An online system uses a trained machine-learning model to predict timing of a state change of a variable state item in an order. The online system applies a trained machine-learning model to information about the variable state item and information about an ambient condition when servicing the order to predict a timing when a state of the variable state item changes from an original state at a location of a source associated with the online system to a different state. Based on the predicted timing, the online system generates a control signal that initiates at least one of a first action associated with the order or a second action associated with the variable state item. The online system performs, using the control signal, at least one of the first action associated with the order or the second action associated with the variable state item.
A shopping cart includes sensors configured to collect data about a physical interaction of a user with a product in a retail store. A set of features, such as product quality score, interaction duration, sequence of product interaction, and/or whether the product was added to the cart, are extracted from the data. These features are fed into a machine learning model to determine the user's quality preference score, indicating a likelihood that the user would be dissatisfied with the quality of the product. If a user orders online and their quality preference score surpasses a threshold, a notification is sent to the picker fulfilling the order. Furthermore, the user may send in satisfaction feedback via a client device of the user. Such feedback may subsequently be used to retrain the machine learning model.
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
3.
Display panel of a programmed computer system with a graphical user interface
An online system leverages a large language model (LLM) to generate a meta-catalog using online catalogs of items that are associated with sources. Items from the online catalogs are clustered. The clustering is based in part on similarity of the items, and each cluster is associated with a different meta-product. The LLM is prompted, based on descriptions of the items, to generate descriptions for meta-products that are associated with the clusters. Entries for the meta-products are generated using the generated descriptions. The meta-catalog for the meta-products is generated using the entries. The meta-catalog is provided to a third party system. A user may interact with the third party system via a user client device to select a meta-product of the meta-catalog for purchase, and the user client device is redirected to the online system to select an item corresponding to the meta-product and complete an order for the item.
An online system enables users to generate an order for items by receiving a collection of components, such as a recipe. The online system maps the components to specific items available at a source. To avoid nonsensical mappings of a specific item to a component, the online system trains a model to predict a probability of a specific item being suitable for inclusion in at least one collection of components. For example, the model generates a probability of a specific item being included in at least one recipe comprising a plurality of components. The model may be trained using users' inclusion of specific items previously selected for one or more groups of items based on collections of components by users of the online system.
An online system receives, at a search interface, a search term from a user. The system retrieves from a mapping table grouping parameters associated with the search term in the mapping table. The association between the grouping parameters and the search term in the mapping table is generated by generating a prompt including the search term and a set of physical objects that match the search term. The prompt requests grouping parameters for the set of physical objects, wherein each grouping parameter specifies a characteristic of the set of physical objects for providing search results responsive to the search term. The system receives, from the LLM, the grouping parameters and updates the mapping table. The system retrieves search results by querying a database of the online system using the search term. The system generates for display a user interface that groups the search results by the retrieved grouping parameters.
A computer-implemented method for suggesting keywords as a search term of a content item includes receiving, from a content provider, information about the content item in a database of content items. The method further includes generating a set of seed keywords related to the content item, and expanding the set of seed keywords to a plurality of candidate keywords. The plurality of candidate keywords are then scored based, at least in part, on an engagement metric measuring a user engagement with the content item in response to being presented with results from a search query comprising the candidate keyword. A candidate keyword is then selected from the plurality of candidate keywords based on the scoring, and stored relationally to the content item to define an audience for a recommendation about the content item, providing a suggestion to the content provider.
A device interfaced with an online system detects, via a physical sensor, item removal and generates a user interface with an alternative option for conversion. Upon receiving a signal from the device indicating the item removal, the online system selects a set of candidate items for replacement of the removed item, wherein each candidate item has a conversion value that is less than a conversion value of the removed item. The online system applies a trained machine-learning model to generate a conversion score for each candidate item that indicates a likelihood of conversion by the user of each candidate item. The online system selects, based on the conversion score for each candidate item, a replacement item from the set of candidate items, and generates a user interface signal that causes a user interface of the device to prompt the user to convert the replacement item.
