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.
An online concierge system accesses and applies a model to predict likelihoods of acceptance of a service request for an order by pickers. The system accesses timespan distributions for accepted service requests and identifies sets of pickers based on the order. Based on the likelihoods and distributions, the system generates simulated responses of the sets of pickers to the service request and trains an additional model based on attributes of the order, the simulated responses, and information associated with corresponding sets of pickers. The system receives a new order, identifies additional sets of pickers based on the new order, and applies the additional model to predict responses of the additional sets of pickers to an additional service request for the new order. Based on the predicted responses and a delivery time associated with the new order, a minimum number of pickers to send the additional service request is determined.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
A system or a method for using machine learning to automatically route user inquiries to a retailer are presented. The system receives an inquiry from a client device associated with a user. The inquiry includes text content and an image. The system uses a natural language model to analyze the received text to identify a first category of items. The system applies the received image to an image recognition model to identify a second category of items contained in the received image. The system then identifies a retailer that carries items in at least one of the first or second category of items, and suggests the retailer to the user via the client device associated with the user. A retail associate at the retailer can then respond to the inquiry via a client device associated with the retailer.
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
4.
GENERATING A SUGGESTED SCHEDULE FOR A PICKER OF AN ONLINE CONCIERGE SYSTEM BASED ON AN EARNINGS GOAL AND AVAILABILITY INFORMATION
An online concierge system receives a goal and availability information for a picker, in which the availability information describes time slot-location pairs for which the picker is available. The system accesses and applies a first and a second machine learning model to predict a likelihood that an order will be available for service and an amount of earnings for servicing the order, respectively, for each time slot-location pair. The system computes an estimated amount of earnings for each time slot-location pair based on the predictions and generates suggested schedules that each includes one or more time slot-location pairs. For each suggested schedule, the system computes a total estimated amount of earnings based on the estimated amount of earnings and one or more costs. The system identifies a suggested schedule for achieving the goal based on the total estimated amount of earnings or an estimated amount of time included in the suggested schedule.
B62B 3/14 - Voitures à bras ayant plus d'un essieu portant les roues servant au déplacementDispositifs de direction à cet effetAppareillage à cet effet caractérisées par des moyens pour l'emboîtement ou l'empilage, p. ex. chariots pour achats
H02J 7/00 - Circuits pour la charge ou la dépolarisation des batteries ou pour alimenter des charges par des batteries
7.
TRAINING MODEL TO IDENTIFY ITEMS BASED ON IMAGE DATA AND LOAD CURVE DATA
A47F 9/04 - Comptoirs de vérification, p. ex. pour magasins à libre-service
G06Q 20/18 - Architectures de paiement impliquant des terminaux en libre-service, des distributeurs automatiques, des bornes ou des terminaux multimédia
8.
SHOPPING CART SELF-TRACKING IN AN INDOOR ENVIRONMENT
A shopping cart system detects the initiation of a shopping session within a physical retail store by a customer, in which the shopping cart system includes a shopping cart, a processor, a memory, and a set of sensors. Contextual information associated with the shopping cart received by the sensors during the shopping session is tracked, in which the contextual information describes one or more locations of the shopping cart within the store, a state of the shopping cart, and a set of items within the shopping cart. Responsive to identifying an opportunity to present content to the customer based on the contextual information, the system identifies a set of content items associated with one or more items within the store based on the contextual information. The system generates a user interface including the set of content items and sends the user interface to a display area associated with the customer.
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
10.
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 sy stem estimates a benefit for delaying identification of an order by different time intervals and predicts an amount of time to fillfill 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 identificati on of the order that does not result in greater than a threshold likelihood of a late fulfillment of the order.
Self-checkout vehicle systems and methods comprising a self-checkout vehicle having a camera(s), a weight sensor(s), and a processor configured to: (i) identify via computer vision a merchandise item selected by a shopper based on an identifier affixed to the selected item, and (ii) calculate a price of the merchandise item based on the identification and weight of the selected item. Computer vision systems and methods for identifying merchandise selected by a shopper comprising a processor configured to: (i) identify an identifier affixed to the selected merchandise and an item category of the selected merchandise, and (ii) compare the identifier and item category identified in each respective image to determine the most likely identification of the merchandise.
