity SaaS applications to calculate utilization thereof; and select one of the cybersecurity replacement SaaS applications based on utilization data. ### Claim 19 (depends on 18) The system according to claim 18, wherein the processor is configured to: monitor the performance data; predict data based on the performance data during an interval of time to form forecasted data; and select one of the cybersecurity replacement SaaS applications as a function of the forecasted data. ### Claim 20 (depends on 13) The system according to claim 13, wherein the processor is configured to automatically generate new contract terms by the machine learning model for any of the cybersecurity replacement SaaS applications. ### Claim 21 (depends on 13) The system according to claim 13, wherein the criteria include data representing terms in an electronic document constituting requirements associated with an exchange of data. ### Claim 22 (depends on 13) The system according to claim 13, wherein the processor is configured to: evaluate invoices and subsequent payments for a customer; evaluate payment history for the customer; and generate a suggestion to shorten a time span for payment of an invoice based on the payment history.