An online system performs batch matching with synchronization between a broadcast signal and a boost signal. At a first timestep, the system notifies a first set of candidate pickers of a batch. If no picker selects the batch, the system identifies a catalyst action to facilitate matching. The system applies a decision model to determine whether to increase a broadcast signal or to increase a boost signal. If increasing the broadcast signal, the system identifies additional candidate pickers to notify of the batch. If increasing the boost signal, the system transmits the boost signal to the pickers for notification. The system may iteratively assess whether a candidate picker has selected the batch. If not, then the system may identify and perform additional catalyst actions to facilitate the matching of the batch. Eventually, the system receives a selection by one of the candidate picking users for fulfillment.
Leveraging a large language model for alternative ingredient determination is described. An online system receives, from a user device, an instruction to determine an alternative ingredient. An alternative ingredient is different from an ingredient of a recipe but has a common purpose with the ingredient in a context of the recipe. A large language model is prompted, based in part on the instruction, to determine one or more alternative ingredients for the ingredient of the recipe. An output of the large language model includes the one or more alternative ingredients. The output is processed, and at least some of the processed output is provided to the user device, and the user device presents at least one of the one or more alternative ingredients to the ingredient.
An online system that displays items from an item catalog to users supplements content displayed for one or more of the items with information extracted from images of the items. For a particular item in the item catalog, the online system performs image processing, such as optical character recognition, on one or more images of the item to extract text phrases from the images. For each extracted text phrase, the system then uses a trained model to score the text phrase as being a viable informational message. If the score for a text phrase is above a threshold, the online system augments content displayed in a user interface for the item with the text phrase. The online system may decide whether to supplement content for the item with an extracted text phrase based on the output of a predictive model.
An online system performs item redesign and engagement prediction. The system obtains item data describing characteristics of a target item for redesign. The system generates a prompt including the characteristics and directions to redesign at least one of them. The system executes the prompt on a generative model to output redesigns. Each redesign includes a modification to at least one characteristic of the target item. The system inputs features of variants, each variant include one store location and one redesign, into an engagement prediction model to output an engagement score for the variant. The engagement prediction model is trained on historical data describing levels of user engagement with items in association with the many store locations. The system identifies candidate variants based on the user engagement scores for further testing. The system transmits the candidate variants to a testing system to assess viability of the redesign.
An online concierge system applies a predictive model to predict demand of items, and facilitates preemptive picking of items in advance of receiving orders to enable efficient procurement and delivery. The online concierge system may apply a time-series model and/or machine learning model that predicts demand based on historical data. Depending on the predicted demand, items may be preemptively moved from a storage location to a staging area that enables the items to be more rapidly processed and delivered to customers when orders come in.
An online system may receive, from a content provider, a content presentation campaign that includes one or more objectives. The online system may define a set of one or more policy functions that automatically controls the content presentation campaign. A policy function may control one or more criteria in bidding content slots. The online system may monitor a realized outcome of the content presentation campaign. The online system may apply a reinforcement learning algorithm in adjusting the set of policy functions. The reinforcement learning algorithm adjusts one or more parameters in the set of policy functions to reduce a difference between the realized outcome and the desired outcome set by the content provider. The online system generates an adjusted set of policy functions and uses the adjusted set of policy functions in bidding content slots to present one or more content items provided by the content provider.
To evaluate the causal generalizability of multivariate regression models (such as marketing mix models) that evaluate a plurality of input features that may have high correlation and confounding causality, a model architecture is evaluated with respect to experimental data that varies feature values. The model architecture is trained with training data that excludes the experimental data. The trained model is then applied to predict the outcome of the experimental data inputs and the predicted outcome is scored with respect to the experimental outcome. This may be repeated across more than one experiment to evaluate how the model architecture generalizes to different types of variations in different experiments. The scores may then be used to validate the causal predictions and select or confirm a model architecture for use.
An online system receives information captured by a gaze tracking device describing a gaze point of a user and video data captured within a source location, detects a location associated with a first item that matches the gaze point based on the received information, and determines the first item is not available at the source location based on the video data. The system receives a signal indicating the user collected a second item from the source location, determines the second item is a replacement for the first item, and generates a new training example indicating the second item is an acceptable replacement for the first item for the user.
