Granted Patent B1

US 12,579,594 · App. 18/957,639 · Granted Mar 17, 2026

Inventors: Blake Browder (Dallas, TX); Joy Figarsky (Little Rock, AR)

Assignee: BH Operations, LLC

G06Q50/205G06F16/951G06F40/284G06F40/30G06Q30/018

Abstract

Claims (73)

1 . A system for dynamically updating data for course generation, the system comprising:

a web crawler operating on a server, wherein the web crawler is configured to:

identify one or more predetermined HTML elements on a plurality of web pages;

identify isolated data as a function of the one or more predetermined HTML elements;

compare the isolated data to a modification baseline which comprises:

encoding, using an encoder, the isolated data;

comparing the encoded isolated data to the modification baseline by identifying a distance metric wherein the encoded isolated data is represented as vectors and identifying the distance metric comprises identifying a cosine similarity between the isolated data and the modification baseline; and

identifying one or more redundant elements within the encoded isolated data as a function of the distance metric; and

generate regulatory data as a function of the isolated data and the comparison which comprises generating the regulatory data as a function of the isolated data and the one or more redundant elements;

at least a processor; and

a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:

receive the regulatory data;

sanitize guideline training data, which comprises a plurality of regulatory data correlated to a plurality of guideline categorizations, to limit noise by:

determining that at least one training data entry of the guideline training data has a signal to noise ratio below a threshold; and

removing the at least one training data entry from the guideline training data to create sanitized guideline training data:

classify the regulatory data to one or more guideline categorizations by:

training a guideline classifier as a function of the sanitized guideline training data until the guideline classifier satisfies a convergence test, wherein sanitizing of the guideline training data accelerates convergence of the guideline classifier; and

classifying the regulatory data to the one or more guideline categorizations using the trained guideline classifier;

identify a plurality of course modules, wherein each course module is associated with at least one guideline;

match the regulatory data to one or more course modules as a function of the one or more guideline categorizations;

modify the one or more course modules as a function of the regulatory data; and

transmit a notification associated with the one or more modified course modules to an end user.

2 . The system of claim 1 , wherein modifying the one or more course modules as a function of the regulatory data comprises:

transmitting the regulatory data and the one or more course modules to a large language model (LLM) communicatively connected to the at least a processor; and

receiving from the LLM, the one or more modified course modules, wherein the LLM is configured to receive the regulatory data and the one or more course modules and output the modified course modules.

3 . The system of claim 1 , the system further comprising a virtual avatar, the virtual avatar configured to output the one or more course modules.

4 . The system of claim 1 , wherein the modification baseline comprises previous isolated data received from a preceding crawl of the web crawler.

5 . The system of claim 1 , wherein training the guideline classifier comprises iteratively training the guideline classifier by adjusting one or more parameter values of the guideline classifier.

6 . The system of claim 1 , wherein comparing the isolated data to the modification baseline comprises:

identifying one or more content breaks within the isolated data;

segmenting the isolated data into a plurality of segmented data as a function of the content breaks;

encoding each of the plurality of segmented data; and

comparing the plurality of encoded, segmented data to the modification baseline.

7 . The system of claim 6 , wherein the web crawler is further configured to append the plurality of encoded segmented data to the modification baseline as a function of the comparison.

8 . The system of claim 1 , wherein identifying the one or more predetermined HTML elements on the plurality of web pages comprises:

transmitting a conditional request to the plurality of web pages;

receiving a response from each web page of the plurality of web pages as a function of the conditional request, wherein each response comprises a modification state of each web page; and

identifying the one or more predetermined HTML elements with the response of each web page.

9 . A method for dynamically updating data for course generation, the method comprising:

identifying, by a web crawler operating on a server, one or more predetermined HTML elements on a plurality of web pages;

identifying, by the web crawler, isolated data as a function of the one or more predetermined HTML elements;

comparing, by the web crawler, the isolated data to a modification baseline which comprises:

encoding, using an encoder, the isolated data;

comparing the encoded isolated data to the modification baseline by identifying a distance metric wherein the encoded isolated data is represented as vectors and identifying the distance metric comprises identifying a cosine similarity between the isolated data and the modification baseline; and

identifying one or more redundant elements within the encoded isolated data as a function of the distance metric;

generating, by the web crawler, regulatory data as a function of the isolated data and the comparison which comprises generating the regulatory data as a function of the isolated data and the one or more redundant elements;

receiving, by at least a processor, the regulatory data;

sanitizing, by the at least a processor, guideline training data, which comprises a plurality of regulatory data correlated to a plurality of guideline categorizations, to limit noise by:

determining that at least one training data entry of the guideline training data has a signal to noise ratio below a threshold; and

removing the at least one training data entry from the guideline training data to create sanitized guideline training data:

classifying, by the at least a processor, the regulatory data to one or more guideline categorizations by:

training a guideline classifier as a function of the sanitized guideline training data until the guideline classifier satisfies a convergence test, wherein sanitizing of the guideline training data accelerates convergence of the guideline classifier; and

classifying the regulatory data to the one or more guideline categorizations using the trained guideline classifier;

identifying, by the at least a processor, a plurality of course modules, wherein each course module is associated with at least one guideline;

matching, by the at least a processor, the regulatory data to one or more course modules as a function of the one or more guideline categorizations;

modifying, by the at least a processor, the one or more course modules as a function of the regulatory data; and

transmitting, by the at least a processor, a notification associated with the one or more modified course modules to an end user.

10 . The method of claim 9 , wherein modifying, by the at least a processor, the one or more course modules as a function of the regulatory data comprises:

transmitting the regulatory data and the one or more course modules to a large language model (LLM) communicatively connected to the at least a processor; and

receiving from the LLM, the modified course modules, wherein the LLM is configured to receive the regulatory data and the one or more course modules and output the modified course modules.

11 . The method of claim 9 , the method further comprising, outputting, by the at least a processor, the one or more course modules through a virtual avatar.

12 . The method of claim 9 , wherein the modification baselines comprises previous isolated data received from a preceding crawl of the web crawler.

13 . The method of claim 9 , wherein training the guideline classifier comprises iteratively training the guideline classifier by adjusting one or more parameter values of the guideline classifier.

14 . The method of claim 9 , wherein comparing, by the web crawler, the isolated data to the modification baseline comprises:

identifying one or more content breaks within the isolated data;

segmenting the isolated data into a plurality of segmented data as a function of the content breaks;

encoding each of the plurality of segmented data; and

comparing the plurality of encoded segmented data to the modification baseline.

15 . The method of claim 14 , the method further comprising appending, by the web crawler the plurality of encoded segmented data to the modification baseline as a function of the comparison.

16 . The method of claim 9 , wherein identifying, by the web crawler, the one or more predetermined HTML elements on the plurality of web pages comprises:

transmitting a conditional request to the plurality of web pages;

receiving a response from each web page of the plurality of web pages as a function of the conditional request, wherein each response comprises a modification state of each web page; and

identifying the one or more predetermined HTML elements with the response of each web page.

Assignments (3)

References Cited (10)