G01G 19/41 - 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 mécaniques
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
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
12.
DETERMINING ESTIMATED DELIVERY TIME OF ITEMS OBTAINED FROM A WAREHOUSE FOR USERS OF AN ONLINE CONCIERGE SYSTEM TO REDUCE PROBABILITIES OF DELIVERY AFTER THE ESTIMATED DELIVERY TIME
An online concierge system displays an interface to a user identifying an estimated time of arrival for an order. To generate the estimated time of arrival for the order, the online concierge system trains a prediction engine to predict delivery time based on a predicted selection time for a shopper to select the order for fulfillment and predicted travel time for the shopper to deliver items of the order to a location identified by the order. The online concierge system generates a policy optimization model that computes an adjustment for the predicted delivery time. The adjustment is determined by solving a stochastic optimization problem with a constraint on a probability of the order being delivered after the estimated time of arrival. The predicted delivery time combined with the adjustment determines the estimated time of delivery displayed to the user to balance between minimizing late deliveries and wait times.
An online concierge system allows users to order items within discrete time intervals later than a time when an order was received. Each order may require a different set of characteristics for fulfilment by shoppers. Because different shoppers may have different capabilities, it is most efficient to reserve shoppers with specialized characteristics for orders that require them. The online concierge system maintains a set of hierarchical structures for different characteristics of shoppers, with each level in a hierarchical structure having a value. To select a shopper to fulfill an order, the online concierge system scores identifies groups of shoppers having characteristics capable of fulfilling the order based on levels in the hierarchical structure for each characteristic of a group. A shopper from a group having a minimum score is selected to fulfill the order.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 10/08 - Logistique, p. ex. entreposage, chargement ou distributionGestion d’inventaires ou de stocks
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
An item recognition system uses a top camera and one or more peripheral cameras to identify items. The item recognition system may use image embeddings generated based on images captured by the cameras to generate a concatenated embedding that describes an item depicted in the image. The item recognition system may compare the concatenated embedding to reference embeddings to identify the item. Furthermore, the item recognition system may detect when items are overlapping in an image. For example, the item recognition system may apply an overlap detection model to a top image and a pixel-wise mask for the top image to detect whether an item is overlapping with another in the top image. The item recognition system notifies a user of the overlap if detected.
For each retailer in the geographic region, an online system predicts a number of orders placed at the retailer and a capacity to fulfill orders during a forecast time period. The capacity of the retailer is predicted based on a number of pickers expected to be available to the retailer during the forecast time period. The online system determines demand for the services of a picker at the retailer based on a comparison of the predicted number of orders and the predicted capacity to fulfill those orders. The online system displays a user interactive map of the geographic region to the picker. The map displays a pin at the location of each retailer in the geographic region, which describes the categorization determined for the retailer. The picker selects a pin, which causes the user interactive map to display a notification characterizing the demand for services at the retailer.
An online concierge system maintains information about items offered for purchase and users of the online concierge system. Based on prior purchases of items by users, the online concierge system trains a model to determine a likelihood of a user purchasing an item based on an embedding for the object and embedding for the user. The online concierge system identifies a collection of items and generates an embedding for the collection. The collection may be a cluster of items determined from similarities between embeddings of items. Alternatively, the collection may be a group of items having a common category. The online concierge system includes one or more collections of items along with individual items when recommending items for the users, so the trained model is applied to embeddings of the individual items and to embeddings of the one or more collections to generate recommendations for a user.
An online concierge system allows users to order items from a warehouse having multiple physical locations, allowing a user to order items at any given warehouse location. To select a warehouse location for a warehouse selected by a user, the online concierge system identifies a set of items that the user has a threshold likelihood of purchasing from prior orders by the user. For each of a set of warehouse locations, the online concierge system applies a machine-learned item availability model to each item of the identified set. From the availabilities of items of the set at each warehouse location of the set, the online concierge system selects a warehouse location. The online concierge system identifies an inventory of items from the selected warehouse location to the user for inclusion in an order.