An online system receives information captured by a gaze tracking device describing a gaze point of a user and video data captured within a source location, detects a location associated with a first item that matches the gaze point based on the received information, and determines the first item is not available at the source location based on the video data. The system receives a signal indicating the user collected a second item from the source location, determines the second item is a replacement for the first item, and generates a new training example indicating the second item is an acceptable replacement for the first item for the user.
The system trains a machine-learning model to generate a score indicating whether a candidate item is an acceptable replacement for a target item for a user, in which the model is trained using training data that includes the new training example.
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”
An online system is configured to manage messaging between artificial intelligence (AI) agents. A service request (such as a request to order items) is received at an online system from a user client device. A system AI agent and a user AI agent are instantiated with inputs that include a set of objectives or constraints that guides each of the system AI agent and the user AI agent during messaging with the other. The online system manages rounds of messaging between the system AI agent and the user AI agent, and at some point, a proposed agreement between the user and online system is extracted from the messaging. The proposed agreement may then be presented to the user or online system for approval.
An online system generates subsequent orders for users following failed attempts to purchase items. The online system receives a request to fulfill an order from a user device. The online system determines that an item from the order is unable to be fulfilled and generates a failed fulfillment signal for the item associated with the user. At a later time, the online system automatically generates a set of items for a subsequent order for the user, the set of items including at least one item substantially similar to the item that was unable to be fulfilled and predicted by a machine-learned model to be available. The online system transmits a notification to the user that the set of items is available for fulfillment.
An online system selects content for placement in positions of a display on a user device. The online system selects a first set of content items according to a first content selection process and a second set of content items according to a second content selection process. To combine the different sets of content items dynamically, the first set of content items and second set of content items are evaluated by a joint impression scoring that includes factors prioritizing user, intrinsic, and other values. The respective contribution by the different factors may be adjusted by one or more adjustable weights, enabling different situations to effect different combinations of content items from the different content selection processes.
An online system receives orders from users and fulfills the orders by dispatching a picker to a physical source to obtain the items for delivery. Some items in an order may be considered “foundational,” meaning that a user who ordered the items may wish to cancel one or more other items in the order if the foundational item is unavailable (e.g., the item is a critical ingredient for a recipe). The online system predicts items in the order that are foundational using a trained machine-learning model. The online system presents the items to the picker in a sequence so the foundational items are obtained earlier by the picker. This enables the picker to observe whether the determined foundational item is available sooner in the picking process, allowing earlier performance of a remedial action and possibly avoiding replacing previously obtained items affected by the unavailability of the foundational item.
A trained machine-learning model is used to generate a user interface with filters personalized for a user of a computer system. Responsive to a search query, a computer system generates, based on user's features, a set of candidate filter combinations, each candidate filter combination having combined functionalities of a plurality of filters from a maintained collection of filters. The computer system applies the machine-learning model to generate a score for each candidate filter combination that is indicative of a likelihood of user's engagement with the plurality of filters or a likelihood of user's conversion on an item given a user's selection of the plurality of filters. The computer system selects, using the score for each candidate filter combination, a set of filter combinations. The computer system causes the user interface to display a set of user interface elements associated with the set of filter combinations along with search results.
An online system computes an incremental cost prediction for each of a set of user-treatment pairs to select a set of treatments to apply to users to satisfy a predicted interaction gap. The online system generates a set of candidate user-treatment pairs that each include user data for a user of the online system and treatment data for a treatment of a set of treatments. The online system computes an incremental interaction prediction and a treatment cost prediction for each of the candidate user-treatment pairs by applying an incremental interaction model to the user data and the treatment data in each user-treatment pair. The online system computes incremental cost predictions for each of the user-treatment pairs based on the computed incremental interaction predictions and treatment cost predictions and selects which users to apply treatments to and which treatments to apply to those users based on the incremental cost predictions.
An online system applies a visual language model and an optical character recognition model to a received image to generate descriptive information about unknown items in the image. The online system prompts a generative model with the descriptive information about unknown items in the image to separate the descriptive information into different bins each corresponding to a different unknown item in the image. For each unknown item detected in the image, the online system generates a target embedding from its descriptive information and performs a nearest neighbor search on an item catalog including embeddings for various items to find a set of candidate embeddings matching the target embedding. The online system retrieves item attributes of candidate items each corresponding to a candidate embedding of the set and prompts the generative model with this information to rank candidate items for the unknown item in the image.
