# US 2026/0087698 A1 · Systems and methods for generating custom courses using machine learning

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- Number: US 2026/0087698 A1
- Document ID: US20260087698A1
- Status: Published application
- Kind: A1
- Application number: 18/894,309
- Filed: 2024-09-24
- Published: 2026-03-26
- Earliest claimed: 2024-09-24
- Assignees: Constructor Technology AG; Constructor Education and Research Genossenschaft
- Applicants: Constructor Technology AG; Constructor Education and Research Genossenschaft
- Inventors: Sergey ULASEN; Andrey ADASCHIK; Ilya BAIMETOV; Alexander TORMASOV; Serg BELL; Stanislav PROTASOV; Nikolay DOBROVOLSKIY; Laurent DEDENIS
- Web page: https://patentgrep.com/patent/US20260087698A1

## CPC

- G06T 11/60: Creating or editing images; Combining images with text
- G06F 3/0482: Interaction with lists of selectable items, e.g. menus
- G06F 3/0484: for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
- G06F 3/04847: Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
- G06F 9/451: Execution arrangements for user interfaces
- G06F 16/9538: Presentation of query results
- G06F 40/166: Editing, e.g. inserting or deleting
- G09B 7/04: characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question or supplying a further explanation
- G06T 2200/24: involving graphical user interfaces [GUIs]
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)

## Abstract

disclosed herein are systems and method for generating custom courses using machine learning. a method may include: receiving, via a user interface (UI), a first user selection of a topic; retrieving content associated with the topic from a database of reference materials; generating, for display on the GUI, the content in a default organizational scheme; receiving, via the GUI, a second user selection to organize the content in a custom organizational scheme of a preferred duration for consuming the topic; determining, by a hardware processor, an amount of time needed by a user to consume the content in the default organization scheme; and automatically updating the content displayed in the UI in accordance with the custom organizational scheme based on the preferred duration and the amount of time.

## Claims

### Claim 1

A method for generating custom content using machine learning, the method comprising:
receiving, via a user interface (UI), a first user selection of a topic from a plurality of topics;
retrieving content associated with the topic from a database of reference materials, wherein the content comprises text and visuals summarizing one or more reference materials from the database and is organized in a plurality of sub-topics related to the topic;
generating, for display on the UI, the content in a default organizational scheme;
receiving, via the UI, a second user selection to organize the content in a custom organizational scheme of a preferred duration for consuming the topic;
determining, by a hardware processor, an amount of time needed by a user to consume the content in the default organization scheme; and
automatically updating, using machine learning, the content displayed in the UI in accordance with the custom organizational scheme by:
adding additional content to the content when the amount of time is less than the preferred duration; and
filtering out existing content from the content when the amount of time is greater than the preferred duration.

### Claim 2 (depends on 1)

The method of claim 1, wherein automatically updating the content displayed in the UI in accordance with the custom organizational scheme comprises:
executing a first machine learning algorithm trained to generate, for an input duration, a word limit of text in the content, a media limit of graphics in content, and an assessment limit of questions in the content; and
executing a second machine learning algorithm trained to summarize the one or more reference materials into the content comprising an updated amount of text capped at the word limit, an updated amount of graphics capped at the media limit, and an updated amount of questions capped at the assessment limit.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
generating, using another machine learning algorithm, a script for an avatar configured to orally present the content, wherein a length of the script is based on a size of the content and a language selected for use by the avatar.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
receiving, via the UI, a third user selection to update the custom organizational scheme based on a preferred difficulty level for comprehending the topic;
determining, by the hardware processor, a respective difficulty level of each reference material in the database;
retrieving updated content from at least one reference material with a difficulty level that matches the preferred difficulty level; and
automatically updating the content displayed in the UI to the updated content in accordance with the custom organizational scheme.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
receiving, via the UI, a fourth user selection to update the custom organizational scheme based on a preferred subset of sub-topics to include from a plurality of topics; and
automatically updating the content displayed in the UI in accordance with the custom organizational scheme by:
filtering out the existing content from the content, wherein the existing content comprises information unrelated and/or partially related to the preferred subset of sub-topics; and
adding the additional content to the content to match the preferred duration, wherein the additional content comprises information related to the preferred subset of sub-topics.

### Claim 6 (depends on 1)

The method of claim 1, further comprising:
receiving, via the UI, a reference material or a link to the reference material to use for generating the content;
adding the reference material to the database, wherein the one or more reference materials comprise the reference material received via the UI.

### Claim 7 (depends on 1)

The method of claim 1, wherein information of each sub-topic in the plurality of sub-topics is outputted in a different visual panel.

### Claim 8 (depends on 7)

The method of claim 7, wherein a visual panel of a respective sub-topic includes options to adjust a duration and a difficulty level of the respective sub-topic.

### Claim 9 (depends on 7)

The method of claim 7, wherein a visual panel of a respective sub-topic includes an option to provide a reference material from which information about the respective sub-topic is exclusively extracted.

