公开(公告)号：US20240331015A1

公开(公告)日：2024-10-03

申请号：US18129464

申请日：2023-03-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Kenneth Jason Sanchez ,  Eric Hermann

IPC: G06Q30/0601 ,  G06Q30/02

CPC classification number: G06Q30/0635 ,  G06Q30/0281 ,  G06Q30/0639

Abstract: An online concierge system facilitates creation of shopping lists of items for ordering from a physical retail store and at least partial self-service fulfillment of orders by the customer. To support fulfillment by the customer, the online concierge system may intelligently select one or more items of the order to be picked by a third-party picker and prepopulated to a shopping cart reserved for the customer in advance of the customer arriving at the retail location. The items for prepopulating may be selected based on various factors that optimize prepopulation decisions on an item-by-item basis in accordance with various machine learning models. The online concierge system may furthermore facilitate procurement of the remaining items by the customer through a customer client device that may track item procurement and/or provide guidance for locating items.

公开(公告)号：US20240330977A1

公开(公告)日：2024-10-03

申请号：US18129447

申请日：2023-03-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Cheng Jia ,  Justin Miller ,  Yan Zhuang ,  Anvar Gazizov ,  Hassan Djirdeh ,  Aakarsh Madhavan ,  Brijendra Nag ,  Ji Chao Zhang

IPC: G06Q30/0242 ,  G06Q30/0273

CPC classification number: G06Q30/0243 ,  G06Q30/0275

Abstract: A keyword campaign automatically groups keywords for customized override bids for the keyword group. The keywords of a campaign may be analyzed by a computer model to predict membership in a category in addition to the likelihood that the bid of the keyword will be modified. The keyword groups may be automatically generated based on the predictions, and performance metrics are evaluated for the keyword groups at one or more modified bids. The performance metrics of the keyword groups at the modified bids may then be used to set override bids. The automatically generated keyword groups and performance metrics permit a sponsor to intelligently group and customize keyword bids with reduced interface interactions and without requiring individual keyword bid adjustments.

公开(公告)号：US12086754B2

公开(公告)日：2024-09-10

申请号：US17752772

申请日：2022-05-24

Applicant: Maplebear Inc.

Inventor： Benjamin Knight ,  Darren Johnson ,  Salmaan Ayaz ,  Saumitra Maheshwari ,  Tomasz Debicki ,  Do Quang Phuoc Dang ,  Valery Vaskabovich

IPC: G06Q10/0833 ,  G06Q10/087 ,  G06Q20/40

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：US20240289861A1

公开(公告)日：2024-08-29

申请号：US18587655

申请日：2024-02-26

Applicant: Maplebear Inc.

Inventor： Haixun Wang ,  Tejaswi Tenneti ,  Taesik Na ,  Yuanzheng Zhu ,  Vinesh Reddy Gudla ,  Lee Cohn

IPC: G06Q30/0601

CPC classification number: G06Q30/0631 ,  G06Q30/0627 ,  G06Q30/0635 ,  G06Q30/0643

Abstract: Responsive to an input query from a user, an online system presents a list of recommended items that are related to the input query. The input query may be formulated as a natural language query. The online system performs an inference task in conjunction with the model serving system to generate one or more additional queries that are related to the input query and/or are otherwise related to the recommended items presented in response to the input query. The additional queries may be presented to the user in conjunction with the list of recommended items.

公开(公告)号：US20240289632A1

公开(公告)日：2024-08-29

申请号：US18588622

申请日：2024-02-27

Applicant: Maplebear Inc.

Inventor： Li Tan ,  Haixun Wang ,  Jian Li

IPC: G06N3/092 ,  H04L51/02

CPC classification number: G06N3/092 ,  H04L51/02

Abstract: An online system trains a specific-purpose LLM. The online system obtains training examples and divides training examples across batches. The online system generates a specific response by applying parameters of the specific-purpose LLM to a batch of training examples. The online system generates a general response by applying parameters of a general-purpose LLM to the batch of training examples. The online system computes a human readability score representing the difference between the specific response and the general response. The online system computes an objective compliance score by applying an evaluation model to the specific response, the evaluation model trained to score the first response based on a specific objective. The online system updates the parameters of the specific-purpose LLM based on the human readability score and the objective compliance score.

公开(公告)号：US20240257221A1

公开(公告)日：2024-08-01

申请号：US18631964

申请日：2024-04-10

Applicant: Maplebear Inc.

