IP Library › Granted Patent US 11,481,550
Granted Patent B2
US 11,481,550 · App. 15/807,431 · Granted Oct 25, 2022

Generating presentation slides with distilled content

Inventors: Vishnu Sivaji (New York, NY); Steven Joseph Saviano (Brooklyn, NY); Andrea Dulko (Brooklyn, NY)
Assignee: Google LLC
G06F40/258G06F16/345G06F40/103G06F40/106G06F40/131G06F40/186G06N20/00G06F40/151G06F40/177
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Quick Facts
Patent No.
US 11,481,550
App. No.
15/807,431
Granted
Oct 25, 2022
Kind
B2
Abstract

A method for generating presentation slides with distilled content including receiving one or more data files as source material for slide generation, obtaining content from the one or more data files for a slide of a slide presentation, identifying a layout template for the slide based on the content, and distilling the content into distilled content to generate a presentation visualization item based on the distilled content. The distilled content may include a subset of the content. The method may also include generating the slide based on the presentation visualization item and the layout template.

Claims (49)

1. A method for generating presentation slides with distilled content, comprising:

providing a user interface (UI) for presentation to a user, the UI displaying a data file having a plurality of portions of content, and a slide generation UI element allowing the user to request slide generation using the data file as source material;

receiving, via the UI, a user selection of the slide generation UI element;

receiving, via the UI, an indication of a user selection of a first portion of content of the plurality of portions of content;

identifying a plurality of logical breakpoints in the first portion of content based on at least one of a format or size of a plurality of content items of the first portion of content;

determining, based on the identified plurality of logical breakpoints, a set of slides to be included in a slide presentation;

identifying a layout template for each of the set of slides based on content of the plurality of content items of the first portion of content;

applying a trained machine learning model to the plurality of content items of the first portion of content to obtain an output of the trained machine learning model, the output of the trained machine learning model indicating one or more distilled content items and a format for generating a presentation visualization item comprising the one or more distilled content items, wherein the one or more distilled content items include a subset of the plurality of content items of the first portion of content, and wherein the trained machine learning model is trained using (i) training inputs identifying a set of training files and (ii) corresponding target outputs identifying summaries for the training files, wherein a corresponding target output further identifies a format for a summary of a respective training file, and wherein the trained machine learning model is trained to learn what text segment to include in a distilled content item and what format to use for a presentation visualization item comprising the distilled content item; and

generating the set of slides based on each identified layout template and the presentation visualization item, the presentation visualization item having the indicated format.

2. The method of claim 1 , further comprising:

receiving an indication of another user selection of a second portion of content of the plurality of portions of content of the data file; and

distilling a plurality of content items of the second portion of content into one or more additional distilled content items to generate a second presentation visualization item, wherein each of the one or more additional distilled content items includes a subset of the plurality of content items of the second portion of content, and wherein at least one slide of the set of slides is further generated based on the second presentation visualization item.

3. The method of claim 1 , wherein distilling the plurality of content items of the first portion of content is of a first type.

4. The method of claim 1 , wherein the plurality of content items of the first portion of content comprises a first set of sentences; and the one or more distilled content items comprise a second set of sentences that includes fewer sentences than the first set of sentences, and wherein the generated presentation visualization item comprises a list that is based on the second set of sentences.

5. The method of claim 1 , wherein the plurality of content items of the first portion of content comprises a data table; and the one or more distilled content items comprise a range of data from the data table, and wherein the generated presentation visualization item comprises a data chart that is based on the range of data.

6. The method of claim 1 , wherein the plurality of content items of the first portion of content comprises an image, and wherein the presentation visualization item comprises the image.

7. The method of claim 1 , further comprising:

receiving an interaction with the set of slides; and

using the interaction for a heuristic rule to be applied to generation of a subsequent set of slides.

8. The method of claim 1 , wherein the data file comprises at least one of a text document, a database file, a spreadsheet, a data table, a video file, or an image file.

9. The method of claim 1 , further comprising:

setting text of a parent header in the data file as a title for the respective layout template of the slide; and

setting the one or more distilled content items including text associated with the parent header as a body for the layout template.

