Disclosed herein relates to a self-checkout anti-theft vehicle system, comprising: a self-checkout vehicle having a plurality of sensors and components implemented thereon, the self-checkout vehicle being used by shoppers for storing selected merchandises in a retail environment; and a centralized computing device. The centralized computing device is configured to: obtain information related to each merchandise selected and placed into the self-checkout vehicle by a shopper by exchanging data with the plurality of sensors and components via a first communication network, identify each merchandise via a second, different communication network based at least upon the information obtained from the plurality of sensors and components, and process payment information of each merchandise.
G06Q 20/18 - Architectures de paiement impliquant des terminaux en libre-service, des distributeurs automatiques, des bornes ou des terminaux multimédia
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
G06K 7/10 - 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
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
G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
G06N 5/01 - Techniques de recherche dynamiqueHeuristiquesArbres dynamiquesSéparation et évaluation
G06N 5/022 - Ingénierie de la connaissanceAcquisition de la connaissance
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
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
An online system generates personalized content carousels and personalized content items in conjunction with large language models (LLMs). The personalized carousels and items are generated subject to consent from users. In one or more embodiments, a content item is a recipe page, coupon, incentive, advertisement, sponsored page, or sponsored item, accessed via the online system. The online system generates one or more carousel themes for the user based on the order history of the user by prompting the LLM. For each carousel theme, the online system also generates a set of content item names. The online system applies an embedding model to identify content items that are relevant to each content name. One or more content carousels including the set of content items are presented to the user.
An online system maintains various items and maintains values for different attributes of the items, as well as an item embedding for each item. When the online system receives a query for retrieving one or more items, the online system generates an embedding for the query. Based on measures of similarity between the embedding for the query and item embeddings, the online system selects a set of items. The online system identifies a specific attribute of items and generates a whitelist of values for the specific attribute based on measures of similarity between item embeddings for items in the selected set and the embedding for the query. The online system removes items having values for the selected attribute outside of the whitelist of values from the selected set of items to identify items more likely to be relevant to the query.
A receipt capture device can collect transaction information from transactions conducted at a point of sale system by capturing receipt data transmitted from the point of sale system for the purpose of printing receipts at an external receipt printer. The receipt capture device can then send the collected receipt data to an online system for analysis. At the online system, received receipt data can be decoded from the printer-readable format it is transmitted in and used to enhance the online system's understanding of transactions occurring at a retailer associated with the point of sale system. For example, the online system can determine an approximate inventory of items available at purchase at the retailer by aggregating items recently purchased in transactions at the point of sale system.
A ranking computer model is trained based on grouping a collection of users of an online system into different buckets based on intended likelihoods of presenting a set of content items to the collection of users, wherein a contextual bandit model is employed to compute the intended likelihoods. The online system applies the ranking computer model to generate, based on user data for a user of the online system and contextual data associated with a current session of the user, a ranking score for each content item in a set of content items. The online system selects, based on the ranking score for each content item, one or more content items from the set of content items. The online system causes a device associated with the user to display a user interface with the one or more content items for recommendation to the user.
An online concierge system predicts how available tasks will be for a particular assistant in the assistant's current context. Task availability is computed differently in different embodiments. In a first embodiment, the task availability assessment functionality predicts an expected gap between demand for task performance and supply of assistants to perform those tasks. This expected gap is compared to historical gap values in a market segment (e.g., a particular geographical region during a particular span of time) to make a rough assessment of task availability relative to the average of that market segment. In a second embodiment, a set of features relevant to nearby retailer locations, the current geographic location, and/or the particular assistant is input to a deep learning model, which accordingly predicts a specific amount of time until the assistant receives a first task assignment.
A ranking computer model is trained based on grouping a collection of users of an online system into different buckets based on intended likelihoods of presenting a set of content items to the collection of users, wherein a contextual bandit model is employed to compute the intended likelihoods. The online system applies the ranking computer model to generate, based on user data for a user of the online system and contextual data associated with a current session of the user, a ranking score for each content item in a set of content items. The online system selects, based on the ranking score for each content item, one or more content items from the set of content items. The online system causes a device associated with the user to display a user interface with the one or more content items for recommendation to the user.
The online system is configured to efficiently handle user requests by choosing a suitable prompt from a pre-curated library and selecting one of a plurality of large language models (LLMs) to respond to the user queries. These prompts are tailored for compatibility with different LLMs. When a user query is received, the system simultaneously forwards it to multiple LLMs and receives diverse responses. Performance metrics are then generated based on these multiple responses, aiding in the selection of the most suitable LLM. The chosen LLM is used for processing subsequent queries from the same user. This approach not only ensures that users receive high-quality, prompt responses but also optimizes the system's performance by dynamically selecting the most efficient LLM based on both quality and speed.
An online system receives information describing a physical retail store, in which the information includes attributes of physical elements within the store and their arrangement. A request is received from a user to generate a rendering of the store in a virtual reality environment. A profile of the user describing the user's geographic location and a set of historical actions performed by the user are accessed, in which the set of historical actions is associated with one or more of the physical elements. Based on the information describing the store and the profile, the rendering is generated to include virtual reality elements representing a set of the physical elements arranged based on the arrangement of the physical elements, and the rendering is sent for display to the user. When an update to the information describing the store is received, the rendering is updated and sent for display to the user.
A recipe prediction model is used to suggest replacement items by inferring intent of a user of an online system. Upon receiving a signal indicating that an item in a cart requested by the user is not available and responsive to identifying a failure of a replacement model to identify suitable replacement items according to defined criteria, the online system applies the recipe prediction model trained to infer a recipe that is potentially associated with the item and output a recipe's name and a replacement item category. The online system collects, based on the recipe's name, a set of recipes from a database. The online system identifies, from the set of recipes, based on the replacement item category, a set of candidate replacement items. A device associated with the user displays a user interface with one or more replacement items selected for recommendation to the user.
An online system may provide an instruction prompt to a machine-learned language model. The instruction prompt may include an instruction to generate an evaluation label of a training sample of a classification model and a textual format related to how data is arranged. The evaluation label may be used in a supervised training of the classification model. The online system may provide a batch of evaluation request prompts to the machine-learned language model. Each evaluation request prompt includes data that is at least partially arranged in the textual format described in the instruction prompt. The online system may receive a plurality of responses from the machine-learned language model. Each response includes the evaluation label corresponding to each evaluation request prompt. The online system may store at least evaluation labels and the data in the evaluation request prompts as training samples for the supervised training of the classification model.
Classifying results of a user's search query using a trained classification model. In response to the search query, an online system retrieves a set of candidate search results, each candidate search result associated with a respective item of a plurality of items. The online system accesses the classification model that is trained to compute a probability of classification of each item into each class of a plurality of classes, each class associated with a type of relevance to the search query. The online system applies the classification model to generate, for each item, a classification score associated with each class. The online system classifies, based on the classification score, each item into a corresponding type of relevance to the search query. The online system selects, based on the classification of each item, a list of items for displaying at a user interface of a device associated with the user.
