An online system uses benchmarking tests to identify indexing algorithms for an embedding database. To perform these benchmarking tests, the online system receives a set of parameters for configuring an embedding database. For example, the parameters may include a performance parameter and a latency parameter. The online system generates algorithm scores for a set of candidate indexing algorithms based on the parameters. Specifically, the online system tests each of the candidate indexing algorithms by generating a testing database based on a subset of the entries for the full database and by performing benchmarking tests on the testing database. The online system uses these tests to compute performance metrics for each candidate indexing algorithm and uses those performance metrics to compute an algorithm score for each indexing algorithm. The online system uses the computed algorithm scores to select an indexing algorithm for the embedding database.
An online system predicts time to park at a fulfillment location in fulfillment of an order by a fulfillment user. The online system receives an order from a requesting user, and applies a timeliness prediction model to the order, the parking configuration of the corresponding fulfillment location, to other contextual factors, or some combination thereof to predict the time to park at the fulfillment location. The timeliness prediction model is trained on historical orders with their associated completion times and known parking configurations of the respective fulfillment locations. The online system may batch orders together to optimize fulfillment efficiency in consideration of the predicted lag time for the order. The online system assigns and transmits the batches to fulfillment users to fulfill at the fulfillment locations.
INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL
An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.
A system receives an order for fulfillment from a customer device, the order associated with a delivery time. The system determines a base compensation value for the order and sends the order and base compensation value to devices of one or more fulfillment agents. If the order is not accepted within a predetermined time, the system applies a trained machine learning model to updated input features of the order and the fulfillment agents to predict an amount of lateness time past the delivery time. Based on the predicted amount of lateness time, the system determines an updated lateness value, determines an updated compensation value, and sends the order with the updated compensation value to the fulfillment agents. The system repeats prediction, lateness value determination, and compensation adjustment until the order is accepted.
A smart system, such as a smart shopping cart system, uses an efficient selection algorithm to select an item identifier prediction for an item. The smart cart system uses a set of machine-learning models to generate identifier predictions based on images. To select an item identifier, the smart system applies an efficient selection algorithm to the predictions from the machine-learning models. An efficient selection algorithm is an algorithm that requires minimal computational resources to perform. For example, the efficient selection algorithm may be a simple majority algorithm that selects the identifier prediction generated by a majority of the models or a weighted voting algorithm where each model's vote is weighted by some metric. The smart system applies the efficient selection algorithm to select an item identifier prediction from the ones generated by the models. The smart system may display content related to the item associated with the item identifier prediction.
G06V 30/224 - Reconnaissance de caractères caractérisés par le type d’écriture de caractères imprimés pourvus de marques de codage additionnelles ou de marques de codage
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
An online system receives a query from a user of the online system. The online system identifies a candidate set of cold start results to the query defined as having been presented to the user less than a threshold number of times. The cold start results are then filtered based on their relevance to the query to generate a final set of cold start results and a score is generated for each cold start result without interaction data using a scoring baseline common to standard results with interaction data. Accordingly, the online system ranks the cold start results with a set of standard results based on the score for each cold start result using the scoring baseline and presents the same for display to the user.
An online system uses a trained machine-learning model to predict hard-to-find items, which may facilitate picking of these items. The online system receives, from one or more devices of one or more pickers, a device of a source, one or more devices associated with one or more users, and/or a computing system associated with a physical receptacle utilized by at least one user for shopping in a location of the source, data with information about an item. The online system applies the trained machine-learning model to output, based on the received data, a findability score for the item indicative of a findability of the item. Based on the findability score, the online system generates and communicates one or more action signals to a device of a picker, the device of the source, and/or a device associated with a user prompting one or more actions in relation to the item.
G06Q 10/087 - Gestion d’inventaires ou de stocks, p. ex. exécution des commandes, approvisionnement ou régularisation par rapport aux commandes
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source
8.
Display panel of a programmed computer system with a graphical user interface
An online system displays an interface to users including slots in which sources from a list of sources of items (e.g., physical items, content items) are presented. The user may select a source via the interface to view items provided by, or associated with, the source. To simplify a user identifying a desired source, the online system includes sources that a user is likely to select as well as new sources in the list. To balance the competing interests of relevance of sources with which the user previously interacted and discovery of new sources, the online system selects an allocation of slots for new sources and for sources with prior interaction based on interactions by users in the geographic regions with different allocations of slots. Based on the selected allocation of slots, the online system selects specific retailers for each slot using ranking models corresponding to different slots.
A client device or an online system communicating with the device receives video data captured by a camera of the device, in which the video data depicts a field of view of a display area of the device. The device/ system detects an object within the field of view based on the video data and applies one or more machine-learning algorithms to identify the object as an item available at a source. The device/system accesses item data for items available at the source and selects one or more supplemental items associated with the identified item based on item data for the identified item and each supplemental item. The device/system generates an augmented reality element including a listing of the supplemental item(s). as well as information or a selectable option associated with each supplemental item. The augmented reality element is then displayed in the display area of the device.
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06V 10/72 - Préparation de données, p. ex. prétraitement statistique des caractéristiques d’images ou de vidéos
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
11.
Using a Trained Machine-Learning Model of an Online System to Handle Unclaimed Online Pickup Orders
An online system uses a trained model for intelligent handling of unclaimed online pickup orders. After identifying that an order placed by a user of the online system is unclaimed at a location of a source, the online system obtains, from a device of a picker associated with the online system and/or a device associated with the source, signals with information about each item in each bundle of the unclaimed order. The online system applies the trained model to identify, based on the obtained signals, a preferred method for disposal of each bundle. Based on the identified preferred method for disposal of each bundle, the online system generates a disposal decision signal and communicates the disposal decision signal to the device associated with the source that prompts personnel at the location of the source to dispose each bundle of the unclaimed order using the identified preferred disposal method.
An online concierge system generates suggested instructions for presentation to a user. The online concierge system access instruction examples corresponding to a target item category and generates candidate instruction representations based on instruction messages within each instruction example. The online concierge system generates preliminary scores for the candidate instruction representations that are directly related to an intra-category frequency of use of the instruction tokens of the candidate instruction representation within the target item category. The online system normalizes these preliminary scores for the candidate instruction representations based on the inter-category frequency of use of the instruction tokens in all item categories to generate final scores for the candidate instruction representations. The online concierge system selects a set of instruction representations based on these final scores and generates suggested instructions based on the set of instruction representations.
An online system automatically identifies item attributes of an item. The online system prompts a set of outputs from a set of multi-modal large language models with an image of the product and a request to determine if the details of size information is present in the image. The online system receives a set of outputs, wherein an output describes whether the size information is present in the image. The system then prompts the set of language models with a request to extract the value of the size information in the image. Responsive to determining that a threshold number of outputs have matching values of size information that is present in the image, the system updates the item attribute data with the matching values of size information of the product.
