SYSTEMS AND METHODS FOR CONTENT DISTRIBUTION USING MACHINE LEARNING

Organization Name

ADOBE INC.

Inventor(s)

Rebecca Dorothy West of Auburn CA (US)

Lauren Dest of Austin TX (US)

Mihir Naware of Redwood City CA (US)

Elliot Axel Patrick Puzenat of Le Plessis Robinson (FR)

Stephen Becigneul of Levallois-Perret (FR)

Alexis Tessier of Eckwersheim, Bas-Rhin (FR)

Suman Basetty of Fremont CA (US)

Kimberly K. Lenox of Orinda CA (US)

Anil Kamath of Los Altos HIlls CA (US)

SYSTEMS AND METHODS FOR CONTENT DISTRIBUTION USING MACHINE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18477693 titled 'SYSTEMS AND METHODS FOR CONTENT DISTRIBUTION USING MACHINE LEARNING

Simplified Explanation:

This patent application describes a method, computer-readable medium, apparatus, and system for content distribution using machine learning. The system receives a prompt, generates a campaign brief based on the prompt, and delivers content to users via a user experience platform.

The system uses a machine learning model to generate campaign briefs based on prompts.
Campaign briefs include user segment identification, communication channel identification, and content elements.
The machine learning model is trained using a variety of campaign briefs to improve accuracy.
Content is delivered to users based on the campaign brief via a user experience platform.

Key Features and Innovation:

Utilization of machine learning for content distribution.
Personalized content delivery based on user segments and communication channels.
Training of machine learning model with diverse campaign briefs for improved performance.

Potential Applications:

This technology can be applied in digital marketing, advertising, and content distribution industries.

Problems Solved:

Efficient and targeted content distribution.
Personalized user experience.
Improved campaign performance.

Benefits:

Enhanced user engagement.
Higher conversion rates.
Cost-effective marketing strategies.

Commercial Applications:

The technology can be used in digital marketing campaigns, online advertising, and customer engagement strategies to optimize content distribution and improve campaign effectiveness.

Prior Art:

Potential areas to search for prior art related to this technology include machine learning in marketing, content distribution systems, and personalized advertising platforms.

Frequently Updated Research:

Stay updated on advancements in machine learning algorithms for content distribution, user segmentation techniques, and campaign optimization strategies.

Questions about Content Distribution Technology:

1. How does machine learning improve content distribution strategies?

  - Machine learning enhances content distribution by analyzing user behavior, preferences, and trends to deliver personalized content effectively.

2. What are the key components of a campaign brief in this technology?

  - A campaign brief includes user segment identification, communication channel selection, and content elements for targeted content delivery.

Original Abstract Submitted

A method, non-transitory computer readable medium, apparatus, and system for content distribution are described. An embodiment of the present disclosure includes receiving, by a machine learning model, a prompt. The machine learning model generates a campaign brief based on the prompt. The campaign brief includes an identification of a user segment, an identification of a communication channel, and a content element. The machine learning model is trained using training data including a plurality of campaign briefs. A user experience platform provides content corresponding to the content element to a user from the user segment via the communication channel based on the campaign brief.