A cart management system generates an error priority assignment for smart cart systems based on device error predictions for those smart cart systems. An error priority assignment is an assignment of the relative priority of servicing or providing maintenance to a set of smart cart systems. To generate the error priority assignment, the cart management system applies an error detection model to cart data received from the set of smart cart systems. The cart data has measurements captured by sensors coupled to the smart cart systems, and the error detection model uses the cart data to generate device error predictions. Each of these predictions represents a likelihood that a smart cart system will experience a device error within some time period. The cart management system uses the device error predictions to generate the error priority assignment and selects which smart cart system to service based on the error priority assignment.
An artificial intelligence (AI) agent is disclosed that assists an entity to complete a task. The entity is assigned to complete a task. The AI agent monitors events to detect an occurrence of an event associated with the task. A machine learning model of the AI agent is prompted to generate a set of candidate actions based in part on the detected event and data about the entity. A reinforcement learning model of the AI agent scores each candidate action from the set to tailor the candidate actions to the entity. A scored action is selected as a recommended response to the event and is communicated to a client device of the entity which causes the entity to perform the selected action.
An online concierge system provides a client application executed on a client device for customers to generate orders for fulfillment by the online concierge system. If the client device is unable to establish a data connection to a network, the client application locally caches data on the client device for one or more retailers that includes items that have been previously purchased by the customer or that are popular among customers. The customer generates an order through the client application for a retailer based on the locally cached items for the retailer. The online concierge system application generates an encrypted text message based on the order that is transmitted to the online concierge system via short message service (SMS). The online concierge system may also return messages via SMS, which may be presented by the client application.
An online system uses a trained machine-learning model to detect errors in catalog data based on interactions of users of the online system with physical carts. Upon receiving an interaction signal indicating an interaction by the user with a device in a location of a source or an action signal indicating an action in the location of the source, the online system applies the trained model to the interaction signal and/or the action signal to generate an error score for an item that indicates a likelihood of an error in relation to the item. Responsive to the error score being above a threshold score, the online system generates an error checking signal for confirming that the error is present. Responsive to the confirmation of the error, the online system generates a user interface that alerts about the error and requests an action to correct the error.
An online system generates a request to transport a set of items from a source location to a destination location. The set of items includes at least one temperature-sensitive item. The system extracts a set of input features about the request to transport the set of items. The set of input features includes an estimated transportation time for transporting the set of items from the source location to the destination location. The system applies a machine learning model to the set of input features to output a score for the temperature-sensitive item, indicating a likelihood that the temperature-sensitive item will transition outside of a target temperature range before completing the transportation. Responsive to the method outputting the score above a threshold, the system adjusts the request and outputs the adjusted request to one or more computing systems, causing the one or more computing systems to display the adjusted request.
An online system uses a trained machine-learning model to create an online cart or a physical cart for a user of the online system. Upon receiving a signal with an indication about an interaction by the user with one or more items via a first conversion channel of the online system, the online system retrieves one or more candidate items for the user to convert via a second conversion channel of the online system that is different from the first conversion channel. The online system applies the machine-learning model to output a conversion score for each retrieved candidate item that indicates a likelihood of conversion. Responsive to the conversion score being above a threshold score, the online system generates a user interface at a device associated with the user prompting the user to use the second conversion channel for conversion of each retrieved candidate item.
An online system uses a machine-learning model to identify servicing agents suited to perform tasks of new types. The online system maintains a list of tuples for servicing agents, each tuple including a score for a servicing agent and an identifier of a task type, the score indicating a level of aptitude of the servicing agent to perform a task of the task type. Upon obtaining a description for a task of a new type, the online system applies the machine-learning model to the list of tuples and the description for the task to generate a task score for each servicing agent that is indicative of a level of aptitude of each servicing agent for performing the task of the new type. The online system selects, using the task score for each servicing agent, servicing agents to whom the online system offers the task of the new type.
An online system configures one or more system AI agent instances that interact with user AI agents and performs one or more tasks on behalf of the online system. Thus, responsive to detecting the presence of a user AI agent representing a particular user, the online system directs the session for the user to communicate and interact with a system AI agent.