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
14.
Machine learning approach to provide adaptive search result page load size and layout
An online system receives, at a search interface, a search query from a user. The online system determines a recall set size for search results of the search query. The online system determines a page load size to display at least a portion of the search results by determining a query entropy associated with the search query, inputting a plurality of signals into a machine learning model, the plurality of signals comprising the query entropy, and receiving, from the machine learning model, the page load size. The online system selects a set of physical object identifiers based on the page load size. The online system generates for display a user interface that groups the selected physical object identifiers. The online system causes a device associated with the user to display the generated user interface.
An online system uses a trained model to predict incremental sales caused by a sample counter for in-store free sampling of an item. Upon receiving signals related to in-store purchases of the item, the online system applies the trained model to output, based on the received signals, a ranked list of locations of a source and a ranked list of timeslots for placing the sample counter. The online system selects, from the ranked list of locations and the ranked list of timeslots, a location of the source and a timeslot for placing the sample counter, and generates a decision signal based on the selected location and the selected timeslot. The online system communicates, via the network to a device associated with the source, the decision signal prompting the source to place the sample counter for free sampling of the item at the selected location and during the selected timeslot.
A client device or an online system communicating with the device receives video data captured by a camera of the device, in which the video data depicts a field of view of a display area of the device. The device/system detects an object within the field of view based on the video data and applies one or more machine-learning algorithms to identify the object as an item available at a source. The device/system accesses item data for items available at the source and selects one or more supplemental items associated with the identified item based on item data for the identified item and each supplemental item. The device/system generates an augmented reality element including a listing of the supplemental item(s), as well as information or a selectable option associated with each supplemental item. The augmented reality element is then displayed in the display area of the device.
A client device, or an online system communicating with the device, receives video data depicting a field of view of a display area of the device and applies machine-learning algorithms to the video data to detect objects, including portions of a body of a user of the device, within the field of view and to determine a series of body poses. The device/system uses machine-learning models to predict an action performed by the user based on the series of poses and to predict a recipe being prepared based on the objects and a predicted series of actions performed by the user. The device/system selects a suggestion associated with preparing the recipe based on candidate suggestions associated with preparing the recipe, the objects, or the predicted series of actions, and generates an augmented reality element describing the suggestion. The augmented reality element is displayed in the display area of the device.
An online system retrieves a set of user data including information describing one or more interactions by a user with the system. The system accesses and applies a machine-learning model to predict an exploration score for the user based on the set of user data, in which the score describes a likelihood of a set of interactions by the user with content associated with less than a threshold measure of familiarity to the user. Upon receiving a request from a client device associated with the user to access a user interface including content recommended to the user, the system selects content to recommend to the user based on the score and information describing a set of previous interactions by the user with the content. The system generates the user interface including the selected content and sends the user interface to the client device where it is displayed.
A trained model is used to determine a price sensitivity feature for a user of an online system. The online system generates input data by gathering replacement data via a user interface at a device associated with the user and/or in-store behavior data related to replacement of items performed by the user at a location of a retailer when using a physical receptacle in communication with the online system. The online system applies a price sensitivity model to predict, based on the input data, a price sensitivity score for the user indicative of the price sensitivity feature of the user. The online system identifies, based on the price sensitivity score, one or more actions related to prompting the user to convert one or more items. The online system applies the one or more actions to prompt the user to convert the one or more items.
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.
A device may obtain historical order data comprising orders submitted by users to an online system, each order indicating a retailer location and a timestamp. A device may generate a first set of training examples, each training example indicating order demand at a retailer location during one period of time from a first set of periods of time. A device may train the demand forecast prediction model with the first set of training examples. A device may apply the demand forecast prediction model to a second set of periods of time to predict order demand for each period of time in the second set of periods of time. A device may track order demand across each period of time in the second set of periods of time. A device may generate a second set of training examples, each training example indicating a difference between the predicted order demand and the tracked order demand at the retailer location during each period of time from the second set of periods of time. A device may retrain the demand forecast prediction model with the second set of training examples.
A trained model is used to generate a user interface of an online system based on predicted nutritional preferences for a user of the online system. Upon receiving a signal indicating interaction of the user with the online system, the online system applies the trained model to output, based on user's features, item features and/or session features, a vector of scores for the user, where each score is indicative of a preference of the user for a respective nutritional attribute of a set of nutritional attributes. Responsive to a score being greater than a threshold score, the online system generates, based on the received signal, a user interface of a device associated with the user that includes a label about the nutritional attribute associated with the score. The online system causes the device associated with the user to display the user interface with the label about the nutritional attribute.
A user interface of an online system is generated based on search for relevant items that match ingredients of a recipe. After receiving, from a device associated with a user of the online system, a query for an ingredient of a recipe, the online system identifies, based on one or more attributes in the query, a set of candidate items for the ingredient. The online system generates a recipe relevance score for each candidate item by applying a weighted sum of scores, ranks the identified candidate items based on their recipe relevance scores, and selects one or more items for presentation to the user. The online system then generates a user interface of the device with a recipe page including the ingredient of the recipe and the one or more items that match the ingredient of the recipe.
An online concierge system fulfills orders placed by users. When a user notifies the online concierge system of a problem with order fulfillment, the online concierge system performs one or more remedial actions (e.g., a credit, a discount, a free delivery). To provide a proactive remedial action before receiving a notification of a problem with order fulfillment from a user, the online concierge system trains a proactive remediation model that predicts, for an order having an event during fulfillment, a likelihood of loss of interaction by the user with the online concierge system (i.e., “churn” of the user) without performing a proactive remedial action. When fulfilling an order, in response to determining an event during fulfillment, the online concierge system applies the proactive remediation model to determine the likelihood of churn of the user if no proactive appeasement is performed, for determining whether to perform a proactive remedial action.
An online system displays an interface to users including slots in which sources from a list of sources of items (e.g., physical items, content items) are presented. The user may select a source via the interface to view items provided by, or associated with, the source. To simplify a user identifying a desired source, the online system includes sources that a user is likely to select as well as new sources in the list. To balance the competing interests of relevance of sources with which the user previously interacted and discovery of new sources, the online system selects an allocation of slots for new sources and for sources with prior interaction based on interactions by users in the geographic regions with different allocations of slots. Based on the selected allocation of slots, the online system selects specific retailers for each slot using ranking models corresponding to different slots.
