# US 12,182,178 B1 · System and methods for varying optimization solutions using constraints based on an endpoint > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,182,178 B1 - Document ID: US12182178B1 - Status: Granted - Kind: B1 - Application number: 18/612,783 - Filed: 2024-03-21 - Granted: 2024-12-31 - Earliest claimed: 2024-03-21 - Assignees: The Strategic Coach Inc. - Applicants: The Strategic Coach Inc. - Inventors: Barbara Sue Smith; Daniel J. Sullivan - Web page: https://patentgrep.com/patent/US12182178B1 ## CPC - G06F 16/285: {Clustering or classification} - G06F 16/2428: {Query predicate definition using graphical user interfaces, including menus and forms (G06F16/2423 takes precedence)} - G06F 40/30: Semantic analysis - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A system for varying optimization solutions using constraints based on an endpoint, the system including at least a processor, a memory communicatively connected to the at least a processor, the memory containing instructions configuring the processor to receive process data including a plurality of impediments, generate an endpoint using a module configured to, analyze the plurality of impediments by extracting a feature from each impediment of the plurality of impediments, classify a plurality of impediments using the extracted features to a plurality of identifiers, rank the plurality of identifiers based on severity score, output the endpoint based on an identifier severity score, identify a plurality of nodes, receive at least a constraint, locate in the plurality of nodes an outlier cluster based on the endpoint, determine an outlier process as a function of the outlier cluster, and determine a visual element data structure as a function of the outlier process. ## Claims ### Claim 1 A system for varying optimization solutions using constraints based on an endpoint, the system comprising: at least a processor; a memory communicatively connected to the at least a processor, the memory containing instructions configuring the processor to: receive process data comprising a plurality of impediments; generate an endpoint using a module configured to: analyze the plurality of impediments by extracting a feature from each impediment of the plurality of impediments; classify a plurality of impediments using the extracted features to a plurality of identifiers; rank the plurality of identifiers based on an identifier severity score; output the endpoint based on the identifier severity score; identify a plurality of nodes; receive at least a constraint; locate, in the plurality of nodes, an outlier cluster based on the endpoint, wherein locating the outlier cluster comprises: identifying a target process; inputting the target process and the plurality of nodes into an impact metric machine learning model; receiving an impact metric from the impact metric machine learning model; and determining the outlier cluster as a function of the impact metric; determine an outlier process as a function of the outlier cluster; and determine a visual element data structure as a function of the outlier process. ### Claim 2 (depends on 1) The system of claim 1, wherein the module comprises a language processing model configured to identify a plurality of keywords from the plurality of impediments to output the plurality of features. ### Claim 3 (depends on 1) The system of claim 1, wherein the module comprises a feature classifier configured to classify the plurality of impediments to the plurality of identifiers. ### Claim 4 (depends on 1) The system of claim 1, wherein the module comprises a scoring machine learning model configured: to receive the plurality of identifiers as an input; and perform a scoring function to output a plurality of severity scores, wherein the scoring machine learning model is trained with datasets including weights associated with a plurality of features and identifiers. ### Claim 5 (depends on 1) The system of claim 1, wherein the at least a constraint comprises an interface query data structure wherein the interface query data structure is at least partially based on data describing attributes of a user that is retrieved from a database including categorical information correlated to a historical range of data. ### Claim 6 (depends on 1) The system of claim 1, wherein the impact metric indicates the degree to which the plurality of nodes supports the target process. ### Claim 7 (depends on 1) The system of claim 1, wherein determining the outlier process as a function of the outlier cluster comprises: inputting an outlier cluster in an outlier process machine learning model; receiving an outlier process from the outlier machine learning model. ### Claim 8 (depends on 1) The system of claim 1, wherein the memory contains instructions configuring the at least a processor to: determine a visual element as a function of the visual element data structure; and configure a user device to display the visual element to the user. ### Claim 9 (depends on 8) The system of claim 8, wherein the visual element is configured to display the input field to the user by a Graphical User Interface (GUI), wherein the GUI is a point of interaction between the user and the remote display device. ### Claim 10 A method for varying optimization solutions using constraints based on an endpoint, the method comprising: receiving, by a computing device, process data comprising a plurality of impediments; generating, by the computing device, an endpoint using a module configured to: analyze the plurality of impediments by extracting a feature from each impediment of the plurality of impediments; classify a plurality of impediments using the extracted features to a plurality of identifiers; rank the plurality of identifiers based on an identifier severity score; output the endpoint based on the identifier severity score; identifying, by the computing device, a plurality of nodes; receiving, by the computing device, at least a constraint; locating, by the computing device, in the plurality of nodes an outlier cluster based on the endpoint, wherein locating the outlier cluster comprises: identifying a target process; inputting the target process and the plurality of nodes into an impact metric machine learning model; receiving an impact metric from the impact metric machine learning model; and determining the outlier cluster as a function of the impact metric; determining, by the computing device, an outlier process as a function of the outlier cluster; and determining, by the computing device, a visual element data structure as a function of the outlier process. ### Claim 11 (depends on 10) The method of claim 10, further comprising identifying, using a language processing model, to identify a plurality of keywords from the plurality of impediments to output the plurality of features. ### Claim 12 (depends on 10) The method of claim 10, further comprising, using a feature classifier, to classify the plurality of impediments to the plurality of identifiers. ### Claim 13 (depends on 10) The method of claim 10, further comprising: receiving, using a scoring machine learning model, the plurality of identifiers as an input; and performing, using the scoring machine learning model, a scoring function to output a plurality of severity scores, wherein the scoring machine learning model is trained with datasets including weights associated with a plurality of features and identifiers. ### Claim 14 (depends on 10) The method of claim 10, wherein the at least a constraint comprises an interface query data structure wherein the interface query data structure is at least partially based on data describing attributes of a user that is retrieved from a database including categorical information correlated to a historical range of data. ### Claim 15 (depends on 10) The method of claim 10, wherein the impact metric indicates that the plurality of nodes supports the target process. ### Claim 16 (depends on 10) The method of claim 10, wherein determining the outlier process as a function of the outlier cluster comprises: inputting an outlier cluster in an outlier process machine learning model; receiving an outlier process from the outlier machine learning model. ### Claim 17 (depends on 10) The method of claim 10, wherein the memory contains instructions configuring the at least a processor to: determine a visual element as a function of the visual element data structure; and configure a user device to display the visual element to the user. ### Claim 18 (depends on 17) The method of claim 17, wherein the visual element comprises a remote display device is configured to display the input field to the user by a Graphical User Interface (GUI), wherein the GUI is a point of interaction between the user and the remote display device.