An online concierge system suggests replacement items when an ordered item may be unavailable. To promote similarity of sources between the replacement item with the ordered item, candidate replacement items are scored, in part, based on a source similarity score based on a source of the candidate replacement item and a source of the ordered item. The source similarity score may be determined by a computer model based on user interactions with item sources. The similarity score may be based on source embeddings that may be determined based on respective item embeddings or may be determined by training source embeddings directly from user-source interactions. The similarity score for a candidate replacement item may be combined with a replacement score indicating the user's likelihood of selecting the candidate replacement item as a replacement to yield a total score for selection as suggestion as a replacement for the ordered item.
An online concierge system assigns shoppers to fulfill orders from users. To allocate shoppers, the online concierge system predicts future supply and demand for the shoppers' services for different time windows. To forecast a supply of shoppers, the online concierge system trains a machine learning model that estimates future supply based on access to a shopper mobile application through which the shoppers obtain new assignments by shoppers. The online concierge system also forecasts future orders. The online concierge system estimates a supply gap in a future time period by selecting a target time to accept for shoppers to accept orders and determining a corresponding ratio of number of shoppers and number of orders. The online concierge system may adjust a number of shoppers allocated to the future time period to achieve the determined ratio number of shoppers and number of orders.
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
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"
15.
PROVIDING AND DISPLAYING SEARCH RESULTS IN RESPONSE TO A QUERY
An online system displays search results in response to a query by receiving a query from a customer. An online system accesses a set of candidate items and computes a relevance score and personalization score for each item. The online system computes the relevance score based on query data and item data and may normalize the relevance score. The online system computes the personalization score based on item data, such as an item embedding, and user data, such as a user embedding. The online system computes a query specificity score and adjusts the personalization score with the query specificity score such that generic queries have high personalization scores and specific queries have low personalization scores. The online system combines the relevance and personalization scores for each candidate item into a ranking score and displays the candidate items to the customer based on their ranking scores.
An online shopping concierge platform receives data indicating one or more customer interactions associated with a particular item offered by the online shopping concierge platform; identifies a plurality of different and distinct images of the particular item; generates, based at least in part on multiple different and distinct machine learning (ML) models and for each image of the plurality of different and distinct images, a composite score for the image; selects, based at least in part on its respective composite score, an image of the particular item to be presented to the customer; generates data describing a graphical user interface (GUI) comprising a listing of the particular item including the selected image; and communicates to a computing device associated with the customer the data describing the GUI such that the computing device associated with the customer renders and displays the listing.
An online system displays an ordering interface, and responsive to receiving a request from a client device to place an order including one or more items to be collected from a retailer location, the system retrieves data associated with each item. The system accesses and applies a machine-learning model to predict a likelihood of each item being a predictable availability item having at least a threshold measure of fluctuating availability throughout the day at the retailer location based on data associated with a corresponding item. The system identifies a set of predictable availability items based on the predicted likelihood(s) and predicts an availability of each identified predictable availability item at the retailer location during a future timeframe. The system then updates the ordering interface to describe the predicted availability of each predictable availability item at the retailer location during the future timeframe.
An online system includes an interface which facilitates communication between customers and pickers who are servicing the user's order. The customer may request a modification to their order through the interface. The online system performs an inference task in conjunction with the model serving system or the interface system to continuously monitor conversations between users and pickers to infer whether a customer requested to modify their order to maintain an updated order and an updated in-store transaction estimate for the order. The online system determines if the order has been updated to account for the requested changes. If the order has not been updated, the online system automatically updates the customer's order and computes an updated in-store transaction estimate based on the changes made.
An online concierge system receives two types of orders, one of which requires fulfillment in a specific time interval, while the other can be fulfilled anytime up to a specific time interval. A machine learning model, trained on historical data about available shoppers in discrete time intervals, is used to predict how many shoppers will be available to fulfill orders in each time interval. For each time interval, the system retrieves the relevant orders of both types and creates candidate groups including orders of both types. For each group, the system determines a fulfillment cost based on items in the orders. The candidate group with the lowest cost is selected, and the orders in the selected group are sent to devices of available shoppers in that interval, prompting the shoppers to view and fulfill the orders.
Systems and methods for a contract-based offer generator is provided. The method includes receiving data describing an offer on a product at an offer generation system and extracting relevant details using a machine learning language model. A plurality of test offers stored in an offer bank and transaction logs associated with the product are accessed. Each test offer is scored using a reinforcement learning model trained on past transaction data to predict the likelihood of achieving an offer objective. The forecast score is adjusted based on differences between the extracted offer and test offers. A subset of test offers is selected to maximize orthogonality of product-related variables and transmitted to client devices. User responses to the test offers are collected and stored, capturing engagement and purchase behaviors. The reinforcement learning model is then retrained based on these responses, enabling continuous improvement in offer selection and effectiveness.
A system generates a set of embeddings for known treatments by applying a machine-learned embedding model to descriptions of the known treatments, where these embeddings form a vector space. The system generates an embedding for a new treatment and mapping it within the vector space, and identifies one or more known treatments with embeddings that exceed a similarity threshold with the new treatment embedding. The system accesses performance data for the selected known treatments to assess user response, and identifies a subset of users for the new treatment based on this performance data. The system also creates a content item that incorporates the new treatment, and transmits instructions to client devices of the targeted users to cause the client devices to display the content item.
An online system may receive a registration of an application for a language model gateway configured as an intermediary between users and a first machine-learned language model. The online system may monitor a conversation associated with the application using the language model gateway. The conversation is between a user of the application and the first machine-learned language model and includes a prompt from the user directed toward the first machine-learned language model. The online system may extract the prompt and compile an input for a second machine-learned language model that is fine-tuned to improve prompts. The input may be the prompt and one or more criteria to improve the prompt. The online system may provide the input to the second machine-learned language model. The online system may determine a suggested improvement to the prompt using the second machine-learned language model and provide the suggested improvement to the user.
An online system receives orders including items from customers and allocates the orders to pickers. A picker obtains items included in an order from a customer and delivers the items to the customer to fulfill the order. To provide encouragement for pickers fulfilling orders, the online system generates a highlight reel of accomplishments of a picker fulfilling orders. The online system generates prompts for a generative model, such as a large language model, based on stored information describing order fulfillment by a picker. Content, such as text, generated in response to a prompt by the generative model includes one or more portions of the stored information describing order fulfillment is included in the highlight reel. A template for the highlight reel is selected for the picker, with the template identifying content displayed to the picker and an order in which the content is displayed.