Personalized recommendations matching a list of item descriptors to catalog products from is described. A list associated with a user is received that includes item descriptors. The item descriptors correspond to catalog products stored in a catalog database that includes a plurality of catalog products. Linking data for the user is retrieved. For at least one of the item descriptors in the list, a model is applied to the linking data to generate a score for each of a set of candidate catalog products. A list of recommended catalog products for the user is built by, for each of the item descriptors in the list, selecting one of the set of candidate catalog products based on the generated scores. The list of recommended catalog products is provided to a user client device associated with the user. The user client device is configured to display the list of recommended catalog products.
An online concierge system receives, from a client device associated with a user, a request to access a user interface including a listing of sources associated with the system, in which each source is associated with a catalog of items. The system retrieves user data describing interactions by the user with items available at the sources and accesses and applies a machine-learning model to predict a user engagement score for each item-source pair associated with the sources based on the user data, in which the score indicates a likelihood of an interaction by the user with an item available at a source. The system selects a set of item-source pairs based on the scores and generates the user interface including the listing and a selectable option to add an item associated with each selected pair to a shopping list. The system then sends the user interface to the client device.
A computer system uses clustering and a large language model (LLM) to normalize attribute tuples for items stored in a database of an online system. The online system collects attribute tuples, each attribute tuple comprising an attribute type and an attribute value for an item. The online system initially clusters the attribute tuples into a first plurality of clusters. The online system generates prompts for input into the LLM, each prompt including a subset of attribute tuples grouped into a respective cluster of the first plurality. Based on the prompts, the LLM generates a second plurality of clusters, each cluster including one or more attribute tuples that have a common attribute type and a common attribute value. The online system maps each attribute tuple to a respective normalized attribute tuple associated with each cluster. The online system rewrites each attribute tuple in the database to a corresponding normalized attribute tuple.
G06F 16/21 - Conception, administration ou maintenance des bases de données
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06F 16/28 - Bases de données caractérisées par leurs modèles, p. ex. des modèles relationnels ou objet
Clustering database items based on output of machine-learning model to link database items that represent the same core item but with a different stored size or form attribute
A trained machine-learning model is used to group items in a database of an online system that represent a same core item (i.e., product) but of different attributes. The online system applies, for pairs of items from a specific chunk of the database, the machine-learning model to metadata for each pair of items and category data for each pair of items to generate a clustering score for each pair of items that indicates a likelihood that both items belong to a cluster of items that identifies the core item. The online system then requests a language model to generate a response including a list of attributes in a structured form for each item from the cluster. The online system stores, in an entry of the database associated with each item from the cluster, the list of attributes in the structured form and an identification of the cluster.
An online system performs an inference task in conjunction with the model serving system infer seasonality of items in an item catalog hosted by the online system. The online system generates and provides a prompt to a machine-learned language model to output a list of item categories predicted to be in season for a particular time period and a particular geographical location, e.g., associated with a requesting user. The language model outputs the list of item categories predicted to be in season. The online system validates the list by leveraging the language model and/or historical user engagement data. The online system maps items in the item catalog to the seasonal item categories and tags the mapped items with an in-season badge for display with the item in an ordering interface to the requesting user.
A trained model is used to predict a type of a user of an online system to generate a personalized user interface of the online system. Upon receiving data related to a current session of the user with the online system, the online system applies the trained model to output, based on the session data, a score for the user indicative of a predicted type of the user for the current session. The online system compares the score with a threshold score, and responsive to the score being greater than the threshold score, the online system identifies, based on the score, user data, and information about the current session, a set of user interface elements arranged in a specific order for presentation to the user. The online system then generates a user interface of the device associated with the user that includes the arranged user interface elements.
A machine learned model for item recommendations following failed attempts to purchase those items. During a session, an online system receives a request to fulfill an order from a user device. The system receives a message indicating that an item from the order was not fulfilled. The system logs the item in connection with a profile of the user stored in a database of the online system. During a subsequent session with the user device, the system determines that the logged item is available for fulfillment. The system applies the model to output an intent score indicative of an intent of a user of the user device to acquire the logged item. The logged item is ranked based on the intent score, and a user interface is generated that includes a recommendation to acquire the logged item. The system causes the user device to display the generated user interface.
An online system receives a user query for execution by at least one of a set of generative artificial intelligence (AI) models. The online system assigns the user query to one or more query categories of a set of query categories. The online system accesses a dataset stored in a database. For each query category, the dataset stores a preferred generative AI model for the query category among the set of generative AI models. The online system selects a preferred generative AI model for the user query from the database based on the one or more query categories assigned to the user query. The online system provides a prompt to a model serving system hosting the selected generative AI model. The online system receives, from the model serving system, a response to the user query generated by executing the prompt.
An online concierge system may use images received from shopping carts within retailers to determine the availability of items within those retailers. A shopping cart includes externally-facing cameras that automatically capture images of the area around the shopping cart as the shopping cart travels through a retailer. The online concierge system receives these images, which depict displays within the retailers from which a picker or a retailer patron can collect items. The online concierge system determines which items should be depicted in the images and which items are actually depicted in the images. The online concierge system identifies which items should be depicted, but are not depicted, and determines that these items are unavailable (e.g., out of stock) at that retailer. The online concierge system updates an availability database to indicate that these items are unavailable and may notify the retailer that the item is unavailable.
A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.
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.
G06K 7/14 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation électromagnétique, p. ex. lecture optiqueMéthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire utilisant la lumière sans sélection des longueurs d'onde, p. ex. lecture de la lumière blanche réfléchie
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
An online system trains a machine-learned lift prediction model configured as a neural network. The machine-learned lift prediction model can be used during the inference process to determine lift predictions for users and items associated with the online system. By configuring the lift prediction model as a neural network, the lift prediction model can capture and process information from users and items in various formats and more flexibly model users and items compared to existing methods. Moreover, the lift prediction model includes at least a first portion for generating control predictions and a second portion for generating treatment predictions, where the first portion and the second portion share a subset of parameters. The shared subset of parameters can capture information important for generating both control and treatment predictions even when the training data for a control group of users might be significantly smaller than that of the treatment group.
An online system receives a user query for execution by at least one of a set of generative artificial intelligence (Al) models. The online system assigns the user query to one or more query categories of a set of query categories. The online system accesses a dataset stored in a database. For each query category, the dataset stores a preferred generative Al model for the query category among the set of generative Al models. The online system selects a preferred generative Al model for the user query from the database based on the one or more query categories assigned to the user query. The online system provides a prompt to a model serving system hosting the selected generative Al model. The online system receives, from the model serving system, a response to the user query generated by executing the prompt.
