US 2019/0377785 A1 · AUTOMATICALLY GENERATING DIGITAL ENTERPRISE CONTENT VARIANTS

Abstract

This disclosure relates to methods, non-transitory computer readable media, and systems that, based on a sparse textual segment, can use machine learning models to generate document variants that are both conforming to digital content guidelines and uniquely tailored for distribution to client devices of specific audiences via specific delivery channels. To create such variants, in some embodiments, the methods, non-transitory computer readable media, and systems generate suggested modifications to a draft document that correspond to features of content-guideline-conforming documents. Additionally, or alternatively, in certain implementations, the disclosed methods, non-transitory computer readable media, and systems generate suggested modifications to a draft document that correspond to features of audience-channel-specific documents.

Claims20

Dependent claims

In a digital medium environment for generating and implementing digital content campaigns across computer networks, a computer-implemented method for machine generating digital-enterprise-content variants from textual segments, comprising:

receiving, from a publisher device, user input indicating a textual segment;
composing a draft document from digital-content-campaign documents identified from a query based on the textual segment;
performing a step for proposing suggested modifications to the draft document based on content-guideline-conforming documents and audience-channel-specific documents;
providing, to the publisher device, selectable options corresponding to the suggested modifications to the draft document;
and
revising the draft document to incorporate one or more modifications of the suggested modifications based on a user selection of one or more selectable options from the selectable options.

3 more elements

A non-transitory computer readable storage medium comprising instructions that, when executed by at least one processor, cause a system to:

in response to identifying user input from a publisher device indicating a textual segment, compose a draft document from digital-content-campaign documents identified from a query based on the textual segment;
generate a first suggested modification to the draft document, for display on the publisher device, by comparing the draft document with content-guideline-conforming documents;
in response to identifying user input from the publisher device indicating at least one of an audience or a delivery channel, generate a second suggested modification to the draft document, for display on the publisher device, by comparing the draft document with audience-channel-specific documents;
and
based on a user selection of a modification from at least one of the first suggested modification or the second suggested modification, modify the draft document to incorporate the modification from the first suggested modification and the second suggested modification.

2 more elements

A system for machine generating digital-enterprise-content variants from textual segments comprising:

at least one processor;
at least one non-transitory computer memory comprising content-guideline-conforming documents, audience-channel-specific documents, and instructions that, when executed by at least one processor, cause the system to:
generate, for display on a publisher device, a first suggested modification to a draft document by:
utilizing a machine learning model to determine an attribute dimension score for the draft document based on textual features of the draft document and an attribute-dimension-score distribution for the content-guideline-conforming documents based on textual features of the content-guideline-conforming documents;
and
generating the first suggested modification based on a comparison of the attribute dimension score and the attribute-dimension-score distribution;
and
generate, for display on the publisher device, a second suggested modification to the draft document by comparing a communication tone score for the draft document with communication tone scores for the audience-channel-specific documents;
and
based on a user selection of a modification from at least one of the first suggested modification or the second suggested modification, modify the draft document to incorporate the modification from the first suggested modification and the second suggested modification.

7 more elements

Dependencies are inferred from the claim text.

Automatically generating digital enterprise content variants

Life of this patent

Automatically generating digital enterprise content variants

Life of this patent

Abstract

Claims20