Granted Patent B1

US 12,499,172 · App. 19/255,093 · Granted Dec 16, 2025

Inventors: Andrius Grybauskas (Kaunas, LT); Arminas Maciulskas (Kaunas, LT); Tomas Rasymas (Kaunas, LT)

Assignee: HOSTINGER operations, UAB

G06F16/958

Abstract

Claims (100)

1 . A method for generating a website, comprising:

receiving a brand name and a website description;

extracting, using a language model, user preferences from the website description;

determining, using the language model, a website structure including pages and sections based on the user preferences, wherein determining the website structure comprises:

determining, using the language model, a website type based on the website description;

selecting available sections from a predefined section database based on the website type; and

arranging the available sections into the pages based on the website type;

generating website content for the pages and the sections using the language model based on the brand name and the website description;

assembling the website structure and the website content into a data structure representing a hierarchical arrangement of the pages and the sections; and

rendering the data structure as a browser-compatible website.

2 . The method of claim 1 , further comprising:

determining images based on the user preferences, wherein the images are assembled into the data structure.

3 . The method of claim 2 , wherein determining the images based on the user preferences comprises:

generating, using the language model, keywords based on the website description;

converting, using the language model, the keywords into vector embeddings;

performing a vector search using the vector embeddings to identify relevant images in an image database,

wherein the vector embeddings are compared to image embeddings, and

wherein each image embedding includes a first embedding representing a content description of the each image and a second embedding representing visual features of the each image; and

selecting at least one of the relevant images for inclusion in the website.

4 . The method of claim 1 , wherein generating the website content comprises:

identifying content placeholders within the sections;

generating, using the language model, content for each identified placeholder based on the brand name and the website description; and

generating search engine optimization metadata for each page based on the content and identified user preferences.

5 . The method of claim 1 , wherein extracting the user preferences comprises:

analyzing, using the language model, the website description to identify explicit user preferences for at least one of: colors, fonts, layout, or content style; and

generating, using the language model, implicit preferences based on business characteristics identified in the website description.

6 . The method of claim 1 , wherein the data structure comprises:

page objects representing each page of the website;

section objects representing sections within each page; and

element objects representing individual elements within each section, wherein each element object includes positioning data.

7 . The method of claim 1 , wherein rendering the data structure comprises:

loading website components based on the data structure;

generating a Document Object Model (DOM) based on the website components; and

rendering the DOM in a web browser.

8 . A system for generating a website, comprising:

a memory subsystem; and

processing circuitry, the processing circuitry configured to execute instructions stored in the memory subsystem to:

receive a brand name and a website description;

extract, using a language model, user preferences from the website description;

determine, using the language model, a website structure including pages and sections based on the user preferences, wherein to determine the website structure comprises instructions to:

determine, using the language model, a website type based on the website description;

select available sections from a predefined section database based on the website type; and

arrange the available sections into the pages based on the website type;

generate website content for the pages and the sections using the language model based on the brand name and the website description;

assemble the website structure and the website content into a data structure representing a hierarchical arrangement of the pages and the sections; and

render the data structure as a browser-compatible website.

9 . The system of claim 8 , wherein the processing circuitry further configured to execute instructions stored in the memory subsystem to:

determine images based on the user preferences, wherein the images are assembled into the data structure.

10 . The system of claim 9 , wherein to determine the images based on the user preferences comprises instructions to:

generate, using the language model, keywords based on the website description;

convert, using the language model, the keywords into vector embeddings;

perform a vector search using the vector embeddings to identify relevant images in an image database,

wherein the vector embeddings are compared to image embeddings, and

wherein each image embedding includes a first embedding representing a content description of the each image and a second embedding representing visual features of the each image; and

select at least one of the relevant images for inclusion in the website.

11 . The system of claim 8 , wherein to generate the website content comprises instructions to:

identify content placeholders within the sections;

generate, using the language model, content for each identified placeholder based on the brand name and the website description; and

generate search engine optimization metadata for each page based on the content and identified user preferences.

12 . The system of claim 8 , wherein to extract the user preferences comprises instructions to:

analyze, using the language model, the website description to identify explicit user preferences for at least one of: colors, fonts, layout, or content style; and

generate, using the language model, implicit preferences based on business characteristics identified in the website description.

13 . The system of claim 8 , wherein the data structure comprises:

page objects representing each page of the website;

section objects representing sections within each page; and

element objects representing individual elements within each section, wherein each element object includes positioning data.

14 . The system of claim 8 , wherein to render the data structure comprises instructions to:

load website components based on the data structure;

generate a Document Object Model (DOM) based on the website components; and

render the DOM in a web browser.

15 . One or more non-transitory computer readable media storing instructions operable to cause one or more processors to perform operations for generating a website, the operations comprising:

receiving a brand name and a website description;

extracting, using a language model, user preferences from the website description;

determining, using the language model, a website structure including pages and sections based on the user preferences, determining the website structure comprises:

determining, using the language model, a website type based on the website description;

selecting available sections from a predefined section database based on the website type; and

arranging the available sections into the pages based on the website type;

generating website content for the pages and the sections using the language model based on the brand name and the website description;

assembling the website structure and the website content into a data structure representing a hierarchical arrangement of the pages and the sections; and

rendering the data structure as a browser-compatible website.

16 . The one or more non-transitory computer readable media of claim 15 , the operations further comprising:

determining images based on the user preferences, wherein the images are assembled into the data structure.

17 . The one or more non-transitory computer readable media of claim 16 , wherein determining the images based on the user preferences comprises:

generating, using the language model, keywords based on the website description;

converting, using the language model, the keywords into vector embeddings;

performing a vector search using the vector embeddings to identify relevant images in an image database,

wherein the vector embeddings are compared to image embeddings, and

wherein each image embedding includes a first embedding representing a content description of the each image and a second embedding representing visual features of the each image; and

selecting at least one of the relevant images for inclusion in the website.

18 . The one or more non-transitory computer readable media of claim 15 , wherein extracting the user preferences comprises:

analyzing, using the language model, the website description to identify explicit user preferences for at least one of: colors, fonts, layout, or content style; and

generating, using the language model, implicit preferences based on business characteristics identified in the website description.

19 . The one or more non-transitory computer readable media of claim 15 , wherein the data structure comprises:

page objects representing each page of the website;

section objects representing sections within each page; and

element objects representing individual elements within each section, wherein each element object includes positioning data.

20 . The one or more non-transitory computer readable media of claim 15 , wherein rendering the data structure comprises:

loading website components based on the data structure;

generating a Document Object Model (DOM) based on the website components; and

rendering the DOM in a web browser.

Assignments (1)

Continuity (1)

References Cited (28)