DoorDash has filed a patent for a method that involves a server computer obtaining user and service provider features, inputting them into a model, and updating the model. The server computer then determines multiple carts, provides them to an end user device, receives a selection or modification of a cart, processes the selection by facilitating preparation and delivery of items in the cart, and updates the model using data related to the cart. GlobalData’s report on DoorDash gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on DoorDash, supply chain management systems was a key innovation area identified from patents. DoorDash's grant share as of June 2023 was 1%. Grant share is based on the ratio of number of grants to total number of patents.
The patent is filed for a method of providing and processing carts for end users
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By GlobalDataA recently filed patent (Publication Number: US20230206308A1) describes a method for providing personalized shopping carts to users based on their preferences and needs. The method involves obtaining user features, service provider features, and a model. These features are then inputted into the model, which is updated to form an updated model. Using this updated model, the server computer determines multiple carts, each containing one or more items, and provides them to the user's device.
Once the user receives the carts, they can select a cart or modify it according to their preferences. The server computer processes the selection by facilitating the preparation and delivery of the items in the cart. Additionally, the updated model is further updated using data related to the cart, forming a subsequent model.
The model used in this method is a machine learning model that is trained using user features and service provider features. After determining the carts, the method includes sorting each cart based on a relevance score, indicating how relevant the cart is to the user. This relevance score is created by the updated model.
The server computer also stores the carts in a candidate cart database. The user features, service provider features, and the model are obtained from respective databases. The user features include order histories, addresses, and dietary preferences, while the service provider features include ratings, order rates, and item tags.
In terms of processing the selection and facilitating the preparation and delivery of items, the server computer generates an order request message containing the selected items. This message is provided to a service provider computer responsible for preparing the items. The server computer also determines a transporter user device based on location and generates a delivery request message. This message is sent to the transporter user device, which decides whether to accept the delivery. The server computer receives a delivery response message indicating the acceptance or rejection of the delivery.
The patent also describes a server computer that implements the method. It includes a processor and a computer-readable medium with executable code for obtaining user features, service provider features, and a model. The server computer then updates the model, determines carts, provides them to the user's device, processes the selection, and updates the model using cart-related data.
Overall, this patent presents a method and system for personalized shopping carts based on user and service provider features, utilizing a machine learning model to enhance the shopping experience.
To know more about GlobalData’s detailed insights on DoorDash, buy the report here.
GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