An online concierge system selects picker expertise tags that showcase abilities or experiences of pickers that fulfill orders for the system. The online concierge system establishes a set of user-order cohorts based on characteristics of orders and users placing the orders. When an order is received, the online concierge system identifies a relevant user-order cohort and applies a trained model to predict, in the context of the user-order cohort, the performance of various candidate picker expertise tags applicable to the order. The trained model may be generated via a training and testing process in which different picker expertise tags are tested in the context of a user-order cohort, and performance metrics are observed to learn which picker expertise tags perform best in the context of a user-order cohort.
An online concierge system may update, responsive to a request from a user client device, a shopping list with a food item. The system may query a recipe database based in part on the food item to obtain one or more recipes that use the food item as a key ingredient, where the key ingredients in a recipe are tagged in the recipe database. The key ingredients for each of the corresponding recipes are identified using a machine learned model. The system ranks the one or more recipes based on one or more ranking criteria, such as a number of key ingredients of the recipe that are present in the shopping list. The system may provide the one or more ranked recipes to the user client device for presentation.
An online system displays an ordering interface and, responsive to receiving a request from a client device associated with a user to place an order, retrieves information describing a set of unused credits provided to the user by each of one or more programs. The system identifies a set of the program(s), wherein the set of unused credits provided by each identified program is eligible to be used for acquiring an item in the order. The system accesses and applies a machine-learning model to predict an expiration of the set of unused credits provided to the user by each identified program based on the retrieved information and a current time. The system ranks the set of programs based on the prediction(s), determines a default allocation of a subset of each set of unused credits to the order based on the ranking, and updates the interface to include the default allocation.
An online concierge system detects acquired items included among an inventory of a customer and identifies one or more candidate available items from the acquired items based on a predicted perishability of each item and a predicted amount of each item that was used. The system retrieves recipes, matches the item(s) likely to be available to a set of recipes based on their ingredients, and identifies any remaining items for each matched recipe not likely to be available. The system retrieves a set of attributes associated with the customer and the set of recipes and computes a suggestion score for each recipe based on the attributes. The system ranks the recipes based on their scores, identifies one or more recipes for suggesting to the customer based on the ranking, and sends the recipe(s) and any remaining items for each recipe to a client device associated with the customer.
G06Q 10/087 - Gestion d’inventaires ou de stocks, p. ex. exécution des commandes, approvisionnement ou régularisation par rapport aux commandes
G06K 7/10 - 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
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 30/0202 - Prédictions ou prévisions du marché pour les activités commerciales
An online concierge system receives multiple images of an item from a first client device associated with a shopper associated with the online concierge system, in which each of the images of the item is captured from a different angle and/or position and the item is included among an inventory of a warehouse associated with a retailer associated with the online concierge system. Based in part on the images of the item, the online concierge system generates a three-dimensional image of the item, in which the three-dimensional image of the item includes a dimension of the item and/or a color of the item. The online concierge system then sends the three-dimensional image of the item to a second client device associated with a customer of the online concierge system, in which a perspective of the three-dimensional image is modifiable within a display area of the second client device.
An online system uses a machine-learned language model (e.g., an LLM) to improve multilingual search capabilities. The system generates a prompt for the LLM that includes a set of search queries in a first language along with their context, as well as a request for translating these queries into a second language. This prompt is sent to a model serving system, which executes it through the LLM and returns translated queries in the second language. Additionally, the concierge system accesses a first set of features derived from the search results in the first language, and updates these features based on the newly translated search queries to create a second set of features. These translated queries and the second set of features are then used to train a search model optimized for queries in the second language.
An online concierge system identifies churn of a customer, which occurs when the customer does not perform a specific action within a threshold time period. The online concierge system determines an event causing churn of the customer based on characteristics of the customer and attributes describing prior fulfillment of an order for the customer. To mitigate different events causing churn, the online concierge system maps areas of expertise of pickers for different aspects of order fulfillment to corresponding events. Through a trained picker scoring model, the online concierge system determines picker scores for different pickers fulfilling an order for a customer using characteristics of pickers, including an expertise, characteristics of the customer, and an event causing churn of the customer. Based on the picker scores, the online concierge system selects a specific picker for fulfilling a subsequent order from the customer.
A trained computer model to identify a list of representative previously purchased items for recommendation to a user of an online system. The online system clusters, based on a similarity score for each pair of items, a set of previously purchased items into multiple clusters. The online system accesses a computer model trained to predict a likelihood of engagement by the user for each item in each cluster, and applies the computer model to predict, based on one or more features of each item, the likelihood of engagement for each item in each cluster. The online system generates, based on the likelihood of engagement, a score for each item in each cluster. The online system selects, based on the score for each item, a representative item from each cluster. The online system causes a device associated with the user to display the representative item from each cluster.
A system generates item images using an item image generation model. The system receives a prompt for the model. The prompt is configured to request the model generate item images for an item. The system executes the model using the prompt to generate a set of item images. The system evaluates each of the set of item images to determine performance data of each of the set of item images. The system iteratively improves the set of item images by performing the following steps. The system updates the prompt based on the performance data of each of the set of item images to obtain a new prompt. The system executes, using the new prompt, the model to generate a new set of item images, and the system evaluates the new set of item images to determine performance data of each of the new set of item images.
G06F 16/56 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données d’images fixes en format vectoriel
G06F 16/953 - Requêtes, p. ex. en utilisant des moteurs de recherche du Web
33.
LANGUAGE MODEL DECODING FOR SEARCH QUERY COMPLETION
A language model is used to generate autosuggestions to complete or revise a user's partial search query. An initial partial query is applied to the language model to generate query candidates for completing the search query. The language model may generate the query candidates as additional or alternate tokens for the partial search query. When the user revises the partial query, the previously-generated candidates can be re-used to reduce subsequent processing time for generating additional candidates. The previously-generated candidates are compared with the revised partial query to select which of the candidates to be re-used and expanded for generating additional tokens. Additional tokens can be generated in parallel for the previously-generated candidates or with model values from the previous generation, enabling the tokens to be generated effectively with reduced latency consistent with user expectations for search-related autosuggestions.
An online system receives a user request from a client device through the interface, identifies one or more featured products based on the query, and generates a prompt for input to a machine-learned generative language model. The prompt specifies both the user's request and a request to suggest the featured products in association with a response to the user request. This prompt is fed into a machine-learned language model via a model serving system for execution. The online system receives a response generated by the model, generates a query response based on the response generated by the model, and transmits instructions to the client device to display the query response. The online system collects data on user interactions with the uses the collected data to fine-tune the machine-learned generative language model.