Inventor： Negin Entezari ,  Sharath Rao Karikurve ,  Shishir Kumar Prasad ,  Haixun Wang

IPC: G06Q30/0601 ,  G06Q10/087

CPC classification number: G06Q30/0633 ,  G06Q10/087

Abstract: An online concierge shopping system identifies candidate items to a user for inclusion in an order based on prior user inclusion of items in orders and items currently included in the order. From a multi-dimensional tensor generated from cooccurrences of items in orders from various users, the online concierge system generates item embeddings and user embeddings in a common latent space by decomposing the multi-dimensional tensor. From items included in an order, the online concierge system generates an order embedding from item embeddings of the items included in the order. Scores for candidate items are determined based on similarity of item embeddings for the candidate items to the order embedding. Candidate items are selected based on their scores, with the selected candidate items identified to the user.

公开(公告)号：US20240249335A1

公开(公告)日：2024-07-25

申请号：US18159357

申请日：2023-01-25

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Taesik Na ,  Vinesh Reddy Gudla ,  Xiao Xiao

IPC: G06Q30/0601 ,  G06F16/9535 ,  G06Q30/0201

CPC classification number: G06Q30/0631 ,  G06F16/9535 ,  G06Q30/0201

Abstract: An online system displays search results in response to a query by receiving a query from a customer. An online system accesses a set of candidate items and computes a relevance score and personalization score for each item. The online system computes the relevance score based on query data and item data and may normalize the relevance score. The online system computes the personalization score based on item data, such as an item embedding, and user data, such as a user embedding. The online system computes a query specificity score and adjusts the personalization score with the query specificity score such that generic queries have high personalization scores and specific queries have low personalization scores. The online system combines the relevance and personalization scores for each candidate item into a ranking score and displays the candidate items to the customer based on their ranking scores.

公开(公告)号：US20240242145A1

公开(公告)日：2024-07-18

申请号：US18156347

申请日：2023-01-18

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Haochen Luo ,  Eric Hermann ,  Rishab Saraf ,  Abhinav Darbari ,  Teodor Lefter ,  Jason Sanchez ,  Jagannath Putrevu

IPC: G06Q10/0631 ,  G06Q10/0639 ,  G06Q10/0835 ,  G06Q10/087 ,  G06Q30/0202 ,  G06Q30/0601

CPC classification number: G06Q10/063118 ,  G06Q10/06398 ,  G06Q10/08355 ,  G06Q10/087 ,  G06Q30/0202 ,  G06Q30/0635

Abstract: An online concierge shopping system fulfills orders using workers who pick items at a warehouse to complete an order and workers to deliver the orders to a customer's location. To optimize the staffing of workers for each task, the system uses a trained model to predict the number of workers needed to achieve an optimal outcome based on an input set of contextual information. The system also schedules specific workers to various shifts using the predicted number of workers needed and then searching a feasibility space for an optimal solution. The trained model may be updated based on performance observations.

公开(公告)号：US20240202694A1

公开(公告)日：2024-06-20

申请号：US18169012

申请日：2023-02-14

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ganglu Wu ,  Shiyuan Yang ,  Xiao Zhou ,  Qi Wang ,  Qunwei Liu ,  Youming Luo

IPC: G06Q20/20 ,  G06K7/14 ,  G06Q30/0601 ,  G06T3/40 ,  G06V10/25

CPC classification number: G06Q20/208 ,  G06K7/1443 ,  G06Q30/0633 ,  G06Q30/0641 ,  G06T3/40 ,  G06V10/25

Abstract: An automated checkout system modifies received images of machine-readable labels to improve the performance of a label detection model that the system uses to decode item identifiers encoded in the machine-readable labels. For example, the automated checkout system may transform subregions of an image of a machine-readable label to adjust for distortions in the image's depiction of the machine-readable label. Similarly, the automated checkout system may identify readable regions within received images of machine-readable labels and apply a label detection model to those readable regions. By modifying received images of machine-readable labels, these techniques improve on existing computer-vision technologies by allowing for the effective decoding of machine-readable labels based on real-world images using relatively clean training data.

公开(公告)号：US20240193637A1

公开(公告)日：2024-06-13

申请号：US18582839

申请日：2024-02-21

Applicant: Maplebear Inc.

Inventor： Michael Montero

IPC: G06Q30/0242 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q30/0207 ,  G06Q30/0251

CPC classification number: G06Q30/0244 ,  G06Q30/0201 ,  G06Q30/0239 ,  G06Q30/0271 ,  G06N20/00

Abstract: Systems and methods for scoring promotions are provided. A set of training offers are received, which include combinations of variable values. These combinations of variable values are converted into a vector value. The offers are paired and the vectors subtracted from one another, resulting in a pair vector. Metrics for the success of offers is collected, and are subtracted from one another for the paired offers to generate a raw score. This raw score is then normalized using the pair vector. The normalized scores are utilized to generate a model for the impact any variable value has on offer success, which may then be applied, using linear regression, to new offers to generate an expected level of success. The new scored offers are ranked and the top-ranked offers are selected for inclusion in a promotional campaign.