### Claim 10 (depends on 1)

The method of claim 1, wherein the content is a course and wherein the custom organizational scheme is a custom syllabus indicative of an order in which the plurality of sub-topics are presented, mutual references, and an amount of text, graphics, and assessments for each sub-topic.

### Claim 11 (depends on 1)

The method of claim 1, further comprising:
receiving, via the UI, a new topic that is not included in the plurality of topics and at least one reference material that describes the new topic;
parsing the at least one reference material to identify a plurality of new sub-topics related to the new topic; and
extracting, from the at least one reference material, information about each of the plurality of new sub-topics.

### Claim 12

A system for updating a user interface displaying content related to a topic based on user preference, comprising:
at least one memory;
at least one hardware processor coupled with the at least one memory and configured, individually or in combination, to:
receive, via a user interface (UI), a first user selection of a topic from a plurality of topics;
retrieve content associated with the topic from a database of reference materials, wherein the content comprises text and visuals summarizing one or more reference materials from the database and is organized in a plurality of sub-topics related to the topic;
generate, for display on the UI, the content in a default organizational scheme;
receive, via the UI, a second user selection to organize the content in a custom organizational scheme of a preferred duration for consuming the topic;
determine an amount of time needed by a user to consume the content in the default organization scheme; and
automatically update, using machine learning, the content displayed in the UI in accordance with the custom organizational scheme by:
adding additional content to the content when the amount of time is less than the preferred duration; and
filtering out existing content from the content when the amount of time is greater than the preferred duration.

### Claim 13 (depends on 12)

The system of claim 12, wherein the at least one hardware processor is configured to automatically updating the content displayed in the UI in accordance with the custom organizational scheme by:
executing a first machine learning algorithm trained to generate, for an input duration, a word limit of text in the content, a media limit of graphics in content, and an assessment limit of questions in the content; and
executing a second machine learning algorithm trained to summarize the one or more reference materials into the content comprising an updated amount of text capped at the word limit, an updated amount of graphics capped at the media limit, and an updated amount of questions capped at the assessment limit.

### Claim 14 (depends on 12)

The system of claim 12, wherein the at least one hardware processor is configured to:
generate, using another machine learning algorithm, a script for an avatar configured to orally present the content, wherein a length of the script is based on a size of the content and a language selected for use by the avatar.

### Claim 15 (depends on 12)

The system of claim 12, wherein the at least one hardware processor is configured to:
receive, via the UI, a third user selection to update the custom organizational scheme based on a preferred difficulty level for comprehending the topic;
determine a respective difficulty level of each reference material in the database;
retrieve updated content from at least one reference material with a difficulty level that matches the preferred difficulty level; and
automatically update the content displayed in the UI to the updated content in accordance with the custom organizational scheme.

### Claim 16 (depends on 12)

The system of claim 12, wherein the at least one hardware processor is configured to:
receive, via the UI, a fourth user selection to update the custom organizational scheme based on a preferred subset of sub-topics to include from a plurality of topics; and
automatically update the content displayed in the UI in accordance with the custom organizational scheme by:
filtering out the existing content from the content, wherein the existing content comprises information unrelated and/or partially related to the preferred subset of sub-topics; and
adding the additional content to the content to match the preferred duration, wherein the additional content comprises information related to the preferred subset of sub-topics.

### Claim 17 (depends on 12)

The system of claim 12, wherein the at least one hardware processor is configured to:
receive, via the UI, a reference material or a link to the reference material to use for generating the content;
add the reference material to the database, wherein the one or more reference materials comprise the reference material received via the GUI.

### Claim 18 (depends on 12)

The system of claim 12, wherein information of each sub-topic in the plurality of sub-topics is outputted in a different visual panel.

### Claim 19 (depends on 18)

The system of claim 18, wherein a visual panel of a respective sub-topic includes options to adjust a duration and a difficulty level of the respective sub-topic.

### Claim 20

A non-transitory computer readable medium storing thereon computer executable instructions for updating a user interface displaying content related to a topic based on user preference, including instructions for:
receiving, via a user interface (UI), a first user selection of a topic from a plurality of topics;
retrieving content associated with the topic from a database of reference materials, wherein the content comprises text and visuals summarizing one or more reference materials from the database and is organized in a plurality of sub-topics related to the topic;
generating, for display on the GUI, the content in a default organizational scheme;
receiving, via the GUI, a second user selection to organize the content in a custom organizational scheme of a preferred duration for consuming the topic;
determining, by a hardware processor, an amount of time needed by a user to consume the content in the default organization scheme; and
automatically updating, using machine learning, the content displayed in the UI in accordance with the custom organizational scheme by:
adding additional content to the content when the amount of time is less than the preferred duration; and
filtering out existing content from the content when the amount of time is greater than the preferred duration.