G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
A trained computer model for automatic identification of a wrong delivery location for an order placed at an online system. The online system receives, via a user interface, a user input that includes a delivery location for the order. The online system compares the received delivery location with a stored delivery location for the user. Responsive to identifying that the received and stored delivery locations are different, the online system accesses and applies a computer model to predict, based on features of the order, a likelihood of the received delivery location being correct. The online system generates, based on the predicted likelihood, a confidence score of the received delivery location being correct. Responsive to the confidence score being below a threshold score, the online system causes a device of the user to display a user interface with a message prompting the user to verify accuracy of the received delivery location.
An online system updates the labels on negative examples to account for the possibility that the example is a false negative. The system generates a set of initial training examples that each include a query input by the user and item data for an item presented as a result to the user's query. Each training example also includes an initial label, which represents whether the user interacted with the item presented as a search result. The online system updates the initial label for a negative training example by identifying a set of bridge queries and computing a similarity score between the query for the training example and the bridge queries. The online system computes an updated label for the negative example based on the similarity scores and updates the training example with the updated label.
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
37.
DATABASE SEARCH BASED ON MACHINE LEARNING BASED LANGUAGE MODELS
An online system receives information describing a set of items requested by a user and an indication via a chat interface that a particular item needs replacement. The online system generates one or more prompts configured to request a machine learned language model to identify the particular item that needs replacement and to identify one or more replacement items for the particular item. The online system receives a set of item identifiers from the machine learned language model and selects a replacement item from a database based on the set of item identifiers. The online system may also receive an order and a communication history associated with a user including a message with a request to modify the a. The online uses the machine-learning language model to map the request type to the set of API requests for updating the order to reflect the request from the user.
An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.
A warehouse from which shoppers fulfill orders for an online concierge system maintains an online concierge system-specific portion for which the online concierge system specifies placement of items in regions. To place items in the online concierge system-specific portion, the online concierge system accounts for co-occurrences of different items in orders and measures of similarity between different items. From the co-occurrences of items, the online concierge system generates an affinity graph. The online concierge system also generates a colocation graph based on distances between different regions in the online concierge system-specific portion. Using an optimization function with the affinity graph and the colocation graph, the online concierge system selects regions within the online concierge system-specific portion for different items to minimize an amount of time for shoppers to obtain items in the online concierge-system specific portion.
Administration of a customer loyalty program which provides discounts on products and services; Advertising, marketing, and promoting the goods and services of others via a global computer network, namely, advertising and promoting the availability of goods for selection, ordering, purchase, and delivery; Comparison shopping services; Providing advertising and advertisement services; On-line ordering services featuring consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; On-line wholesale and retail store services featuring a wide variety of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise featuring delivery to home, office, and other designated locations; Event planning and management for marketing, branding, promoting or advertising the goods and services of others
09 - Appareils et instruments scientifiques et électriques
Produits et services
(Based on 44(d) Priority Application)(Based on Intent to Use) Downloadable computer e-commerce software to allow users to perform electronic business transactions via a global computer network; Downloadable software in the nature of a mobile application for commerce, namely, software that allows users to perform electronic business transactions via a global computer network; Downloadable software in the nature of a mobile application for allowing users to execute shopping-related demands in the nature of building a shopping list, placing an order for groceries, food, snacks, beverages, and alcoholic beverages online, and selecting recipes according to groceries purchased; Downloadable software for browsing and purchasing consumer goods of others; Downloadable software for engaging and coordinating personal shopper and delivery services; Downloadable software for providing information on available same-day transportation and delivery services; Downloadable software for searching for and accessing, creating, publishing and browsing information in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Downloadable software for shopping in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Downloadable software for event planning (Based on Intent to Use) Downloadable software in the nature of a mobile application for splitting and reimbursing costs and expenses
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Personal shopping for others; Personal shopping in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise for others
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for enabling self-service electronic checkout and purchasing of goods by users; Providing a web site featuring technology that enables users to search, browse, and purchase a wide variety of consumer goods of others; Software as a service (SAAS) services featuring software for designing, developing, hosting, implementing and maintain web sites for others that enable, collect data with respect to, and process the selection, ordering, billing, delivering, and advertising of consumer goods and services, and for use in advising retailers regarding the use of such websites by others in the field of retail, ordering, and delivery services featuring consumer goods; Providing temporary use of on-line non-downloadable software for users to perform electronic business transactions via global computer network; Providing temporary use of on-line non-downloadable software for analyzing consumer behavioral data; Providing temporary use of on-line non-downloadable software for analyzing retail store customer data; Providing a website featuring non-downloadable software using artificial intelligence (AI) for enabling autonomous checkout; Providing temporary use of on-line non-downloadable software for use in acquiring shopping information and price comparisons that may be downloaded from a global computer network; Providing temporary use of on-line non-downloadable software for use in advertising products and brands via electronic displays throughout retail stores; Providing online non-downloadable computer software platforms for use in database management, sales, customer tracking and management, and inventory management for the e-commerce, wholesale and retail industries; Providing temporary use of on-line non-downloadable software for use in inventory tracking and data analytics in the field of retail stores; Providing temporary use of on-line non-downloadable software for providing recommendations of items for purchase based on shopper preferences; Providing temporary use of on-line non-downloadable software for providing dynamic pricing of items throughout a retail store; Providing temporary use of on-line non-downloadable software for browsing, comparing, and purchasing a wide variety of consumer goods of others; Providing temporary use of on-line non-downloadable software for facilitating, coordinating and scheduling delivery in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Providing temporary use of on-line non-downloadable software for engaging and coordinating personal shopper and delivery services; Providing temporary use of on-line non-downloadable software for ordering delivery services; Providing temporary use of on-line non-downloadable software for providing information on available same-day transportation and delivery services; Providing temporary use of on-line non-downloadable software for event planning; Providing temporary use of on-line non-downloadable software for splitting and reimbursing costs and expenses
Administration of a customer loyalty program which provides discounts on products and services; Advertising, marketing, and promoting the goods and services of others via a global computer network, namely, advertising and promoting the availability of goods for selection, ordering, purchase, and delivery; Comparison shopping services; Providing advertising and advertisement services; On-line ordering services featuring consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; On-line wholesale and retail store services featuring a wide variety of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise featuring delivery to home, office, and other designated locations; Event planning and management for marketing, branding, promoting or advertising the goods and services of others
09 - Appareils et instruments scientifiques et électriques
Produits et services