An online concierge system maintains a graph of items available for purchase. The graph maintains edges between items, where an edge between an item and an additional item indicates that one or more customers have previously replaced the item with the additional item. The edge between the item and the additional item also identifies a number of times customers have replaced the item with the additional item. When a customer orders an item, the online concierge system traverses the graph of items to identify candidate replacement items for the ordered item and identifies one or more of the candidate replacement items to the customer. When identifying the candidate replacement items, the online concierge system accounts for distance between the ordered item and different candidate replacement items in the item graph.
A trained model detects seasonal items in an item catalog database of an online system. Upon acquiring item data with information about an item in the item catalog database, the online system applies the trained model to output, based on the item data, a seasonality score for the item that is indicative of a predicted seasonality of the item, and to identify a season associated with the item. The online system updates the item catalog database by adding an identification of the identified season to an entry of the item in the item catalog database. The online system further generates, based on the seasonality score and the identified season, action data associated with one or more actions in relation to the item. The online system communicates, to a computing system of a retailer, the action data prompting the one or more actions in relation to the item.
An online system uses a computer-vision item identification model to identify items and physical containers storing those items to detect sorting errors of the physical containers. The online system receives a first image from a client device that depicts a set of physical containers that contain items for a batch of orders that the online system has received. The online system identifies items in those physical containers by applying a contained-item identification model to the first image. The online system uses the output of this model to determine which visible items are in each physical container and uses that information plus order data for the batch of orders to determine which physical containers are associated with each order. The online system compares this first image to a subsequently received image to determine whether the correct physical containers were delivered by the user.
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
43.
PREDICTING USER LOCATION DURING AN ATTENDED DELIVERY USING A MACHINE LEARNED MODEL
An online system predicts whether a user will be at a delivery location at a delivery time for an attended delivery of an order using a machine-learned model. The online system receives the order from a client device of a user and a request by the user for an attended delivery of the order where the user will be at the delivery location at the delivery time of the order. The machine-learned model predicts that the user will not be at the delivery location at the delivery time based on user attributes of the user and order attributes of the order that are input into the machine-learned model. The online system performs a remedial action including transmitting a notification to the client device of the user to provide additional instructions for the attended delivery responsive to the determination that the user is not likely to be at the delivery location.
An online concierge system receives item data for an item included among an inventory at a retailer location, in which the item data includes a set of real-time item data for the item and a set of constraints. The system accesses and applies a first machine-learning model to predict a freshness satisfaction score for the item based at least in part on the item data. The system updates the item data to include the score and accesses and applies a second machine-learning model to predict an elasticity of demand for the item based at least in part on the updated item data. The system determines an optimal value associated with the item based at least in part on the freshness satisfaction score, the elasticity of demand, and the set of constraints. A value associated with the item is then adjusted based at least in part on the optimal value.
An online system configures one or more system Al agent instances that interact with user Al agents and performs one or more tasks on behalf of the online system. Thus, responsive to detecting the presence of a user Al agent representing a particular user, the online system directs the session for the user to communicate and interact with a system Al agent.
An online system configures one or more system AI agent instances that interact with user AI agents and performs one or more tasks on behalf of the online system. Thus, responsive to detecting the presence of a user AI agent representing a particular user, the online system directs the session for the user to communicate and interact with a system AI agent.
A chat interface supported by language models is used for generating a group order at an online system based on a conversation between multiple users. Upon receiving, via the chat interface, input data with information about the conversation, the online system requests a first language model to generate, based on the input data, a list of ingredients. The online system then requests a second language model to map the list of ingredients into a list of items at a retailer associated with the online system. Upon generation of the list of items, the online system causes the chat interface to display content prompting approval by the users for conversion of the list of items. Responsive to the approval, the online system places the group order that includes the list of items for delivery to a user of the online system.
G06F 40/35 - Représentation du discours ou du dialogue
G06F 40/40 - Traitement ou traduction du langage naturel
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
48.
PERSONALIZED PRESENTATION OF CONTENT BASED ON LOCATION DATA CAPTURED FROM SMART CART SYSTEMS
A system stores content items at a content data store, each content item corresponding to an item within an environment. The system accesses location data captured by a plurality of location sensors in the environment, each coupled to a smart cart system located within the environment. The location data indicates a current location of a corresponding smart cart system. The system computes a number of smart cart systems within a threshold area around a display screen within the environment based on the location data. The system computes a presentation score for each of the content items by combining a context relevance score and a personal relevance score weighted based on a personalization weighting. The system selects a subset of the content items for display based on the presentation scores and causes the display screen to present the subset of content items.
A system causes a display screen to present a set of content for a first time period. The system accesses a first set of location data captured by location sensors coupled to shopping carts. The first set of location data indicates a location of each of a plurality of users of the shopping carts ]. The system identifies a set of users within a distal proximity of the display screen during the first time period. The system accesses sensor data captured by sensors of the shopping carts and detects an action performed by a first user in relation to a first item. The system identifies a timestamp for the action. In response to the timestamp being within a threshold amount of time after the first time period, the system stores proximity data indicative of an interaction with the set of content in association with the first user.
A system may store a plurality of images depicting items within an environment, where each image was captured by a camera coupled to a shopping cart. The system identifies a target item associated with a user device that is located within the environment. The system identifies a location of the target item within the environment based on item data associated with the target item and environment map data describing the environment. The system selects, from the plurality of images, an image depicting the item at the location within the environment based on the environment map data and the location data associated with each of the plurality of images. The system identifies a portion of the identified image that depicts the item by applying a machine-learning model to the identified image. The system modifies the identified portion of the identified image to highlight the target item.
A smart shopping cart identifies items using cameras and sensors. The cart captures images of items within its storage area and applies machine-learning models, such as a barcode detection model, an OCR model, and an image embedding model, to generate identifier predictions. These predictions are processed using an efficient selection algorithm, which may involve majority voting, weighted voting, or linear regression, to select the most accurate identifier. The cart updates its display and user interface with the identified item. The process may be performed primarily by the CPU to enhance computational efficiency, avoiding the latency associated with GPU data transfer. Additional techniques, such as circular buffers and frame skipping, are employed to further optimize resource usage.
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
52.
PERSONALIZED PRESENTATION OF CONTENT BASED ON LOCATION DATA CAPTURED FROM SMART CART SYSTEMS
A system stores content items at a content data store, each content item corresponding to an item within an environment. The system accesses location data captured by a plurality of location sensors in the environment, each coupled to a smart cart system located within the environment. The location data indicates a current location of a corresponding smart cart system. The system computes a number of smart cart systems within a threshold area around a display screen within the environment based on the location data. The system computes a presentation score for each of the content items by combining a context relevance score and a personal relevance score weighted based on a personalization weighting. The system selects a subset of the content items for display based on the presentation scores and causes the display screen to present the subset of content items.
