18055238. GENERATING EDITABLE EMAIL COMPONENTS UTILIZING A CONSTRAINT-BASED KNOWLEDGE REPRESENTATION simplified abstract (ADOBE INC.)

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GENERATING EDITABLE EMAIL COMPONENTS UTILIZING A CONSTRAINT-BASED KNOWLEDGE REPRESENTATION

Organization Name

ADOBE INC.

Inventor(s)

Yeuk-yin Chan of New York NY (US)

Andrew Thomson of Moraga CA (US)

Caroline Kim of San Francisco CA (US)

Cole Connelly of New York NY (US)

Eunyee Koh of San Jose CA (US)

Michelle Lee of San Francisco CA (US)

Shunan Guo of San Jose CA (US)

GENERATING EDITABLE EMAIL COMPONENTS UTILIZING A CONSTRAINT-BASED KNOWLEDGE REPRESENTATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18055238 titled 'GENERATING EDITABLE EMAIL COMPONENTS UTILIZING A CONSTRAINT-BASED KNOWLEDGE REPRESENTATION

Simplified Explanation

The present disclosure involves systems, methods, and computer-readable media that generate editable email components using an Answer Set Programming (ASP) model with hard and soft constraints. The systems extract facts from email fragments, determine rows or columns of email cells using ASP hard constraints, and identify editable email component classes using ASP soft constraints.

  • Email components are generated from email fragments using an ASP model with hard and soft constraints.
  • Facts are extracted from email fragments to inform the ASP model.
  • Rows or columns defining email cells are determined using ASP hard constraints.
  • Editable email component classes are identified using ASP soft constraints with classification weights.

Potential Applications

This technology could be applied in:

  • Email automation systems
  • Content generation tools
  • Natural language processing applications

Problems Solved

This technology helps in:

  • Streamlining email composition processes
  • Enhancing email content quality
  • Improving user experience in email editing

Benefits

The benefits of this technology include:

  • Increased efficiency in email creation
  • Enhanced customization options for email components
  • Improved accuracy in email content generation

Potential Commercial Applications

This technology could be commercially apply in:

  • Email marketing platforms
  • Customer relationship management (CRM) systems
  • Business communication tools

Possible Prior Art

One possible prior art could be the use of machine learning algorithms for email content generation. However, the specific utilization of an ASP model with hard and soft constraints for editable email components may be a novel approach.

Unanswered Questions

How does this technology compare to traditional email editing tools?

This article does not provide a direct comparison between this technology and traditional email editing tools. It would be interesting to know the specific advantages or disadvantages of using an ASP model for email component generation compared to conventional methods.

What are the potential limitations of implementing this technology in real-world email systems?

The article does not address the potential challenges or constraints of integrating this technology into existing email platforms. Understanding the practical implications and limitations of deploying this system could be crucial for its adoption in the industry.


Original Abstract Submitted

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates editable email components by utilizing an Answer Set Programming (ASP) model with hard and soft constraints. For instance, in one or more embodiments, the disclosed systems generate editable email components from email fragments of an email file utilizing an Answer Set Programming (ASP) model. In particular, the disclosed systems extract facts for the ASP model from the email file. In addition, the disclosed systems determine rows or columns defining cells of the email file utilizing ASP hard constraints defined by a first set of ASP atoms corresponding to the facts. Moreover, the disclosed systems determine editable email component classes for the email fragments utilizing ASP soft constraints defined by ASP classification weights and a second set of ASP atoms corresponding to the facts.

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