Downloadable computer e-commerce software to allow users to perform electronic business transactions via a global computer network; Downloadable software in the nature of a mobile application for commerce, namely, software that allows users to perform electronic business transactions via a global computer network; Downloadable software in the nature of a mobile application for allowing users to execute shopping-related demands in the nature of building a shopping list, placing an order for groceries, food, snacks, beverages, and alcoholic beverages online, and selecting recipes according to groceries purchased; Downloadable software for browsing and purchasing consumer goods of others; Downloadable software for engaging and coordinating personal shopper and delivery services; Downloadable software for providing information on available same-day transportation and delivery services; Downloadable software for searching for and accessing, creating, publishing and browsing information in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Downloadable software for shopping in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Downloadable software for event planning; Downloadable software in the nature of a mobile application for splitting and reimbursing costs and expenses
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Personal shopping for others; Personal shopping in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise for others
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(Based on 44(d) Priority Application)(Based on Intent to Use) Providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for enabling self-service electronic checkout and purchasing of goods by users; Providing a web site featuring technology that enables users to search, browse, and purchase a wide variety of consumer goods of others; Software as a service (SAAS) services featuring software for designing, developing, hosting, implementing and maintain web sites for others that enable, collect data with respect to, and process the selection, ordering, billing, delivering, and advertising of consumer goods and services, and for use in advising retailers regarding the use of such websites by others in the field of retail, ordering, and delivery services featuring consumer goods; Providing temporary use of on-line non-downloadable software for users to perform electronic business transactions via global computer network; Providing temporary use of on-line non-downloadable software for analyzing consumer behavioral data; Providing temporary use of on-line non-downloadable software for analyzing retail store customer data; Providing a website featuring non-downloadable software using artificial intelligence (AI) for enabling autonomous checkout; Providing temporary use of on-line non-downloadable software for use in acquiring shopping information and price comparisons that may be downloaded from a global computer network; Providing temporary use of on-line non-downloadable software for use in advertising products and brands via electronic displays throughout retail stores; Providing online non-downloadable computer software platforms for use in database management, sales, customer tracking and management, and inventory management for the e-commerce, wholesale and retail industries; Providing temporary use of on-line non-downloadable software for use in inventory tracking and data analytics in the field of retail stores; Providing temporary use of on-line non-downloadable software for providing recommendations of items for purchase based on shopper preferences; Providing temporary use of on-line non-downloadable software for providing dynamic pricing of items throughout a retail store; Providing temporary use of on-line non-downloadable software for browsing, comparing, and purchasing a wide variety of consumer goods of others; Providing temporary use of on-line non-downloadable software for facilitating, coordinating and scheduling delivery in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Providing temporary use of on-line non-downloadable software for engaging and coordinating personal shopper and delivery services; Providing temporary use of on-line non-downloadable software for ordering delivery services; Providing temporary use of on-line non-downloadable software for providing information on available same-day transportation and delivery services; Providing temporary use of on-line non-downloadable software for event planning (Based on Intent to Use) Providing temporary use of on-line non-downloadable software for splitting and reimbursing costs and expenses
An online system augments a dataset in conjunction with a model serving system. The online system accesses a dataset for training a machine-learning model. The online system generates a prompt to generate candidate samples in the training dataset to the model serving system. The online system receives a response comprising one or more candidate samples. The online system compares the one or more candidate samples to at least one existing sample of the dataset to determine whether the one or more candidate samples are within a threshold level of similarity to an existing sample. If a candidate sample received from the machine-learning language model is not within the threshold level of similarity to an existing sample, the online system updates the dataset with the candidate sample.
An online system accesses user behavior data and incentive data collected for a user prior to a current time period. The online system trains a behavior prediction model to receive user behavior data for a user and an incentive and output an incentive score using the collected user behavior data. The online system receives one or more candidate incentives generated by an incentive generation model based on the accessed user behavior data and incentive data. The online system applies each candidate incentive to the behavior prediction model to generate an incentive prediction describing a degree of user interaction of the particular user with the online system responsive to offering the candidate incentive to the user. The online system offers one or more candidate incentives to the user based on the determined incentive predictions.
An online system performs an atypical replacement recommendation task in conjunction with a model serving system or the interface system to make recommendations to a user for replacing a target item with an atypical replacement item. The online system receives a search query from a user and identifies a target item based on the search query. The online system identifies a set of candidate items for replacing the target item. The online system may select one or more atypical replacement items in the set of candidate items, and generate an explanation for each atypical replacement item. The explanation provides a reason for using the atypical replacement item to replace the target item. The online system provides the atypical replacement items and the corresponding explanations as a response to the search query.
A trained computer model for generating an aggregated health score for a business user of an online system. The online system obtains a set of health scores for a set of individual employees of a business user of an online system. The online system accesses a computer model of the online system trained to determine an aggregated health score for the business user. The online system applies the computer model to generate, based at least in part on the set of health scores and content of a set of orders placed by the business user, the aggregated health score for the business user. The online system causes a device of the business user to display a user interface with the aggregated health score.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
56.
AUTOMATIC QUALITY ASSESSMENT OF AN ITEM DURING ORDER FULFILLMENT
Use of a language model to automatically perform visual assessment of quality of an item being fulfilled by a picker. The online system receives an image of the item and identifies a set of potential problems associated with the item. The online system generates a plurality of prompts for input into the language model including the image and one or more questions each corresponding to a respective potential problem of the set potential problems. The online system requests the language model to generate, based on the plurality of prompts, a feedback response for each potential problem. The online system generates an aggregated output by aggregating the feedback response for each potential problem, and based on the aggregated output, a second message that identifies one or more relevant problems associated with the item. The online system causes a device of the picker to display the second message.
Different possible candidate routes for efficiently obtaining a set of items at given retailer premises are generated and simulated to estimate degrees of difficulty of the various routes, such as how long they are expected to take. The current conditions can be inferred based on analysis of environment data received from a plurality of devices associated with users shopping for items on the retailer premises, such as location data, camera data, or comments related to the retailer premises. The simulation takes into account current or expected conditions in the environment of the retailer premises, such as obstructions, alternative placements of items, etc. Routes with least degrees of difficulty may be presented to the users shopping for the items so that the users can use the most efficient routes when obtaining the items.
A system uses a contextual bandit model for query processing. The system receives, from a client device, a user query for identifying one or more items by the system and described by query feature(s). The system obtains contextual feature(s) describing the query's context. The system applies a query processing model to the user query to determine a relevance score for each query result. The system applies a contextual bandit model to the query features and the contextual features to determine a weight vector for ranking parameters. The ranking parameters include relevance of a query result to the user query and dependability of the query result. The system determines, for each query result, a ranking score based on the weight vector and ranking parameter values of the query result. The system transmits the query results ranked according to the ranking scores for display on the client device.
A system uses a contextual bandit model for query processing. The system receives, from a client device, a user query for identifying one or more items by the system. The user query is described by one or more query features. The system obtains one or more contextual features describing a context of the user query. The system applies a contextual bandit model to the query features and the contextual features to select a query processing model from a plurality of query processing models. The system applies the selected query processing model to the user query to obtain query results. The system transmits the query results for display on the client device.
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.