A system causes a display screen to present a set of content for a first time period. The system accesses a first set of location data captured by location sensors coupled to shopping carts. The first set of location data indicates a location of each of a plurality of users of the shopping carts]. The system identifies a set of users within a distal proximity of the display screen during the first time period. The system accesses sensor data captured by sensors of the shopping carts and detects an action performed by a first user in relation to a first item. The system identifies a timestamp for the action. In response to the timestamp being within a threshold amount of time after the first time period, the system stores proximity data indicative of an interaction with the set of content in association with the first user.
A system may store a plurality of images depicting items within an environment, where each image was captured by a camera coupled to a shopping cart. The system identifies a target item associated with a user device that is located within the environment. The system identifies a location of the target item within the environment based on item data associated with the target item and environment map data describing the environment. The system selects, from the plurality of images, an image depicting the item at the location within the environment based on the environment map data and the location data associated with each of the plurality of images. The system identifies a portion of the identified image that depicts the item by applying a machine-learning model to the identified image. The system modifies the identified portion of the identified image to highlight the target item.
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.
A smart shopping cart identifies items using cameras and sensors. The cart captures images of items within its storage area and applies machine-learning models, such as a barcode detection model, an OCR model, and an image embedding model, to generate identifier predictions. These predictions are processed using an efficient selection algorithm, which may involve majority voting, weighted voting, or linear regression, to select the most accurate identifier. The cart updates its display and user interface with the identified item. The process may be performed primarily by the CPU to enhance computational efficiency, avoiding the latency associated with GPU data transfer. Additional techniques, such as circular buffers and frame skipping, are employed to further optimize resource usage.
An online security system identifies matching client devices by comparing location data points acquired during application workflow from different devices. The location data points comprise a location of the device and a timestamp. Identifiable pairs of data points are collated from different devices when they show a device present within a threshold distance or the same geographic region at identical times. The system utilizes a set of matching criteria to decide whether one data set for one device corresponds with a set from another. Verification of the matches allows the system to ascertain the same user is operating both devices and link the user to both devices. This system enhances security by identifying users likely gaining unauthorized access through multiple device usage simultaneously.
METHOD, COMPUTER PROGRAM PRODUCT, AND SYSTEM FOR TRAINING A MACHINE LEARNING MODEL TO GENERATE USER EMBEDDINGS AND RECIPE EMBEDDINGS IN A COMMON LATENT SPACE FOR RECOMMENDING ONE OR MORE RECIPES TO A USER
An online concierge system generates recipe embeddings for recipes including multiple items and user embeddings for users, with the recipe embeddings and user embeddings in a common latent space. To generate the user embeddings and the recipe embeddings, a model includes separate layers for a user model outputting user embeddings and for a recipe model outputting recipe embeddings. When training the model, a weight matrix generates a predicted dietary preference type for a user embedding and for a recipe embedding and adjusts the user model or the recipe model based on differences between the predicted dietary preference type and a dietary preference type applied to the user embedding and to the recipe embedding. Additionally cross-modal layers generate a predicted user embedding from a recipe embedding and generate a predicted recipe embedding from a user embedding that are used to further refine the user model and the recipe model.
A scrollable listing of icons associated with catalogs is displayed, in which the scrollable listing of icons is overlaid onto a page when the page is scrolled, each icon is associated with a catalog, and each icon is displayed with an indication of a set of items selected from a corresponding catalog. Status information may be displayed with one or more of the icons. In response to a user selection of an icon from the scrollable listing of icons, the page is updated to include items included in a catalog associated with the selected icon. In response to a user selection to add an item from the page including the items, the indication displayed with the selected icon in the scrollable listing of icons is updated to indicate the added item.
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.
G06Q 10/087 - Gestion d’inventaires ou de stocks, p. ex. exécution des commandes, approvisionnement ou régularisation par rapport aux commandes
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
61.
MACHINE LEARNING MODEL FOR DETERMINING A TIME INTERVAL TO DELAY BATCHING DECISION FOR AN ORDER RECEIVED BY AN ONLINE CONCIERGE SYSTEM TO COMBINE ORDERS WHILE MINIMIZING PROBABILITY OF LATE FULFILLMENT
An online concierge identifies orders to shoppers, allowing shoppers to select orders for fulfillment. The online concierge system may generate batches that include multiple orders, allowing a shopper to select a batch to fulfill multiple orders. As orders are continuously being received, delaying identification of orders to shoppers may allow greater batching of orders. To allow greater opportunities for batching, the online concierge system estimates a benefit for delaying identification of an order by different time intervals and predicts an amount of time to fulfill the order. The online concierge system then delays assigning orders for which there is a threshold benefit for delaying and selects a time interval for delaying identification of the order that does not result in greater than a threshold likelihood of a late fulfillment of the order.
ALLOCATING SHOPPERS FOR ORDER FULFILLMENT BY AN ONLINE CONCIERGE SYSTEM ACCOUNTING FOR VARIABLE NUMBERS OF SHOPPERS ACROSS DIFFERENT TIME WINDOWS AND VARYING CAPABILITIES FOR FULFILLING ORDERS
An online concierge system facilitates order fulfillment by maintaining discrete time intervals for deliveries and a hierarchical data structure encoding picker characteristics. Each level in the tree structure represents a fulfillment capability and is assigned a value. Upon receiving an order specifying items and a time interval, the system applies a machine learning model to predict the number and capability levels of available pickers for the specified interval. The model is trained using historical data labeled with picker availability and their corresponding capability levels. Training includes predicting picker counts per level, computing an error metric, and updating model parameters to minimize error. The system further analyzes the order to assign tags that map to required picker characteristics, aligning them with corresponding levels in the tree structure.
An online concierge system schedules pickers (shoppers) to fulfill orders from users. During periods of peak demand, the system increases compensation to shoppers to encourage more to participate, thereby reducing missed orders. The system determines an optimal multiplier to increase compensation based on predictive models of supply and demand and then applying an optimization algorithm to search different hyperparameters that affect how the models generate the multipliers. The system selects the optimal multipliers for different time periods and locations. The system may further present the multipliers being offered during future time periods and enable users to activate reminder alerts for select periods. The offers may be presented in a ranked list using a model trained to infer likelihoods of the user accepting participation and/or setting a reminder notification.
A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.
A system collects user data describing characteristics of multiple users. A first machine-learning model assesses this data to predict churn scores of the users. When a user sends an error signal concerning their experience with the system, the system retrieves a identified churn score for this user and applies a second machine-learning model. This second model takes as input user data and their churn score to select a corrective action among a set of corrective actions aimed at reducing the user's churn score. After implementing the selected corrective action, the system collects and updates the user's data to reflect their continued engagement or departure. The system uses this updated user data to retrain the first or second model to improve the predictive accuracy of the first or second model.
