18424017. USING MACHINE LEARNING FOR ICONOGRAPHY RECOMMENDATIONS simplified abstract (Capital One Services, LLC)
Contents
- 1 USING MACHINE LEARNING FOR ICONOGRAPHY RECOMMENDATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 USING MACHINE LEARNING FOR ICONOGRAPHY RECOMMENDATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does the system ensure the recommended visual components align with the organization's branding and messaging?
- 1.11 What measures are in place to address potential biases in the machine learning model's recommendations?
- 1.12 Original Abstract Submitted
USING MACHINE LEARNING FOR ICONOGRAPHY RECOMMENDATIONS
Organization Name
Inventor(s)
Briana Shaver of Flower Mound TX (US)
Mark Morrison of Plano TX (US)
USING MACHINE LEARNING FOR ICONOGRAPHY RECOMMENDATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18424017 titled 'USING MACHINE LEARNING FOR ICONOGRAPHY RECOMMENDATIONS
Simplified Explanation
The abstract describes a recommendation system that utilizes a machine learning model trained on organization-specific data to provide visual components to accompany text input.
- The recommendation system inputs text into a machine learning model trained on organization-specific data.
- The machine learning model uses natural language processing and sentiment detection to parse the text.
- Based on the text, the machine learning model provides recommendations for visual components stored in the organization's database.
- The recommendation system generates an initial draft including the text and recommended visual components.
Potential Applications
This technology could be applied in content creation tools, marketing automation platforms, and personalized recommendation systems.
Problems Solved
This technology helps streamline the process of creating visually appealing content by providing relevant visual components based on the text input.
Benefits
The system saves time and effort by automating the selection of visual components that complement the text, resulting in more engaging and effective content.
Potential Commercial Applications
"Enhancing Content Creation with AI-Driven Visual Recommendations"
Possible Prior Art
One possible prior art could be content recommendation systems that suggest images based on text input, but not specifically trained on organization-specific data.
Unanswered Questions
How does the system ensure the recommended visual components align with the organization's branding and messaging?
The abstract does not provide details on how the system ensures the visual components recommended are in line with the organization's branding and messaging. This could be a crucial aspect for organizations looking to maintain consistency in their content.
What measures are in place to address potential biases in the machine learning model's recommendations?
The abstract does not mention any steps taken to address potential biases in the machine learning model's recommendations. It would be important to understand how the system mitigates biases to ensure fair and accurate recommendations.
Original Abstract Submitted
In some implementations, a recommendation system may input text into a machine learning model that was trained using input specific to an organization associated with the text and was refined using input specific to a portion of the organization. The recommendation system may receive, from the machine learning model, a recommendation indicating one or more visual components, stored in a database associated with the organization, to use with the text. The machine learning model may use natural language processing and sentiment detection to parse the text. Accordingly, the recommendation system may receive the one or more visual components from the database and generate an initial draft including the text and the one or more visual components.
