Doordash, inc. (20240203120). GENERATING MAPPING INFORMATION BASED ON IMAGE LOCATIONS simplified abstract
Contents
- 1 GENERATING MAPPING INFORMATION BASED ON IMAGE LOCATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING MAPPING INFORMATION BASED ON IMAGE LOCATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Location Optimization System for Delivery Services
- 1.13 Original Abstract Submitted
GENERATING MAPPING INFORMATION BASED ON IMAGE LOCATIONS
Organization Name
Inventor(s)
Sushil Vellanki of Freemont CA (US)
Kuleen Nimkar of Oak Park IL (US)
Li Xiang Tian of San Francisco CA (US)
GENERATING MAPPING INFORMATION BASED ON IMAGE LOCATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240203120 titled 'GENERATING MAPPING INFORMATION BASED ON IMAGE LOCATIONS
Simplified Explanation
The patent application describes a system that receives images and location data from various devices over time to determine a consensus delivery location using machine learning.
Key Features and Innovation
- System receives images and location data from agent devices.
- Images may have different location data associated with them.
- Machine-learning model determines if images contain enough information.
- Consensus delivery location is determined based on the location data of the images.
- Mapping information is stored for the delivery location.
Potential Applications
This technology could be used in:
- Logistics and delivery services to optimize delivery routes.
- Real estate for accurate property location mapping.
- Emergency services for quick and accurate location identification.
Problems Solved
- Efficiently determining a consensus location from multiple sources of data.
- Ensuring accurate mapping information for delivery locations.
- Streamlining decision-making processes based on location data.
Benefits
- Improved accuracy in determining delivery locations.
- Enhanced efficiency in logistics and mapping processes.
- Better decision-making based on location data.
Commercial Applications
Title: Location Optimization System for Delivery Services This technology can revolutionize the logistics industry by providing accurate and efficient delivery location mapping, leading to cost savings and improved customer satisfaction. It can also be utilized in real estate and emergency services for precise location identification.
Prior Art
Readers can explore prior art related to location-based machine learning models, image processing for location data, and consensus location determination algorithms.
Frequently Updated Research
Researchers are constantly developing new algorithms and models to enhance the accuracy and efficiency of location-based systems like the one described in this patent application.
Questions about Location Optimization System for Delivery Services
How does the machine-learning model determine if images contain enough information?
The machine-learning model analyzes the content of the images to assess the amount of relevant location data present, based on predefined criteria and training data.
What are the potential challenges in implementing this technology in real-world scenarios?
Implementing this technology may face challenges related to data privacy, data security, and the integration of diverse data sources for consensus location determination.
Original Abstract Submitted
in some examples, a system may receive over time, from one or more agent devices, and in association with a delivery location, a plurality of images and associated respective location data. further, the respective location data associated with at least one of the images can differ from the respective location data associated with at least one other one of the images. the plurality of images are input to a machine-learning model that is trained to determine whether individual images include a threshold amount of information. based at least on the machine-learning model indicating that the individual images satisfy the threshold amount of information, the system determines, based on at least one of averaging or clustering of the respective location information associated with the plurality of images, a consensus location for the delivery location. the system stores the consensus location information as mapping information associated with the delivery location.