10. A non-transitory, computer-readable medium storing instructions that, when executed by a processing device, cause the processing device to:

provide a user interface (UI) for presentation to a user, the UI displaying a data file having a plurality of portions of content, and a slide generation UI element allowing the user to request slide generation using the data file as source material;

receive, via the UI, a user selection of the slide generation UI element;

receive, via the UI, an indication of a user selection of a first portion of content of the plurality of portions of content;

identify a plurality of logical breakpoints in the first portion of content based on at least one of a format or size of a plurality of content items of the first portion of content;

determine, based on the identified plurality of logical breakpoints, a set of slides to be included in a slide presentation;

identify a layout template for each of the set of slides based on content of the plurality of content items of the first portion of content;

apply a trained machine learning model to the plurality of content items of the first portion of content to obtain an output of the trained machine learning model, the output of the trained machine learning model indicating one or more distilled content items and a format for generating a presentation visualization item comprising the one or more distilled content items, wherein the one or more distilled content items include a subset of the plurality of content items of the first portion of content, and wherein the trained machine learning model is trained using (i) training inputs identifying a set of training files and (ii) corresponding target outputs identifying summaries for the training files, wherein a corresponding target output further identifies a format for a summary of a respective training file, and wherein the trained machine learning model is trained to learn what text segment to include in a distilled content item and what format to use for a presentation visualization item comprising the distilled content item; and

generate the set of slides based on each identified layout template and the presentation visualization item, the presentation visualization item having the indicated format.

11. The computer-readable medium of claim 10 , wherein the plurality of content items of the first portion of content is of a first type.

12. The computer-readable medium of claim 10 , the plurality of content items of the first portion of content comprises a first set of sentences; and the one or more distilled content items comprise a second set of sentences that includes fewer sentences than the first set of sentences, wherein the generated presentation visualization item comprises a list that is based on the second set of sentences.

13. The computer-readable medium of claim 10 , wherein the plurality of content items of the first portion content comprises a data table; and the one or more distilled content items comprise a range of data from the data table, and wherein the generated presentation visualization item comprises a data chart that is based on the range of data.

14. The computer-readable medium of claim 10 , wherein the plurality of content items of the first portion of content comprises an image; and wherein the presentation visualization item comprises the image.

15. A system comprising:

a memory device storing instructions; and

a processing device coupled to the memory device, wherein executing the instructions causes the processing device to:

provide a user interface (UI) for presentation to a user, the UI displaying a data file having a plurality of portions of content, and a slide generation UI element allowing the user to request slide generation using the data file as source material;

receive, via the UI, a user selection of the slide generation UI element;

receive, via the UI, an indication of a user selection of a first portion of content of the plurality of portions of content;

identify a plurality of logical breakpoints in the first portion of content based on at least one of a format or size of a plurality of content items of the first portion of content;

determine, based on the identified plurality of logical breakpoints, a set of slides to be included in a slide presentation;

identify a layout template for each of the set of slides based on content of the plurality of content items of the first portion of content;

apply a trained machine learning model to the plurality of content items of the first portion of content to obtain an output of the trained machine learning model, the output of the trained machine learning model indicating one or more distilled content items and a format for generating a presentation visualization item comprising the one or more distilled content items, wherein the one or more distilled content items include a subset of the plurality of content items of the first portion of content, and wherein the trained machine learning model is trained using (i) training inputs identifying a set of training files and (ii) corresponding target outputs identifying summaries for the training files, wherein a corresponding target output further identifies a format for a summary of a respective training file, and wherein the trained machine learning model is trained to learn what text segment to include in a distilled content item and what format to use for a presentation visualization item comprising the distilled content item; and

generate the set of slides based on each identified layout template and the presentation visualization item, the presentation visualization item having the indicated format.

16. The system of claim 15 , the plurality of content items of the first portion of content comprises a first set of sentences; and the one or more distilled content items comprise a second set of sentences that includes fewer sentences than the first set of sentences, and wherein the generated presentation visualization item comprises a list that is based on the second set of sentences.

17. The system of claim 15 , wherein the plurality of content items of the first portion content comprises a data table; and the one or more distilled content items comprise a range of data from the data table, and wherein the generated presentation visualization item comprises a data chart that is based on the range of data.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 9, 2017
From: SIVAJI, VISHNU; SAVIANO, STEVEN JOSEPH; DULKO, ANDREA
To: GOOGLE LLC
Reel/Frame 044085/0638 →
Continuity (2)
Provisional Application 62420263 · Nov 10, 2016
Related Publication 20180129634A1 · May 10, 2018
Cited By (3)
US 12,367,303 US 12,626,048 US 12,718,000