A concierge system identifies retail locations within a distance of a picker client device of a picker. This distance defines a zone and the system provides a map of the zone for display within a picker client application. For each retail location in the zone, the system determines a batch volume for the retail location and an average batch volume for the zone and generates a batch availability score using a model trained on batch volumes for the retail location and batch volume for the zone. The batch availability score can be a value reflecting batch availability or busyness of the retail location relative to other retail locations or can be a wait time prediction in minutes until the picker receives a batch at the retail location. The system modifies how the retail locations are displayed on the map to emphasize those with batch availability scores above a threshold value.
A system may access a first set of images captured by cameras coupled to a shopping cart, wherein each image depicts a user associated with the shopping cart. A system may apply a pose detection model to each of the images to predict a user's pose. A system may apply an action prediction model to the set of images and the predicted poses to predict whether the user performed an action to change the contents of a storage area of the shopping cart. A system may, responsive to predicting that the user performed a change action, apply an item identification model to a second set of images of a storage area of the shopping cart to identify an item associated with the change action. A system may update an item list of the user based on the change action and the identified item.
A system may smartly edit the context of a conversation to be input into a chatbot LLM by using a conversation compression algorithm to prune and compress redundant elements. The system evaluates the conversation context compression algorithm using both a chatbot LLM and an adversarial LLM. The system retrieves a logged conversation and generates a compressed conversation context from the logged conversation. The system generates a synthetic user response by applying the adversarial LLM and generates a test conversation by replacing a user response in the conversation with the synthetic user response. The system generates a compressed context of the test conversation. The system generates a test chatbot LLM response by prompting the chatbot LLM with the compressed context of the test conversation. The system evaluates the conversation context compression algorithm by comparing the test chatbot response with a benchmark chatbot response.
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
64.
GENERATING INFORMATION INTEGRITY INSTRUCTIONS USING A GENERATIVE MODEL
An online system stores data obtained from various users of the online system. For example, the online system maintains databases for various users, with a database including data received from the user. As users provide data to the online system for storage, the online system applies data integrity checks received from users that verifies received data satisfies one or more criteria. To facilitate creation and execution of data integrity checks, the online system tunes a large language model (LLM) using executable instructions for previously generated data integrity checks and metadata describing execution of the previously generated data integrity checks. After tuning, the online system obtains one or more parameters that are input as prompts to the LLM to generate executable instructions for performing a data integrity check using the parameters.
An online concierge system receives a free-text query describing items and constraints from a client device associated with a user. The system generates a prompt including the query and a request to identify the items and constraints. The system provides the prompt to a large language model, extracts, from an output of the model, the constraints and one or more categories associated with the items, and identifies retailers based on user data associated with the user. For each retailer, the system identifies a set of items associated with each category, determines, based on the constraints, a combination of a subset of items associated with each category, and computes a score for the combination based on the user data and item data associated with items in the combination. The system ranks the combinations based on the scores and sends information describing a ranked set of the combinations to the client device.
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 system receives an order containing a list of items from a user's client device and tracks the current locations of a client device of a shopper within a warehouse. The system applies a trained item sequence model to generate a suggested picking sequence, minimizing time required for the shopper to pick the items. The item sequence model is trained using historical order data, including durations between picking items from different aisles and pairwise distances between aisle locations. The system transmits the suggested picking sequence to the shopper's client device for display. Responsive to determining that the client device of the shopper's location deviates from the suggested sequence, the system dynamically updates the sequence by applying the model to remain items and the shopper's current location.
An online system generates text-based representations of various types of data for processing using a large language model. The online system extracts location data from a map of a source location and converts the location data into a text-based representation of the location data. The online system receives a set of item identifiers from a client device of a user and generates an LLM prompt based on the set of item identifiers and the text-based representations of the location data. The online system receives a response from the LLM and parses the response for a text-based description of related items. The online system maps the text-based description of the related items to item identifiers and transmits a notification to the client device that includes item data associated with the related items.
An online system presents a sponsored content page to a user in conjunction with a model serving system. The online system accesses a content page for a food item and identifies one or more sponsorship opportunities at the content page. The online system identifies one or more candidate sponsors for each sponsorship opportunity. The online system selects a bidding sponsor for the sponsorship opportunity from the one or more candidate sponsors and a candidate item associated with the bidding sponsor as a sponsored item. The online system provides a content page, a description of the sponsored item, and a request to generate a sponsored content page for the sponsorship opportunity to a model serving system. The online system receives a sponsored content page generated by a machine-learning language model at the model serving system and presents the sponsored content page to a user.
An online system receives orders from users and dispatches pickers to fulfill the orders by obtaining ordered items at a retailer. If an ordered item cannot be found by a picker, the picker may refund the item or attempt to find a replacement item. While obtaining a replacement item may increase revenue to the online system, it can also cause a bad outcome for user experience (e.g., an unacceptable replacement item, a refund request of the replacement item, etc.). To balance these interests, the online system trains a model to predict an outcome metric comprising a likelihood of a bad outcome from replacing an item or an expected amount of profit to the online system from a replacement item. The online system compares the outcome metric to a threshold to determine whether to promote or dissuade the picker from replacing a not-found item.
A system performs incremental updates to an event data store based on hydration and stateful validation of events to allow efficient generation of reports based on event data. The system stores data describing events received in a raw event data store. An event represents user interactions associated with a content item. Each event is classified as a thin event, or a thick event based on the type of user interaction associated with the event. The system monitors changes to the raw event data store, for example, using a listener process. The system stores event data in a hydrated event data store that includes records, each record storing information describing thin events associated with a thick event along with additional attributes describing each thin event.
Embodiments relate to automatic determination of an alternative item for an item of original set of items included into an order of a user of an online system. The online system accesses a first computer model trained to identify a set of candidate replacement items for the item and selects a subset of the candidate replacement items from the identified set based on a constraint that each candidate replacement item in the subset has a smaller monetary value than the item. The online system accesses a second computer model trained to select a candidate replacement item from the subset based on a predicted likelihood of conversion by the user for each candidate replacement item. The online system causes a device of the user to display a user interface with the selected candidate replacement item for inclusion into the order instead of the item from the original set of items.
An online system automatically generates a personalized collection of items around a theme. The online system generates a prompt for input into a language model, the prompt including information about a plurality of items and a text describing the theme around which the collection of items will be built. The online system requests the language model to generate, based on the prompt, a list of products eligible for building the collection of items. The online system accesses a computer model trained to identify a set of items personalized for a user of the online system. The computer model identifies, based on the list of eligible products and information about the user, the set of items for populating the collection of items. The online system causes a device of the user to display a user interface with the collection of items for inclusion into a cart of the user.
