# US 12,182,748 B2 · Apparatus and methods for generating a process enhancement

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- Number: US 12,182,748 B2
- Document ID: US12182748B2
- Status: Granted
- Kind: B2
- Application number: 18/397,888
- Filed: 2023-12-27
- Published: 2024-11-28
- Granted: 2024-12-31
- Earliest claimed: 2023-05-26
- Applicants: Uva IP, LLC
- Inventors: Luis Uva
- Web page: https://patentgrep.com/patent/US12182748B2

## CPC

- G06N 3/08: Learning methods
- G06Q 10/0633: Workflow analysis
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 3/048: Activation functions
- G06N 20/00: Machine learning
- 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

An apparatus and method for generating a process enhancement, the apparatus comprising a memory and a processor configured to receive process data, receive user input, determine a plurality of response modules as a function of the user input, determine a modification target as function of the plurality of response modules, wherein determining the modification target includes calculating an importance score for each response module of the plurality of response modules, ranking each response module of the plurality of response modules as a function of the importance score and determining the modification target as a function of the ranking, identify at least a process modification as a function of the process data and the modification target and generate the process enhancement as a function of the at least a process modification.

## Claims

### Claim 1

An apparatus for generating a process enhancement, the apparatus comprising: at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive
process data;
receive user
input;
determine a plurality of response modules as a function of the user input;
determine a modification target as function of the plurality of response modules; identify at least a process modification as a function of the process data and the
modification target; and
generate a process enhancement as a function of the at least a process modification, wherein generating the process enhancement further comprises:
generating enhancement training data;
training an enhancement machine learning model using the enhancement training data wherein training the enhancement machine learning model comprises preprocessing the enhancement training wherein preprocessing the enhancement training data comprises at least data cleaning;
generating an additional process modification using the enhancement machine learning model; and
generating the process enhancement as a function of the at least a process modification and the additional process modification.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein receiving user input comprises receiving the user input using a chatbot.

### Claim 3 (depends on 1)

The apparatus of claim 1, further configured to generate module training data.

### Claim 4 (depends on 3)

The apparatus of claim 3, wherein the processor is further configured to add the additional process modification to the module training data.

### Claim 5 (depends on 3)

The apparatus of claim 3, wherein identifying the at least a process
modification comprises: generating module training data, wherein the module training data comprises
correlations of process modules to process modifications;
training a response module machine learning model using the module training data, wherein the response module machine learning model receives the modification target and process data as input and output at least a process modification; and
determining the at least a process modification as a function of the response module machine learning model.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein the enhancement machine learning model is a neural network.

### Claim 7 (depends on 1)

The apparatus of claim 1, wherein the processor is further configured to add the process enhancement to the process data.

### Claim 8 (depends on 1)

The apparatus of claim 1, wherein the processor is configured to calculate an importance score as a function of a priority.

### Claim 9 (depends on 1)

The apparatus of claim 1, wherein determining the modification target comprises using a fuzzy set comparison.

### Claim 10 (depends on 1)

The apparatus of claim 1, wherein determining the modification target further comprises: calculating an importance score for each response module of the plurality of response
modules; and
determining the modification target as a function of the calculated importance scores.

### Claim 11

A method for generating a resolution
enhancement plan, the method comprising: receiving, by at least a processor, process data;
receiving, by the at least a processor, user input;
determining, by the at least a processor, a plurality of response modules as a function of the user input;
determining, by the at least a processor, a plurality of response modules as a function of the user input;
determining, by the at least a processor, a modification target as function of the plurality of response modules;
identifying, by the at least a processor, at least a process modification as a function of the process data and the modification target; and
generating, by the at least a processor, a process enhancement as a function of the at least a process modification, wherein generating the process enhancement further comprises:
generating enhancement training data;
training an enhancement machine learning model using the enhancement training data wherein training the enhancement machine learning model comprises preprocessing the enhancement training wherein preprocessing the enhancement training data comprises at least data cleaning;
generating an additional process modification using the enhancement machine learning model; and
generating the process enhancement as a function of the at least a process modification and the additional process modification.

### Claim 12 (depends on 11)

The method of claim 11, wherein receiving user input comprises receiving the user input using a chatbot.

### Claim 13 (depends on 11)

The method of claim 11 further comprising generating module training data.

### Claim 14 (depends on 13)

The method of claim 13, further comprising adding the additional process modification to the module training data.

### Claim 15 (depends on 13)

The method of claim 13, wherein identifying the at least a process modification comprises: generating module training data, wherein the module training data comprises correlations of process modules to process modifications;
training a response module machine learning model using the module training data, wherein the response module machine learning model receives the modification target and process data as input and output at least a process modification; and
determining the at least a process modification as a function of the response module machine learning model.

### Claim 16 (depends on 11)

The method of claim 11, wherein the enhancement machine learning model is a neural network.

### Claim 17 (depends on 11)

The method of claim 11, wherein the processor is further configured to add the process enhancement to the process data.

### Claim 18 (depends on 11)

The method of claim 11, wherein the processor is configured to calculate an importance score as a function of a priority.

### Claim 19 (depends on 11)

The method of claim 11, wherein determining the modification target comprises using a fuzzy set comparison.

### Claim 20 (depends on 11)

The method of claim 11, wherein determining the modification target further comprises:
calculating an importance score for each response module of the plurality of response modules; and
determining the modification target as a function of the calculated importance scores.