公开(公告)号：US20240070583A1

公开(公告)日：2024-02-29

申请号：US17823838

申请日：2022-08-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Amod Mital ,  Sherin Kurian ,  Kevin Ryan ,  Shouvik Dutta ,  Jason He ,  Aneesh Mannava ,  Ralph Samuel ,  Jagannath Putrevu ,  Deepak Tirumalasetty ,  Krishna Kumar Selvam ,  Wei Gao ,  Xiangpeng Li

IPC: G06Q10/06 ,  G06Q10/08

CPC classification number: G06Q10/06316 ,  G06Q10/087 ,  G06Q10/06311 ,  G06Q10/08355

Abstract: The online concierge system generates task units based on orders and assigns batches of task units to pickers. The online concierge system generates task units based on received orders. The online concierge system generates permutations of these task units to generate candidate sets of task batches. The online concierge system scores each of these candidate sets, and selects a set of task batches to assign to pickers based on the scores. Additionally, to determine which task UI to display to the picker, the picker client device uses a UI state machine. The UI state machine is a state machine where each state corresponds to a task UI to display on the picker client device. The state transitions between the UI states of the UI state machine indicate which UI state to transition to from a current UI state based on the next task unit in the received task batch.

公开(公告)号：US20240070577A1

公开(公告)日：2024-02-29

申请号：US17823850

申请日：2022-08-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Krishna Kumar Selvam ,  Joseph Cohen ,  Tahmid Sharjar ,  Neel Sarwal ,  Darren Johnson ,  Nicholas Rose ,  Ajay Pankaj Sampat ,  Joey Dong

IPC: G06Q10/06

CPC classification number: G06Q10/063114 ,  G06Q10/06316

Abstract: The online concierge system generates task units based on orders and assigns batches of task units to pickers. The online concierge system generates task units based on received orders. The online concierge system generates permutations of these task units to generate candidate sets of task batches. The online concierge system scores each of these candidate sets, and selects a set of task batches to assign to pickers based on the scores. Additionally, to determine which task UI to display to the picker, the picker client device uses a UI state machine. The UI state machine is a state machine where each state corresponds to a task UI to display on the picker client device. The state transitions between the UI states of the UI state machine indicate which UI state to transition to from a current UI state based on the next task unit in the received task batch.

公开(公告)号：US20240070491A1

公开(公告)日：2024-02-29

申请号：US17900533

申请日：2022-08-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Lanchao Liu ,  George Ruan ,  Zhiqiang Wang ,  Xiangdong Liang ,  Jagannath Putrevu ,  Ganesh Krishnan ,  Ryan Dick

IPC: G06N5/04 ,  G06N5/02

CPC classification number: G06N5/04 ,  G06N5/022

Abstract: An online system accesses a machine learning model trained to predict behaviors of users of the online system, in which the model is trained based on historical data received by the online system that is associated with the users and demand and supply sides associated with the online system. The online system identifies a treatment for achieving a goal of the online system and simulates application of the treatment on the demand and supply sides based on the historical data and a set of behaviors predicted for the users. Application of the treatment is simulated by replaying the historical data in association with application of the treatment and applying the model to predict the set of behaviors while replaying the data. The online system measures an effect of application of the treatment on the demand and supply sides based on the simulation, in which the effect is associated with the goal.

公开(公告)号：US20240013184A1

公开(公告)日：2024-01-11

申请号：US17874956

申请日：2022-07-27

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Yilin Huang ,  Ganglu Wu ,  Xiao Zhou ,  Youming Luo ,  Shiyuan Yang

IPC: G06Q20/20 ,  G06V10/778 ,  G06V10/25 ,  G06V20/50

CPC classification number: G06Q20/208 ,  G06V10/778 ,  G06V10/25 ,  G06V20/50

Abstract: A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.

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

IPC: G06Q10/08 ,  G06Q30/06

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.

公开(公告)号：US20240005096A1

公开(公告)日：2024-01-04

申请号：US17855799

申请日：2022-07-01

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ramasubramanian Balasubramanian ,  Saurav Manchanda

IPC: G06F40/284 ,  G06F40/186 ,  G06N5/02

CPC classification number: G06F40/284 ,  G06F40/186 ,  G06N5/022

Abstract: A masked language model is used to predict an attribute of an object, such as a physical item or product based on the predicted value of a masked token. The masked language model may be trained on a general corpus of text for the language, such that the masked language model learns context and text token relationships. Information about the object may then be added to a query template that structures the item information in an attribute query that may be interpretable by the masked language model to provide a resulting token related to the provided information or to confirm or reject an attribute specified in the query template.

公开(公告)号：US20240004886A1

公开(公告)日：2024-01-04

申请号：US17826162

申请日：2022-05-27

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Nicholas Cooley

IPC: G06F16/2457 ,  G06F16/2453 ,  G06F16/2455

CPC classification number: G06F16/24578 ,  G06F16/2455 ,  G06F16/24542

Abstract: An online system may generate numerous search records in response to searches requested by users. The online system may use a specific way to sample the historical search records to reduce biases in sampling. For example, the online system retrieves historical query records associated with an item query engine. The set of historical query records includes a plurality of search phrases. A historical query record is associated with a search phrase and a list of items returned by the item query engine. The online system determines the search frequencies for the search phrases. The online system stratifies the historical query records into a plurality of bins according to the search frequencies of the search phrases. The online system samples the historical query records from the plurality of bins to collect a representative set of historical query records and outputs the representative set of historical query records for rating.

公开(公告)号：US20230376923A1

公开(公告)日：2023-11-23

申请号：US17751525

申请日：2022-05-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Nathan Bauer

IPC: G06Q20/18

CPC classification number: G06Q20/18

Abstract: An automated checkout system automatically establishes sessions between users and shopping carts by correlating action events with distances of the user's client device to the shopping cart. The automated checkout system determines the client device's distance from the shopping cart at timestamps when an action event occurs with respect to the shopping cart. If the distances and the action events are correlated, the system establishes a session between the user and the shopping cart. Additionally, the automated checkout system attributes target actions to recipe suggestions. The automated checkout system displays a recipe suggestion to a user on a display of a shopping cart, and identifies an item added to the shopping cart. If the added item matches an item in the set of recipes, the automated checkout system applies an attribution model that determines whether to attribute a target action that relates to the item with the recipe suggestion.

公开(公告)号：US20230351486A1

公开(公告)日：2023-11-02

申请号：US17730632

申请日：2022-04-27

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Leho Nigul

IPC: G06Q30/06 ,  G06T19/00 ,  H04L67/306 ,  G06T15/00

CPC classification number: G06Q30/0643 ,  G06T19/006 ,  H04L67/306 ,  G06T15/005

Abstract: An online system receives information describing a physical retail store, in which the information includes attributes of physical elements within the store and their arrangement. A request is received from a user to generate a rendering of the store in a virtual reality environment. A profile of the user describing the user's geographic location and a set of historical actions performed by the user are accessed, in which the set of historical actions is associated with one or more of the physical elements. Based on the information describing the store and the profile, the rendering is generated to include virtual reality elements representing a set of the physical elements arranged based on the arrangement of the physical elements, and the rendering is sent for display to the user. When an update to the information describing the store is received, the rendering is updated and sent for display to the user.

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.