System and methods for varying optimization solutions using constraints based on an endpoint

Patent metadata

Field	Value
Publication number	US-12536201-B2
Application number	US-202418924622-A
Country	US
Kind code	B2
Filing date	Oct 23, 2024
Priority date	Mar 21, 2024
Publication date	Jan 27, 2026
Grant date	Jan 27, 2026

Abstract

A system for varying optimization solutions using constraints based on an endpoint, the system comprising a processor and a memory configuring the processor to receive process data; 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; generate a plurality of impediments using the extracted features to a plurality of identifiers using a machine learning process; output the endpoint based on the identifier severity score; identify a plurality of nodes; receive at least a constraint containing at least a parameter; locate, in the plurality of nodes, an outlier cluster based on the endpoint, the at least a parameter and the labeled plurality of identifiers; 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.

First claim

What is claimed is:

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;
generate a plurality of impediments using a machine learning process as a function of the extracted features;
label a 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 containing at least a parameter;
locate, in the plurality of nodes, an outlier cluster based on the endpoint, the at least a parameter and the labeled plurality of identifiers, wherein locating the outlier cluster based on the endpoint 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 an 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 based on the outlier process.

2 . 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.

3 . The system of claim 1 , wherein the module comprises a feature classifier configured to classify the plurality of impediments to the plurality of identifiers.

4 . 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.

5 . 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.

6 . The system of claim 1 , wherein the impact metric indicates a degree to which the plurality of nodes supports the target process.

7 . 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 process machine learning model.

8 . 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 a user.

9 . The system of claim 8 , wherein the visual element is configured to display an input field to the user by a Graphical User Interface (GUI), wherein the GUI is a point of interaction between the user and a remote display device.

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;
generate a plurality of impediments using a machine learning process as a function of the extracted features;
label 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 containing at least a parameter; locating, by the computing device, in the plurality of nodes an outlier cluster based on the endpoint, the at least a parameter and the labeled plurality of identifiers, wherein locating the outlier cluster based on the endpoint 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 an 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.

11 . 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.

12 . The method of claim 10 , further comprising, using a feature classifier, to classify the plurality of impediments to the plurality of identifiers.

13 . 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.

14 . 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.

15 . The method of claim 11 , wherein the impact metric indicates that a plurality of nodes supports the target process.

16 . 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 process machine learning model.

17 . The method of claim 10 , wherein the method further comprises instructions configuring the at least a processor to:
determining a visual element as a function of the visual element data structure; and configuring a user device to display the visual element to a user.

18 . The method of claim 17 , wherein the visual element comprises a remote display device which 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 a remote display device.

Assignees

• The Strategic Coach Inc

Inventors

• Smith Barbara Sue
• Sullivan Daniel J

Classifications

• G06F40/30

  Semantic analysis · CPC title

• G06F16/2428

  Query predicate definition using graphical user interfaces, including menus and forms (G06F16/2423 takes precedence) · CPC title

• G06F16/285 Primary

  Clustering or classification · CPC title