Embodiments relate to utilizing an optical character recognition extraction and a large language model (LLM) to automatically populate a shopping cart of a user of an online system with items from a physical recipe. The online system receives an image capturing the physical recipe and extracts a raw text from the received image. The online system generates a prompt for input into the LLM, the prompt including a task request for the LLM to generate a list of ingredients using the raw text. The online system inputs the prompt into the LLM to generate the list of ingredients. The online system maps the list of ingredients to a list of items available by one or more retailers associated with the online system. The online system causes a device of the user to display a user interface with the list of items.
An agentic model supported by language models tuned for interaction with pickers on behalf of users of an online system. Upon receiving a message from a picker related to fulfillment of an order of a user, the online system selects a language model of the agentic model associated with a cluster of users including the user and tuned to have a persona of the user that is common to the cluster of users. The online system requests the language model to generate, based on a prompt input into the language model including the message from the picker, first data related to the user and second data related to the cluster of users, a response to the message on behalf of the user. The online system causes a user interface of the device of the picker and a user interface of a device associated with the user to display the response.
An online system trains a ceiling prediction model to determine a user's ceiling for one or more item categories. The user's ceiling for an item category is a maximum amount of an item within the item category the user is likely to include in an order. Based on previously fulfilled orders for the user, information describing a current order from the user, and contextual information about the order, the ceiling prediction model determines the user's ceiling for an item category. The online system leverages the user's ceiling for an item category to refine content about different items that is selected for presentation to a user. For example, the online system determines whether the order includes a quantity of items from an item category that equals the user's ceiling for the item category when determining which items to present to the user.
A smart cart system accounts for edge cases in user interactions by leveraging sensor data and machine-learning models of a smart cart system. For example, a smart cart system uses sensor data to detect when a user removes an item from the smart cart system and presents content to the user on a display of the smart cart system based on the removed item. The smart cart system captures images of the storage area and applies an item identification model to the images to identify the item removed from the storage area. The smart cart system identifies a set of candidate items based on location sensor data describing a location of the smart cart system when the item was removed and computes presentation scores for each of the set of candidate items based on item data for each item the removed item.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G01G 19/52 - Appareils de pesée combinés avec d'autres objets, p. ex. avec de l'ameublement
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 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects
36.
PROMPTING A LARGE LANGUAGE MODEL TO PROVIDE RECOMMENDATIONS FOR IMPROVING A WEBSITE
An online system that maintains a website, such as a white-labeled website, designed by an entity retrieves a set of contextual data associated with the website, in which the set of contextual data includes information describing the entity, one or more elements of the website, or a historical performance of the website. The online system generates a prompt including the set of contextual data and a request for a set of recommendations for improving a performance of the website by updating a set of elements of the website. The online system provides the prompt to a large language model to obtain an output and extracts, from the output, the set of recommendations for improving the performance of the website. The online system sends the set of recommendations to a computing system associated with the entity.
A system artificial intelligence (AI) agent is trained to act on behalf of an online system. The system AI agent comprises a large language model that has been pre-trained using a set of system constraints and a set of system objectives. The system AI agent is trained adversarially using training service requests from a plurality of different user AI agents of different types to determine resolutions to the training service requests. Once trained, the system AI agent may determine resolutions to service requests of users of the online system. In some embodiments, the system agent may determine the resolutions via messaging with user AI agents that represent the users. The online system may further train the system AI agent (and in some embodiments the user AI agents) based in part on the resolutions to the service requests.
An automated checkout system automatically establishes sessions between users and shopping carts by correlating action events with distances of the user’s client device to the shopping cart. The automated checkout system determines the client device’s distance from the shopping cart at timestamps when an action event occurs with respect cart. If the distances and the action events are correlated, the system establishes a session between the user and the shopping cart. Additionally, the automated checkout system attributes target actions to recipe suggestions. The automated checkout system displays a recipe suggestion to a user on a display of a shopping cart, and identifies an item added to the shopping cart. If the added item matches an item in the set of recipes, the automated checkout system applies an attribution model that determines whether to attribute a target action that relates to the item with the recipe suggestion.