An online system trains a language model to generate an association between receipt labels and item names, enabling the online system to identify discrepancies between receipt labels and item names. The online system identifies training label-name pairs from a plurality of orders including a set of item names and associated receipt labels, wherein the training label-name pairs are optimized based on fuzzy matching and/or statistical association scores. The online system fine-tunes the language model to perform tasks for translating between receipt labels and item names or scoring receipt labels and item pairs based on statistical association.
An online system generates text-based tags for item sources to dynamically generate customized clusters of the item sources for a user. The online system selects a set of item categories within the taxonomy based on interaction rate data of users with the item source. The online system uses these selected categories to generate tags for the item source. The online system generates a prompt for an LLM to generate an item source cluster for a set of item sources. The prompt includes the generated tags for the item sources and instructions on how to select item sources to include in the cluster based on the tags. The online system receives a response from the LLM that specifies which item sources to include in the item source cluster and the online system transmits instructions to a client device to present the item source cluster in a user interface.
An online system receives from a device associated with a picker, an image of an order delivered at a location associated with the order for a user and accesses a plurality of features about the order to output a likelihood that the delivered order in the received image is erroneous. The online system applies a machine learning model to the received image of the order and the plurality of features of the order. The machine learning model is trained to predict a likelihood that the delivered order is erroneous. The online system determines that the delivered order is erroneous and transmits a warning message to the device associated with the picker about the identified potential delivery error.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects
G06Q 10/0832 - Marchandises spéciales ou procédures de manutention spéciales, p. ex. manutention de marchandises dangereuses ou fragiles
An online concierge system facilitates ordering, procurement, and delivery of items to a customer from physical retailers based on shared cart recommendations. Based on customer identifying information and other data sources, the online concierge system may recommend prepopulated shared carts that may be of interest to a customer. The prepopulated carts may be associated with other users of the online concierge system or may be associated with specific events, locations, or other metadata. Prepopulated carts may be created by other users that select to share their carts. Additionally, prepopulated carts may be created and shared by retailers, manufacturers, wholesalers, or other stakeholders in the selling of items through the online concierge system. Furthermore, recommended carts may be automatically generated based on machine learning techniques.
An online concierge system receives, from a user client device associated with a user of the online concierge system, a request to access a user interface including information describing one or more items included among an inventory at a retailer location. The system then retrieves a set of item data for an item included among the inventory at the retailer location. The system accesses and applies a machine-learning model to predict a freshness satisfaction score for the item based at least in part on the set of item data for the item. The system then generates the user interface including the information describing the item(s) based at least in part on the freshness satisfaction score for the item and sends the user interface to the user client device, causing the user client device to display the user interface.
An online system retrieves engagement data associated with a base query made by a user for an item, the engagement data describing in part subsequent queries for other items following the base query in a single search session. The system generates a prompt that is provided to a machine learned model. The prompt instructs the machine learned model to generate one or more groups of related queries using the subsequent queries. The system selects a group of related queries from the one or more groups of related queries. The system queries an online catalog using at least some of the related queries from the selected group to determine supplemental search results. The system provides, to a user client device associated with the user, the supplemental search results.
An online system maintains a shared cache for storing actions assigned to users in different experiment groups. The system receives an indication that a user interacted with an online system, and data associated with the user. The system generates a set of propensities for a set of actions by identifying a first set of features for the user, accessing a first machine learning model, and applying the first machine learning model to the first set of features. The system selects an action based on the set of propensities and presents the action to the user. The system updates a cache of a set of user data and includes the transmitted action. The system receives a second indication and accesses a database to determine a selected action stored in association to the user. The system presents the selected action for a second time to the user.
A trained model is used to predict, in real time, a late delivery rate for orders placed at an online system. Upon receiving data related to the placed orders and signals related to supply and demand states of the online system, the online system applies a delivery prediction model trained to output, based on the received data and signals, late delivery scores, each late delivery score indicative of a respective rate of late deliveries for a respective service option for delivery of the orders. The online system compares each late delivery score with a respective threshold score, and responsive to each late delivery score being greater than the respective threshold score, the online system triggers an action in relation to the respective service option for delivery of the orders.
An online system receives from a device associated with a picker, an image of an order delivered at a location associated with the order for a user and accesses a plurality of features about the order to output a likelihood that the delivered order in the received image is erroneous. The online system applies a machine learning model to the received image of the order and the plurality of features of the order. The machine learning model is trained to predict a likelihood that the delivered order is erroneous. The online system determines that the delivered order is erroneous and transmits a warning message to the device associated with the picker about the identified potential delivery error.
G06Q 10/087 - Gestion d’inventaires ou de stocks, p. ex. exécution des commandes, approvisionnement ou régularisation par rapport aux commandes
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
A trained model is used to select a layout template for a search results user interface displayed at a device associated with a user of the online system. Upon receiving a search query via a user interface of the device, the online system applies a search query model trained to identify, based on the search query and user data, a set of search results. Upon identifying the set of search results, the online system applies a layout selection model trained to identify, based at least in part on the set of search results, a layout for the search results user interface. The online system causes the device associated with the user to display the set of search results at the search results user interface using the identified layout for the search results user interface.
An online concierge system receives a query from a user and leverages a set of models to generate an order based on the query. An ingredient identification model is a generative model that receives the query and generates a set of item categories corresponding to items that are combined to satisfy the query. An item identification model trained on catalogs of items offered by retailers receives the set of item categories as an input and generates a list of items available at a retailer corresponding to the set of item categories. The item identification model may generate multiple lists corresponding to different retailers and select a specific list of items based on list scores determined for each list. A candidate order form creation model generates characteristics of an order for obtaining the list of items that leverages prior orders fulfilled for the user.
An online concierge system (“the system”) determines that a shopping list from a user client device includes a request for a quantity of an item that exceeds a quantity that can be fulfilled using a single retailer. Responsive to the determination, the system retrieves model inputs based in part on the request. The system determines segmenting options, for fulfilling the request using multiple sources, and their associated costs using a machine learned model and the model inputs. The segmenting options include different combinations of pickers and sources that can be used to fulfill the request. The system provides one or more of the segmenting options and their associated costs to the user client device. Responsive to receiving, from the user client device, a segmenting option of the one or more segmenting options, the system fulfills the request in accordance with the segmenting option.
An online system for determining quality improvement actions responsive to an item being unavailable at source location after an order was placed. The system receives an indication from a picker client device that a requested item from an order to be fulfilled at a source location is unavailable at the source location. The system retrieves model inputs based in part on the indication. The model inputs may include availability information for the requested item at least one other source location, a foundational item status of the requested item, and a tenure of the user. The system determines a quality improvement action for the requested item using a machine learned model (an order quality model) and the model inputs. The system performs the determined quality improvement action.