An online system receives user reviews for items and generates a review embedding for each review. The system receives a request for information describing an item from a client device associated with a user. Responsive to the request, the system identifies the item and contextual information associated with a current session of the user, and generates a user embedding based on the contextual information. The system compares the user embedding to a set of review embeddings for the item, identifies a set of reviews for the item based on the comparison, and generates a prompt including the identified set of reviews and a request to summarize, for the user, the identified set of reviews. The system provides the prompt to a large language model to obtain a summarized review for the item and sends a user interface including the item and summarized review for display to the client device.
A system may access a first set of images captured by cameras coupled to a shopping cart, wherein each image depicts a user associated with the shopping cart. A system mayapply a pose detection model to each of the images to predict a user's pose. A system may apply an action prediction model to the set of images and the predicted poses to predict whether the user performed an action to change the contents of a storage area of theshopping cart. A system may, responsive to predicting that the user performed a change action, apply an item identification model to a second set of images of a storage area of the shopping cart to identify an item associated with the change action. A system may update an item list of the user based on the change action and the identified item.
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
G06Q 20/18 - Architectures de paiement impliquant des terminaux en libre-service, des distributeurs automatiques, des bornes ou des terminaux multimédia
76.
WEAKLY SUPERVISED EXTRACTION OF ATTRIBUTES FROM UNSTRUCTURED DATA TO GENERATE TRAINING DATA FOR MACHINE LEARNING MODELS
An online concierge system receives unstructured data describing items offered for purchase by various warehouses. To generate attributes for products from the unstructured data, the online concierge system extracts candidate values for attributes from the unstructured data through natural language processing. One or more users associate a subset candidate values with corresponding attributes, and the online concierge system clusters the remaining candidate values with the candidate values of the subset associated with attributes. One or more users provide input on the accuracy of the generated clusters. The candidate values are applied as labels to items by the online concierge system, which uses the labeled items as training data for an attribute extraction model to predict values for one or more attributes from unstructured data about an item.
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
An online concierge system identifies a set of attributes of one or more future time periods and accesses a machine learning model trained to predict a set of working hours for a picker during a future time period, in which the set of working hours describes an availability of the picker to service orders placed with the online concierge system. The online concierge system then applies the machine learning model to the set of attributes to predict the set of working hours for the picker during the future time periods and stores the predicted set of working hours for the picker during the future time periods.
An online system receives user data for users of the online system and assigns the users to one or more user cohorts based on the user data. The online system generates a prompt for content to be included in a landing page presented to each user cohort, in which the prompt includes a template for the landing page and information describing the user cohorts. The online system then provides the prompt to a generative artificial intelligence model to obtain an output and extracts, from the output, a set of content to be included in the landing page for each user cohort. The online system generates variants of the landing page for each user cohort based on the extracted set of content.
An online concierge system uses images captured for fulfillment of a first order to affect item information of a second order. When a picker fulfills the first order in a physical warehouse, the picker captures an image of the physical warehouse, for example to capture an image of potential replacement items. The online concierge system detects items in the image along with a location of the item in the physical warehouse based on the image. The detected items and respective locations may then be used to modify a second order, for example to route a picker for the second order to updated or alternate locations of the detected items.
An online system manages various internal services using network resources or computing resources. Managing the internal services involves generating executable instructions for provisioning new services or for changing or monitoring existing services. To generate executable instructions for allocating or for monitoring network resources, the online system maintains a database of previously generated executable instructions for provisioning resources along with information about various previously generated instructions, such as comments on the executable instructions or past performance information for the previously generated instructions. To generate instructions for a new internal service, the online system tunes a large language model (LLM) with the database and provides prompts to LLM to generate executable instructions for the internal service based the prompts.
An online system hosts a retailer storefront user interface for a third-party retailer that includes content associated with items offered by the retailer for procurement and delivery through the online system. A retailer may provide preferences for where different candidate content is placed in the retailer storefront user interface. The online system applies a machine learning model to the retailer preferences and other contextual information relating to a particular presentation of the retailer storefront user interface to dynamically rank content for different possible placement positions. The ranking scores may relate to predicted performance metrics associated with operations of the online system. Content is then placed in the retailer storefront user interface based on the respective ranking scores.
A method for optimizing delivery assignments in an online system. The system processes delivery orders from user devices, associates the orders with available delivery agents, and allocates them based on real-time data such as inventory availability at different warehouses, delivery agent locations, and order preparation progress. The system dynamically updates order allocations by periodically reallocating orders to different delivery agents based on travel progress, order preparation progress, warehouse proximity, and inventory availability at various warehouses.
B65G 1/137 - Dispositifs d'emmagasinage mécaniques avec des aménagements ou des moyens de commande automatique pour choisir les objets qui doivent être enlevés
G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire
G05D 1/644 - Optimisation des paramètres de parcours, p. ex. consommation d’énergie, réduction du temps de parcours ou de la distance
G05D 1/69 - Commande coordonnée de la position ou du cap de plusieurs véhicules
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
One or more trained computer models are used to determine, at different stages of an order, an estimated time range for delivery of the order at an online system. The online system retrieves a set of candidate ranges of delivery times for the order. The online system applies the one or more computer models trained to predict a value of a metric for each candidate range in the set of candidate ranges, based on one or more features associated with the order. The online system selects a range of delivery times for the order from the set of candidate ranges, based on the predicted value of the metric for each candidate range. The online system causes a device of the user to display a user interface with the selected range of delivery times for the order.
A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G01G 19/12 - Appareils ou méthodes de pesée adaptés à des fins particulières non prévues dans les groupes pour incorporation dans des véhicules ayant des dispositifs électriques sensibles au poids
G01G 19/40 - 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
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/778 - Apprentissage de profils actif, p. ex. apprentissage en ligne des caractéristiques d’images ou de vidéos
G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
An online system obtains a target food from an order for a user and alcohol preferences from an order purchase history. The online system generates a prompt for a machine learning model to request alcohol candidates based on the target food category. The prompt includes the alcohol preferences, and requests for each alcohol candidate, a pairing score indicating how well the target food category pairs with the alcohol candidate and a user preference score indicating how well the alcohol candidate aligns with the alcohol preferences. The online system receives as output the candidate alcohol items. Each alcohol candidate has the pairing score, the user preference score, and a textual reason for scores. The online system matches at least one alcohol item from a catalog with each alcohol candidate. A subset of alcohol items is presented to the user as a carousel.
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.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
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
G06Q 20/18 - Architectures de paiement impliquant des terminaux en libre-service, des distributeurs automatiques, des bornes ou des terminaux multimédia
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 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.
A barcode decoding system decodes item identifiers from images of barcodes. The barcode decoding system receives an image of a barcode and rotates the image to a pre-determined orientation. The barcode decoding system also may segment the barcode image to emphasize the portions of the image that correspond to the barcode. The barcode decoding system generates a binary sequence representation of the item identifier encoded in the barcode by applying a barcode classifier model to the barcode image, and decodes the item identifier from the barcode based on the binary sequence representation.