G06Q 10/087 - Gestion d’inventaires ou de stocks, p. ex. exécution des commandes, approvisionnement ou régularisation par rapport aux commandes
G06Q 30/0202 - Prédictions ou prévisions du marché pour les activités commerciales
18.
IDENTIFYING ITEMS OFFERED BY AN ONLINE CONCIERGE SYSTEM FOR A RECEIVED QUERY BASED ON A GRAPH IDENTIFYING RELATIONSHIPS BETWEEN ITEMS AND ATTRIBUTES OF THE ITEMS
An online concierge system generates a graph connecting items with attributes of the items and other items. Hence, the graph includes nodes corresponding to attributes and nodes corresponding to items, with an item connected to attributes of the item in the graph. Example attributes include a brand, a category, a department, or any other suitable information about the item. When the online concierge system receives a search query to identify one or more items from a customer, the online concierge system parses the search query into combinations of terms and compares different combinations of terms to the graph to determine connections between different combinations of terms in the graph. Based on measures of connectedness between combinations of terms and connections in the graph, items are identified from one or more combinations of terms. Information about the identified items is presented to the customer.
A customer places an order of items to be purchased with an online concierge system. The online concierge system provides the order to a picker who shops for the items at a retailer and delivers them. The online concierge system requests an image of a receipt of the order from the picker. The online concierge system performs image processing on the image of the receipt and uses machine learning and optical character recognition to determine the actual amounts purchased of items. The online concierge system charges the customer based on the actual amounts purchased of each item.
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
An online system provides options for selection by a user. The online system receives a query entered on a client device. The online system queries an item database to retrieve a set of items related to the query and assigns each item to a product category in a predefined taxonomy that maps items to product categories. The online system inputs each item into a prediction model trained to predict a probability that an item is available at a warehouse location. The online system determines that a first product category has low availability based on predicted probabilities for items in the first product category. Responsive to determining that a first product category has low availability, the online system generates a generic item for the first product category and sends a list of items including the generic item to the client device for display responsive to the query.
An online concierge system may determine recommended search terms for a user. The online concierge system may receive a request from a user to view a user interface configured to receive a search query. The online concierge system retrieves long-term activity data including previous search terms entered by the user while searching for items to add to an online shopping cart. For each previous search term, the online concierge system retrieves categorical search terms corresponding to one or more categories to which the previous search term was mapped. The online concierge system determines a set of nearby categorical search terms and sends, for display via a client device, the set of nearby categorical search terms as recommended search terms.
Disclosed are technologies for generating training data for identification neural networks. Series of images are captured of a plurality of merchandise items from different angles and with different background assortments of other merchandise items. A labeled training dataset is generated for the plurality of merchandise items. The series of captured images is normalized, where the merchandise occupies a threshold percentage of pixels in the normalized image. The training dataset is extended by applying augmentation operations to the normalized images to generate a plurality of augmented images. Each image is stored in the training dataset as a unique training data point for the given merchandise item it depicts. Labels are generated mapping each training data point to attributes associated with the depicted merchandise item. Input neural networks are trained on the labeled training dataset to perform real-time identification of selected merchandise items placed into a self-checkout apparatus by a user.
G06Q 10/087 - Gestion d’inventaires ou de stocks, p. ex. exécution des commandes, approvisionnement ou régularisation par rapport aux commandes
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
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
In an online concierge system, a customer adds iterns to an online shopping cart. The online concierge systern determines key ingredients frorn the items in the online shopping cart by mapping the items to generic items and removing non-ingredient items and staple items. The online concierge system retrieves recipes including at least one of the key ingredients. The online concierge system determines complernentary ingredients based on the other ingredients in the recipes and calculates co-occurrence scores for the complementary ingredients. Using the co-occurrence scores, the online concierge system ranks the complementary ingredients and sends for display a subset of the complernentary ingredients as recommended iterns.