G06Q 20/18 - Architectures de paiement impliquant des terminaux en libre-service, des distributeurs automatiques, des bornes ou des terminaux multimédia
39.
SYSTEMS AND METHODS FOR TRAINING DATA GENERATION FOR OBJECT IDENTIFICATION AND SELF-CHECKOUT ANTI-THEFT
Disclosed are technologies for generating training data for identification neural networks. Series of images are captured of a plurality of merchandise items from different angles and with different background assortments of other merchandise items. A labeled training dataset is generated for the plurality of merchandise items. The series of captured images is normalized, where the merchandise occupies a threshold percentage of pixels in the normalized image. The training dataset is extended by applying augmentation operations to the normalized images to generate a plurality of augmented images. Each image is stored in the training dataset as a unique training data point for the given merchandise item it depicts. Labels are generated mapping each training data point to attributes associated with the depicted merchandise item. Input neural networks are trained on the labeled training dataset to perform real-time identification of selected merchandise items placed into a self-checkout apparatus by a user.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/772 - Détermination de motifs de référence représentatifs, p. ex. motifs de valeurs moyennes ou déformantsGénération de dictionnaires
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/20 - ScènesÉléments spécifiques à la scène dans les scènes de réalité augmentée
40.
GENERATION AND ASSIGNMENT OF EXPIRATION STATUS CHECKING TASKS USING A MACHINE LEARNING MODEL TO PREDICT ITEM FRESHNESS
Generation and assignment of expiration status checking tasks using an item freshness model is described. Candidate perishable items are identified to check for expiration at a source location associated with a source computing system. The candidate perishable items are applied to an item freshness model to generate scores for the plurality of candidate perishable items. Based in part on the scores, one or more of the candidate perishable items are selected as one or more perishable items for a picker to check for expiration status. Instructions are provided to a picker client device associated with the picker to check the one or more perishable items for expiration status. Expiration status data is received from the picker client device describing whether each of the one or more perishable items are expired. The expiration status data is provided to the source computing system.
An online system generates images for collections of items using an image generation model. To ensure a generated image accurately reflects a collection of items, the online system determines a type of the collection and selects a template including evaluation questions associated with the determined type. Evaluation questions are curated to determine accuracy of the content of a generated image for the collection. By applying a visual learning model to the questions in the selected template and the generated image, the online system identifies discrepancies between the image and the collection of items from the output of the vision language model. Subsequently, the online system prompts the image generation model to create an updated image for the collection that does not include the identified discrepancies. The online system may repeat the discrepancy identification and image modification until no discrepancies are found in the generated image.
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
42.
Natural Language Processing for Extracting Specific Items from a List of Ingredients
An online system receives a list of ingredients and corresponding quantities of each ingredient. Based on an item catalog of specific items offered by a source, the online system retrieves items offered by the source matching the ingredients and selects an item for an ingredient. Because the source may not offer an item in the same quantity specified by the list of items, the online system also maps a quantity of an ingredient in the list to a quantity of the selected item in a unit in which the source offers the corresponding item. The online system may convert a quantity of an ingredient to a quantity of an item through application of one or more rules or through application of one or more trained models to the quantity of the ingredient.
An online system performs flyer quality assurance monitoring to identify and remedy errors in flyers. The online system generates a prompt for a large language machine-learning model (LLM) to verify the flyer's accuracy. The prompt includes a portion of the flyer and a query to identify errors in that portion. The online system provides the prompt to a model serving system for execution by the LLM. The online system receives, from the model serving system, a response indicating error(s) identified in the portion of the flyer. Responsive to receiving identifying the errors, the online system performs remedial measure(s) to correct the identified error(s). Remedial measures may include correcting associations to items in an item catalog, modifying textual information or image data in the flyer, etc. The online system transmits the corrected flyer to client device(s) for presentation to user(s) of the online system.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
Item linked recipe generation using machine learning is described. Raw data is received that describes a recipe that uses ingredients. Ingredient descriptors are extracted from the raw data for the ingredients. Parsed ingredient data is determined using the ingredient descriptors and a large language model, such that the parsed ingredient data for each ingredient includes a name, a quantity, and a unit of measure. The name of each ingredient is mapped to a corresponding ingredient identifier that is part of an ingredient database. And each ingredient identifier in the ingredient database is associated with a corresponding item that is available for sale at one or more sources. A linked recipe is generated that includes for each ingredient: an ingredient identifier, a quantity of the ingredient, and a unit of measure of the quantity. A recommendation for the linked recipe is provided to a user client device.