An online concierge system trains a computer model to map receipt item labels to order item identifiers, enabling the online concierge system to identify discrepancies between receipt items and customer order items. The online concierge system identifies a training set of data comprising quantities of receipt item labels and corresponding orders having quantities of order item identifiers and trains the computer model to predict quantities of order item identifiers based on the set of training data. The online concierge system applies a one-hot encoding for a receipt item label to determine predicted order item identifiers and maps the receipt item label to order item identifiers based on the predictions.
G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
81.
Search Engine for Recommending Search Queries Based on User Interactions Using a Transformer-Based Language Model
An online system provides a search interface for a user to identify items. The search interface may present suggested search queries to the user, allowing the user to select a suggested search query rather than manually entering search terms to form a search query. To identify search queries most likely to be selected by the user, the online system gets a set of candidate search queries and generates a relevance score for each candidate search query by applying a trained query relevance model to each candidate search query. The scored candidate search queries are selected and ranked using the relevance scores, and the selected candidate search queries are displayed using the ranking in the search interface. The query relevance model is a transformer-based small language model receiving a user sequence of prior search queries and items with which the user interacted and the candidate search terms as input.
A smart shopping cart includes a load sensor to measure the weight of items added to the cart. To avoid waiting for the load sensor to converge, a detection system predicts the weight of items added to the storage area of a smart shopping cart based on the shape of a load curve output by the load sensor when an item is added to the cart. The detection system receives load data from the load sensor, detects that an item was added to the storage area of the shopping cart during a time period and identifies a set of load measurements captured by the load sensor during the time period. The set of load measurements comprise a load curve, to which the detection system applies a weight prediction model to generate a predicted weight of the added item.
A smart shopping cart may utilize cameras and/or load sensors to provide capacity- informed recommendations. The cameras are positioned facing at least a first basket of the smart shopping cart and configured to capture image data during a visit at a retailer location. The load sensors are configured to measure load data during a visit at the retailer location. The cart detects obtained items entering the first basket based on the image data and the load data. The cart identified remaining capacity in the first basket based on the image data and the load data. The cart applies a capacity-informed model to the one or more obtained items and the remaining capacity in the first basket to identify one or more recommended items. The cart displays, via an electronic display, the one or more recommended items.
A smart shopping cart may utilize cameras to identify fulfillment instructions to maximize efficiency. A fulfillment user may be tasked to fulfill a batch of orders at a retailer location. Each order includes one or more items to be obtained. The cart captures image data via one or more cameras in view of the cart's baskets. From the image data, the cart can detect obtained items placed in the baskets and can generate an occupancy state of the baskets indicating a configuration of each obtained item in the baskets. The cart can apply a fulfillment optimization model to the occupancy state to identify a next item to be obtained in the batch of orders and an optimal packing configuration for the next item. The cart can display to the fulfillment user the next item and the optimal packing configuration.
A smart shopping cart includes a load sensor to measure the weight of items added to the cart. To avoid waiting for the load sensor to converge, a detection system predicts the weight of items added to the storage area of a smart shopping cart based on the shape of a load curve output by the load sensor when an item is added to the cart. The detection system receives load data from the load sensor, detects that an item was added to the storage area of the shopping cart during a time period and identifies a set of load measurements captured by the load sensor during the time period. The set of load measurements comprise a load curve, to which the detection system applies a weight prediction model to generate a predicted weight of the added item.
G01G 19/414 - Appareils ou méthodes de pesée adaptés à des fins particulières non prévues dans les groupes avec dispositions pour indiquer, enregistrer ou calculer un prix ou d'autres quantités dépendant du poids utilisant des moyens de calcul électromécaniques ou électroniques utilisant uniquement des moyens de calcul électroniques
G01G 19/52 - Appareils de pesée combinés avec d'autres objets, p. ex. avec de l'ameublement
86.
Displaying an interactive geographical map of sources
An online system receives a request to access an interactive geographical map of sources from a client device associated with a user. The system retrieves data describing a geographical location associated with the user. The system identifies one or more sources within a threshold distance of the location and retrieves data including information describing items available at each source. For each source, the system accesses and applies a machine-learning model to predict a user engagement score indicating a likelihood of one or more interactions by the user with a set of items available at the source if the source is included in the map. Based on the score for each source, the system selects a set of sources and generates the map, in which the map indicates the geographical location of each selected source. The map is then sent to the client device, causing the device to display the map.
An online system manages the availability schedules of fulfillment agents utilizing a favorite order prediction model to predict likelihood of receiving a favorite order. The system receives a request from a fulfillment agent to set an availability schedule for a forthcoming time period. The system applies a prediction model to each of a plurality of discretized time slots of the time period to predict the favorite order likelihood. The model may be trained by the system: retrieving a profile for the fulfillment agent comprising a list of requesting user(s) that have favorited the fulfillment agent, and training the model based on order histories of the list of requesting user(s). The system generates and provides an interface displaying the time slots with a visual indication for each time slot based on its predicted likelihood, e.g., a heat map of likelihoods of receiving a favorite order across the time slots.
A smart shopping cart may utilize cameras and/or load sensors to provide capacity-informed recommendations. The cameras are positioned facing at least a first basket of the smart shopping cart and configured to capture image data during a visit at a retailer location. The load sensors are configured to measure load data during a visit at the retailer location. The cart detects obtained items entering the first basket based on the image data and the load data. The cart identified remaining capacity in the first basket based on the image data and the load data. The cart applies a capacity-informed model to the one or more obtained items and the remaining capacity in the first basket to identify one or more recommended items. The cart displays, via an electronic display, the one or more recommended items.
A smart shopping cart may utilize cameras to identify fulfillment instructions to maximize efficiency. A fulfillment user may be tasked to fulfill a batch of orders at a retailer location. Each order includes one or more items to be obtained. The cart captures image data via one or more cameras in view of the cart's baskets. From the image data, the cart can detect obtained items placed in the baskets and can generate an occupancy state of the baskets indicating a configuration of each obtained item in the baskets. The cart can apply a fulfillment optimization model to the occupancy state to identify a next item to be obtained in the batch of orders and an optimal packing configuration for the next item. The cart can display to the fulfillment user the next item and the optimal packing configuration.
A multi-generation RAG process generates a first-generation prompt for input to LLMs. The first-generation prompt may specify a concept, raw data, and a first-generation request to draw inference of information related to the concept using the raw data. The process provides the first-generation prompt for execution by the LLMs and receives a first-generation response. The process iteratively updates the inference of information using the LLMs. The iteratively updating includes using the inference of information related to the concept that are received from a previous-generation response as contextual information for a subsequent-generation RAG process. The process receives a subsequent-generation response generated by executing the LLMs and stores the iteratively updated inference of information related to the concept for retrieval by the machine-learned language model.