In a delivery service, a picker retrieves items specified in an order by a customer. If a picker encounters an issue with an item in the order, the picker may select, via a user interface, the item and an associated template message, which requests input from the customer regarding a course of action for the item, to send to the customer. The customer may select, via another user interface, a template message describing a course of action for the item. In response to receiving one of a subset of template messages, the online concierge system displays via the user interface, a set of replacement options to the customer, who may select one of the replacement options to be sent to the picker with the template message.
H04M 1/72436 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens de soutien local des applications accroissant la fonctionnalité avec des moyens interactifs de gestion interne des messages pour la messagerie textuelle, p. ex. services de messagerie courte [SMS] ou courriels
An online concierge system receives an order from a customer. The online concierge system transmits a notification to the customer's client device indicating that the order is ready for pick up and receives location data from the customer's client device as the customer travels to a pickup location. In response to the online concierge system receiving a first indication that the customer has entered an outer geofence, the online concierge system transmits a second notification to a runner's client device that the customer is in transit. In response to the online concierge system receiving a second indication that the customer has entered an inner geofence, the online concierge system starts a timer. When the online system receives a confirmation that the order has been picked up by the customer, it stops the timer and computes a wait time for pick up of the order.
Disclosed herein relates to a system, comprising: at least one load receiver mounted on a shopping cart or basket and configured to receive an item placed into the shopping cart or basket for a weighing operation; a plurality of sensors configured to detect a plurality of parameters relating to the weighing operation of the item including at least one of: a relative angle between a force sensing axis of the at least one load receiver and a direction of gravity, a motion of the shopping cart or basket, and an ambient temperature surrounding the shopping cart or basket and the at least one load receiver; and a processor configured to determine an actual weight of the item based on at least a portion of the plurality of parameters.
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 method for populating an inventory catalog includes receiving an image showing an item in the inventory catalog and comprising a plurality of pixels. A machine learned segmentation neural network is retrieved to determine location of pixels in an image that are associated with an image label and the property. The method determines a subset of pixels associated with the item label in the received image and identifies locations of the subset of pixels of the received image, and extracts the subset of pixels from the received image. The method retrieves a machine learned classifier to determine whether an image shows the item label. The method determines, using the machine learned classifier, that the extracted subset of pixels shows the item label. The method updates the inventory catalog for the item to indicate that the item has the property associated with the item label.
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
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
29.
SYSTEMS AND METHODS FOR INTELLIGENT PROMOTION DESIGN IN BRICK AND MORTAR RETAILERS WITH PROMOTION SCORING
Systems and methods for optimizing promotions within a physical retail space are provided. Electronic tags are deployed throughout the retail space. These tags are wirelessly coupled to a server system, allowing for real time and simultaneous updating of pricing and other promotional variables. These tags enable expansive testing of base pricing, promotion optimization, and sell through criteria. Testing may be performed on a wide range of promotional variables to determine what sorts of values for these variables yield the most effective promotions. Price elasticity for individual products can likewise be tracked through price adjustment testing for determining sell through scheduling. Further, by tracking individual consumers through the retail space, personalized promotions can be presented to the individuals.
An online shopping concierge service allows shoppers to purchase items on behalf of customers and checkout using a mobile application, circumventing traditional point-of-sale check-out systems. A customer places an order using a mobile application or website associated with the online shopping concierge service. The online shopping concierge service charges a payment instrument of the customer in the value of the selected items. The system transmits the order to a shopper, who receives an order for fulfillment on a mobile device. The shopper collects and scans items using a mobile application. The mobile application transmits an identification of the items for purchase and their total cost to the online shopping concierge service, which transmits payment to the retailer. Alternatively, the mobile application encodes an identification of the items for purchase into an encoded image, which is scanned by a cashier, allowing the shopper to complete an accelerated check-out.
brevets