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
METHOD, COMPUTER PROGRAM PRODUCT, AND SYSTEM FOR ACCOUNTING FOR VARIABLE DIMENSIONS OF CONTENT ITEMS WHEN POSITIONING CONTENT ITEMS IN A USER INTERFACE HAVING SLOTS FOR DISPLAYING CONTENT ITEMS
An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.
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.
An online system may receive, from a user device, a request to view, at a graphical user interface, available entries of a third-party system. The third-party system may operate multiple physical locations. The operation of each physical location is documented by a time-sensitive dataset which includes multiple dynamic item entries. The online system may retrieve a geographical location associated with the user device and determine a subset of physical locations operated by the third-party system that are eligible for further selection based on distances of the physical locations from the geographical location associated with the user device. The online system may determine a metric measuring a size of the dynamic item entries available in the time-sensitive dataset and select one of the physical locations based on the metric. The online system may cause for display the dynamic item entries in the time-sensitive dataset associated with the selected physical location.
Business administration and management, namely, administration and management of benefit plans for a food prescription and nutrition incentive program; business management, namely, management of a food prescription and nutrition incentive program for nonprofits, insurers, employers, and benefit providers to promote healthy eating through grocery stipends; order fulfillment services in the field of benefit-funded groceries; customer services management for others, namely, providing customer support and assistance services related to the management and use of grocery stipends
An online concierge system assists users in identifying additional information about items in an image. Image regions are identified in the image that may correspond to unknown items and an item search space is determined for detecting items in the image regions based on a context of the image, such as items in a warehouse or a list of items delivered to a customer. The identified items are used to retrieve relevant item information that is included in a prompt for a language model to extract relevant information for the item. As such, the process may automatically process the image into relevant textual information about the pictured items. Applications may be used to assist vision-impaired users in distinguishing delivered items or quickly identifying and evaluating relevant information about items.
Embodiments relate to utilizing a language model to automatically generate a novel recipe with refined content, which can be offered to a user of an online system. The online system generates a first prompt for input into a large language model (LLM), the first prompt including a plurality of task requests for generating initial content of a recipe. The online system requests the LLM to generate, based on the first prompt input into the LLM, the initial content of the recipe. The online system generates a second prompt for input into the LLM, the second prompt including the initial content of the recipe and contextual information about the recipe. The online system requests the LLM to generate, based on the second prompt input into the LLM, refined content of the recipe. The online system stores the recipe with the refined content in a database of the online system.
An online system provides a support application including a chatbot application. One or more tools may each be configured to access external data. The interface system hosts an agent powered by an underlying large language model. The online system receives a user query via the chatbot application. For at least one or more iterations, the online system performs steps to provide a prompt to the LLM that specifies at least the user query, contextual information, a list of available tools, or a request to output an action. The system parses the response from the LLM to extract a selected action and action inputs for the selected action. The system triggers execution of a respective tool that corresponds to the selected action with the action inputs. The system generates a response to the user query and transmits the response to the client device.
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
G06F 16/31 - IndexationStructures de données à cet effetStructures de stockage
An online system may prompt a shopper to capture one or more images of items on a checkout belt of a retailer, wherein the items are for fulfilling orders for one or more users of an online service. An online system may provide the one or more images to a machine learning model configured to classify an item as a product. An online system may classify the items to one or more products by applying the machine learning model to the images. An online system may for each user, matching the classified products to the user's order. An online system may obtain an annotated image of the items highlighting classified products which do not match the user's order. An online system may provide to the shopper the annotated image with a notification of a potential discrepancy.
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 10/74 - 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”
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
A computer system allowing users to search for items of interest provides a search query interface. The system receives characters of a search query in the search interface as the user enters the characters and interactively calculates, ranks, and displays a set of possible search query options from which the user can select. To rank the set of possible search query options, the system modifies rankings of candidate search queries based on factors associated with third parties. More specifically, contextual relevance scores are computed for the candidate search queries based on the context, such as a user to whom the search results are provided. These contextual relevance scores are in turn adjusted using factors associated with third parties, such as values calculated based on consideration offered by third parties. Users are shown the search query options, ranked in order of the adjusted relevance scores, as possible query selections.
A system, such as an online system, allows users to ask natural language questions requesting information stored in a database. The system receives a natural language question. The system determines database tables and database queries associated with the natural language question. The system generates a prompt for input to a machine learned language model. The prompt specifies the natural language question, information describing database tables, and the example database queries. The system sends the prompt to the machine learned language model for execution and receives a response generated by the machine learned language model. The response includes a database query corresponding to the natural language question. The system sends the database query for execution on a database system and provides the result of execution of the database query to the client device.
An online system detects an anomaly associated with an item selection made by a picker for fulfilling an order of a user of an online system. The system generates a prompt for execution by a machine-learned model trained as a large language model. The prompt comprises a chat log between the picker and the user. The system provides the prompt to the machine-learned model for execution. The system receives, as output from the machine-learned model and based on the chat log, a description indicating whether the anomaly is attributable to the user. The system determines, based on the output from the machine-learned model, that the item selection is not attributable to the user. Responsive to determining that the item selection is not attributable to the user, the system provides a notification to a client device of the user to confirm whether the item selection is approved by the user.
39 - Services de transport, emballage et entreposage; organisation de voyages
Produits et services
(1) Online retail store services featuring a wide variety of consumer goods of others; online consumer goods, grocery, food, pharmacy, home goods, pet supply, electronics, clothing, beauty, media, office supply, and general merchandise retail and wholesale store services featuring home, office, and other designated location delivery services; advertising and promoting the goods and services of others via a global computer network, namely, advertising and promoting the availability of goods for selection, ordering, purchase, and delivery; comparison shopping services; promoting the goods and services of others, namely, providing special offers and online catalogs featuring a wide variety of consumer goods of others; online ordering services featuring consumer goods, groceries, foods, pharmacy products, home goods, pet supplies, electronics, clothing, beauty products, media, office supplies, other supermarket products, and general merchandise; promoting the goods and services of others, namely, distributing online information, recipes, advertisements, articles, and media featuring the consumer goods of others; providing others with advertising services, namely, dissemination for others of marketing and advertising tailored to individual customers of retail stores; order fulfillment services; inventory management and control; inventorying merchandise; order fulfillment services incorporating robotics and automation; all of the foregoing excluding photo and video sharing and social networking services
(2) Transport and delivery of consumer goods; consumer goods, grocery, food, pharmacy, home goods, pet supply, electronics, clothing, beauty, media, office supply, and general merchandise delivery services; warehousing services, namely, storage, distribution, pick-up, packing, and shipping of a wide variety of consumer goods
