Doordash, inc. (20240184436). IMAGE SELECTION USING MACHINE LEARNING simplified abstract
Contents
- 1 IMAGE SELECTION USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IMAGE SELECTION USING MACHINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Simplified Explanation
- 1.6 Key Features and Innovation
- 1.7 Potential Applications
- 1.8 Problems Solved
- 1.9 Benefits
- 1.10 Commercial Applications
- 1.11 Prior Art
- 1.12 Frequently Updated Research
- 1.13 Questions about Image Selection and Response Method for Service Providers
- 1.14 Original Abstract Submitted
IMAGE SELECTION USING MACHINE LEARNING
Organization Name
Inventor(s)
Chun-Chen Kuo of San Francisco CA (US)
IMAGE SELECTION USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240184436 titled 'IMAGE SELECTION USING MACHINE LEARNING
Simplified Explanation
Simplified Explanation
The patent application describes a method where a server computer receives images related to service providers, selects an image based on a scoring algorithm, and provides it as a response to an inquiry request from an end user device.
- The method involves receiving multiple images linked to service providers.
- A scoring algorithm is used to select the most suitable image based on a composite score.
- The server computer then sends the selected image as a response to an inquiry request from the end user device.
Key Features and Innovation
- Utilization of a scoring algorithm to select the most appropriate image from a set of images.
- Incorporation of a conversion component and an uncertainty component in the scoring algorithm.
- Providing an efficient way to respond to inquiry requests by selecting the best image based on the composite score.
Potential Applications
This technology can be applied in various industries such as e-commerce, advertising, and service provider directories.
Problems Solved
- Streamlining the process of selecting and providing relevant images to end users.
- Enhancing user experience by offering visually appealing and contextually relevant images.
Benefits
- Improved user engagement and satisfaction.
- Efficient and accurate image selection process.
- Enhanced decision-making for end users based on the provided images.
Commercial Applications
- "Image Selection and Response Method for Service Providers" can be utilized in online platforms, mobile applications, and digital directories to enhance user experience and engagement.
Prior Art
No specific information on prior art related to this technology is provided in the abstract.
Frequently Updated Research
There is no information available on frequently updated research relevant to this technology.
Questions about Image Selection and Response Method for Service Providers
How does the scoring algorithm determine the composite score for selecting images?
The scoring algorithm evaluates each image based on the conversion component and the uncertainty component to calculate a composite score, which helps in selecting the most suitable image.
What are the potential challenges in implementing this method in real-world applications?
The potential challenges could include fine-tuning the scoring algorithm, ensuring seamless integration with existing systems, and managing a large database of images associated with service providers.
Original Abstract Submitted
a method is disclosed. the method includes receiving, by a server computer, a plurality of images associated with one or more service providers. the server computer then receives an inquiry request, and determines an image of the plurality of images. the image is selected in response to a composite score based on a scoring algorithm scoring each image in the plurality of images. the scoring algorithm comprises a conversion component and an uncertainty component. the server computer provides an inquiry response comprising the image to the end user device operated by the end user.
