-
公开(公告)号:US20230316350A1
公开(公告)日:2023-10-05
申请号:US17853619
申请日:2022-06-29
Applicant: Maplebear Inc. (dba Instacart)
Inventor: Benjamin Knight , Benjamin Peyrot , Djordje Gluhovic , Rohit Turumella , Alice Han
CPC classification number: G06Q30/04 , G06Q20/207 , G06Q40/10
Abstract: An online concierge system requests an image of a receipt of an order from a picker after the picker fulfills the order at a store. The online concierge system performs image processing on the image of the receipt and uses machine learning and optical character recognition to determine a tax amount paid for the order and a confidence score associated with the tax amount. The online concierge system may use the machine learning model for segmenting extracted text in the image of the receipt into tokens. The online concierge system may then determine at least one token associated with a tax item and the tax amount associated with the tax item. The online concierge system communicates the tax amount to the store for reimbursement based on the tax amount and the confidence score.
-
公开(公告)号:US20230162141A1
公开(公告)日:2023-05-25
申请号:US17534281
申请日:2021-11-23
Applicant: Maplebear Inc. (dba Instacart)
Inventor: Benjamin Knight , Darren Johnson , Dan Haugh , Saumitra Maheshwari , Qi Xi , Conor Woods
CPC classification number: G06Q10/087 , G06Q30/0629 , G06Q30/0641
Abstract: An online concierge system receives information from a warehouse including locations of items within the warehouse. When a shopper selects an order for fulfillment from the warehouse, the online concierge system sorts the items for the shopper to minimize the time spent in the warehouse using the received information. When the online concierge system does not receive a location of an item within the warehouse, the online concierge system obtains a taxonomy for the warehouse including multiple levels, with each level having a different level of specificity. The online concierge system determines a higher level in the taxonomy for the item and identifies other items offered by the warehouse having the determined category. The online concierge system infers a location of the item within the warehouse used for sorting items of the order from locations of the other items within the warehouse and times when shoppers retrieved the other items.
-
3.
公开(公告)号:US20240005269A1
公开(公告)日:2024-01-04
申请号:US17855793
申请日:2022-07-01
Applicant: Maplebear Inc. (dba Instacart)
Inventor: Benjamin Knight , Qi Xi , Saumitra Maheshwari , Salmaan Ayaz
CPC classification number: G06Q10/087 , G06Q30/0633
Abstract: An online system performs a method. The method comprises obtaining historical pick data for items located in a warehouse, including data for each of the items picked and pick times between each of the items picked, and determining a taxonomy of items offered by the warehouse. The taxonomy identifies a plurality of product categories structured in a hierarchy, wherein each level of the hierarchy corresponds to a particular level of granularity of product data. The method further comprises applying the historical pick data to a machine learning model to generate pairwise relations between product categories at each level of the taxonomy and generating sequences of product categories based on the pairwise relations. An order for items offered by the warehouse is received and compared to the sequences for each level to generate a pick sequence for picking the items efficiently, which is outputted by the system to a mobile application.
-
4.
公开(公告)号:US20230351326A1
公开(公告)日:2023-11-02
申请号:US18136513
申请日:2023-04-19
Applicant: Maplebear Inc. (dba Instacart)
Inventor: Benjamin Knight
IPC: G06Q10/0875
CPC classification number: G06Q10/0875
Abstract: A system receives a request for a set of items at a warehouse from a user device, and determines a set of candidate items responsive to the request. The system applies a trained item availability model to each candidate item to determine a prediction of a likelihood that the candidate item is available for pickup at the warehouse. A subset of candidate items that have a prediction below a threshold is classified as low availability. The computer system also determines a cap of low availability items to present to a user based on a user utility curve. The user utility curve is modeled based on user utility associated with amounts of low availability items presented. The low availability items are filtered to an amount within the determined cap. The filtered low availability items are sent to the user device for presentation in a user interface.
-
5.
公开(公告)号:US20230289707A1
公开(公告)日:2023-09-14
申请号:US17752772
申请日:2022-05-24
Applicant: Maplebear Inc. (dba Instacart)
Inventor: Benjamin Knight , Darren Johnson , Salmaan Ayaz , Saumitra Maheshwari , Tomasz Debicki , Do Quang Phuoc Dang , Valery Vaskabovich
CPC classification number: G06Q10/0833 , G06Q10/087 , G06Q20/4015
Abstract: An online concierge system performs asynchronous automated correction handling of incorrectly sorted items using point-of-sale data. The online concierge system receives orders from customer client devices and determines a batched order based on the received orders. The online concierge system sends the batched order to a shopper client device for fulfillment. The online concierge system receives transaction data associated with the batched order from a third party system. The online concierge system determines whether a sorting error occurred based on the transaction data and the batched order. In response to determining that a sorting error occurred, the online concierge system sends an instruction to correct the sorting error to the shopper client device.
-
公开(公告)号:US20200219171A1
公开(公告)日:2020-07-09
申请号:US16734273
申请日:2020-01-03
Applicant: Maplebear, Inc. (dba Instacart)
Inventor: Mingzhe Zhuang , Camille Van Horne , Christopher Rudnick , Benjamin Knight , Chris Jenkins , Vicky Andonova , Djordje Gluhovic , Riddhima Sejpal , Maksim Golivkin , Sharath Rao
IPC: G06Q30/06
Abstract: Based on orders fulfilled by shoppers of an online concierge system, the online concierge system identifies items in an order that are difficult to find in a warehouse in which the order is fulfilled. When a shopper obtains a difficult to find item from the warehouse, the online concierge system prompts the shopper to provide information for finding the difficult to find item in the warehouse. The online concierge system stores the information for finding the difficult to find item from the shopper in association with the difficult to find item and with the warehouse. Subsequently, when a different shopper is fulfilling an order from the warehouse including the difficult to find item, the online concierge system displays the information for finding the difficult to find item in the warehouse to the different shopper.
-
-
-
-
-