A system reduces user wait times for order pickup by dynamically managing geofences based on user location and historical data. The system receives an order and route-based location data from a user device, and determines a personalized outer geofence using the user's estimated arrival time and a runner's estimated retrieval time, incorporating historical wait time data. An inner geofence is established near the pickup location. When the user enters the outer geofence, a notification is sent to a runner client device indicating the user is en route. Upon the user entering the inner geofence, the system initiates a wait time measurement, which ends upon confirmation of order pickup. The system calculates the user's wait time based on timestamps associated with these events. This computed wait time is then used to determine an outer geofence for a second user, allowing continuous geofence optimization based on evolving user and location data.
A system or method for verifying delivery of items in an order using user and delivery client devices. The system receives, from a user device, an order containing at least one item requiring delivery verification. The system determines that a delivery device is within a geofence around the delivery location and sends instructions to display a user interface for initiating verification. Upon interaction, the system instructs the user device to display a signature interface. If the user cannot provide a signature, the system sends instructions to the delivery device to present a fallback verification interface, including at least one of: a document scanning element, image capture element, or text fields for manual entry of identification information. Verification data collected through this interface is received from the delivery device and stored in a verification database, certifying delivery of the order.
An online concierge system fulfills orders for items offered by retailers and may increase the price of an item offered by a retailer in some instances. The online concierge system applies a markup to an item by applying a pricing policy to a category including the item. To optimize application of pricing policies to categories, the online concierge system categorizes items offered by the retailer and applies an outcome model to combinations of categories and pricing policies. From the output of the outcome model, the online concierge system selects a set of categories and corresponding pricing policies. Using a price adjustment model, the online concierge system determines modifications to one or more of the pricing policies of the set to enforce one or more constraints across multiple pricing policies.
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
G06Q 10/087 - Gestion d’inventaires ou de stocks, p. ex. exécution des commandes, approvisionnement ou régularisation par rapport aux commandes
94.
Using a Trained Model of an Online System to Generate Action Recommendations by Predicting Future Demand
A trained model of an online system is used to generate action recommendations by predicting future demands. The online system gathers in-store data by receiving, from a device of a picker and/or a computing system of an in-store physical receptacle, data with information about an inventory of an item. The online system estimates, based on conversion data for the item, a level of inventory for the item. The trained model is then applied to predict, based on the in-store data and the estimated level of inventory, a demand prediction score indicative of a future demand for the item. The online system generates, based on the estimated level of inventory and the demand prediction score, a depletion metric indicative of a time period until the inventory of the item is depleted. Based on the depletion metric, the online system triggers an action in relation to the inventory of the item.
A language model is used to suggest content based on preferences of a user of an online system and data queried from a catalog database of the online system. The online system gathers input data including a set of recipes and user data and generates a prompt for input into the language model that includes the input data. The online system requests the language model to generate, based on the prompt input into the language model, the list of recipes for the user, wherein each recipe includes a list of ingredients. The online system selects one or more recipes from the list of recipes for presentation to the user. The online system causes a device associated with the user to display a user interface with a suggestion for the user to include, in a cart, a set of ingredients of the selected one or more recipes.
A trained model of an online system is used to display elements of a user interface of a user's device in a manner that increases a likelihood of user's engagement. Upon receiving a request from the user for a page of user interface elements and retrieving a set of user interface elements, the online system applies the trained model to predict a likelihood of user's engagement with each user interface element in the set for each incentive amount of multiple incentive amounts. The online system identifies, based on the likelihood of user's engagement with each user interface element for each incentive amount, an optimal incentive amount for each user interface element. The online system ranks the set of user interface elements based on the likelihood of user's engagement for the optimal incentive amount and displays the set of user interface elements at the user interface according to the ranking.
G06Q 30/0207 - Remises ou incitations, p. ex. coupons ou rabais
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06Q 30/0202 - Prédictions ou prévisions du marché pour les activités commerciales
97.
USING A GENERATIVE ARTIFICIAL INTELLIGENCE MODEL TO GENERATE AN IMAGE OF AN ITEM INCLUDED IN AN ORDER ACCORDING TO A PREDICTED USER PREFERENCE ASSOCIATED WITH THE ITEM
An online system retrieves user data for a user and applies a machine-learning model to predict a measure of preference of the user associated with an item category based on the user data. The system receives an order including an item in the item category and generates a prompt including the predicted measure of preference and a request to generate an image of the item that is consistent with the predicted measure of preference. The system provides the prompt to a generative artificial intelligence model to obtain an output and extracts, from the output, the image of the item that is consistent with the predicted measure of preference. The system sends the image to a picker client device associated with a picker to which the order is assigned, causing the device to display the image in association with instructions to collect the item to service the order.
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
98.
AUTOMATIC ROUTING OF USER INQUIRIES USING MACHINE-LEARNING MODELS
A system or a method for intelligently routing user inquiries to knowledgeable retail shoppers using machine learning. Upon receiving an inquiry from a client device that includes both text and image content, the system applies machine learning models to identify item categories referenced in the text and shown in the image. The system uses an item availability model—trained on historical retailer inventory data—to identify a retailer likely to carry items in the identified categories and transmits suggestion information to the user's device, prompting a user interface that recommends the retailer. The system selects a shopper associated with the retailer who has subject matter expertise in the relevant item categories. Expertise is determined using a machine-learned model trained on labeled data from historical shopper orders. The system then forwards the user inquiry to the expert shopper's device, enabling direct communication and facilitating more accurate, personalized retail assistance.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
An online concierge system provides arrival prediction services for a user placing an order to be retrieved by a shopper. An order may have a predicted arrival time predicted by a model that may err under some conditions. To reduce the likelihood of providing the predicted arrival time (and related services) when the arrival time may be incorrect, the prediction model and related services are throttled (e.g., selectively provided) based on one or more predicted delivery metrics, which may include a time to accept the order by a shopper and a predicted portion of late orders that will be delivered past the respective predicted arrival times. The predicted delivery metrics are compared with thresholds and the result of the comparison used to selectively provide, or not provide, the predicted delivery services.
A system and method for predicting code ownership of software components using machine learning. The system accesses a set of software components, each linked to a known code owner, and extracts two sets of features: one describing each software component and another describing users associated with those components. These features are used to train a machine learning model that outputs a score indicating the likelihood that a specific user is the code owner of a specific software component. Once trained, the model is executed across a plurality of users to generate likelihood scores for a given software component. Based on these scores, the system selects a predicted code owner from the user set and associates the predicted owner with the software component.