An online system receives an image captured at a source location, in which the image depicts one or more objects. The system generates a prompt including the image and a request to identify, from the objects, a set of items available at the source location based on a database of items available at the source location, and to extract, from the image, text describing a price or a promotion associated with each identified item. The system provides the prompt to a large language model to obtain an output, in which the model is fine-tuned based on the database of items. The system extracts, from the output, an identifier and the text associated with each item, retrieves item data for each item based on the identifier associated with the item, and generates promotional content for the source location based on the item data and the price or promotion associated with each item.
An online system retrieves item data for items available at sources in multiple regions and generates candidate nodes based on the item data, in which each candidate node represents items having at least a threshold measure of similarity to each other. The system accesses and applies a machine-learning model to predict a matching score for each combination of an item and a candidate node based on item data for the item and attributes of items represented by the candidate node. The system assigns the items to candidate nodes based on the matching scores, retrieves information describing an availability of each item in each geographical region, and identifies an average availability of items assigned to each candidate node across the geographical regions. The system selects nodes to include in a region- and source-agnostic item database, in which the average availability associated with each selected node is at least a threshold.
A smart cart presents candidate content objects to a user according to presentation constraints determined based on a predicted route of the smart cart. The smart cart obtains, from an item database, a plurality of candidate content objects to be presented to a user of a smart cart. The smart cart obtains a location of the smart cart in an environment. The smart cart applies a machine-learning route prediction model to the location of the smart cart to determine a future route of the smart cart. The smart cart determines, for each candidate content object, one or more presentation constraints based on the future route of the smart cart, wherein the presentation constraints constrain presentation of the candidate content object to the user to maximize a likelihood of the user engaging with the content object. The smart cart presents, via an electronic display, one or more of the candidate content objects according to the presentation constraints.
An online system uses a trained machine-learning model for efficient packing of items. Upon receiving, from a device of an agent or a device of a source via a network, a signal indicating that a set of items are ready for packing, the online system applies the machine-learning model to identify, based at least in part on input data, a packing order for one or more items of the set of items. Based on the identified packing order for the one or more items, the online system generates a packing interface signal. The online system sends the packing interface signal, wherein sending the packing interface signal causes the one or more items to be packed according to the identified packing order. This process is repeated until it is confirmed that all items from the set of items were packed.
An online system utilizes a generative machine-learning model to generate a user interface of the online system with visualization of items of specific quantities. Upon receiving an interaction with an item on the user interface, the online system identifies a quantity of the item to show in the user interface. Responsive to identifying the quantity of the item, the online system generates a prompt for the generative model, the prompt including the identified quantity of the item, information about a reference object, and a request for generating an image of the identified quantity of the item in the reference object. The online system requests the generative model to generate, by providing the prompt to the generative model, the image of the identified quantity of the item. The online system updates the user interface to display the generated image of the identified quantity of the item in the reference object.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 40/40 - Traitement ou traduction du langage naturel
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
50.
PERSONALIZED MACHINE-LEARNED LARGE LANGUAGE MODEL (LLM)
A computer system finetunes a machine-learned language model to generate a personalized response to a user request. The system may generate a user representation for each of a plurality of users by applying a transformer model to a sequence of tokens representing a sequence of activities of the user. The system may train an evaluation model coupled to receive a user representation and a response to a user request and generate an estimated evaluation score indicating a level of personalization of the response to the user. The system may finetune a first machine-learned language model to generate a second machine-learned language model. The finetuned machine-learned language model is configured to provide personalized responses for customer services at an online concierge system.
An online system displays items to a user in search results based on appeasement scores for the items, adjusted according to how specific the search query is. The online system receives a search query from a user of an online system. The online system computes a query specificity score, a measure of the specificity of the search query. The online system accesses candidate items from a database that potentially match the search query. For each candidate item, the online system may compute or predict an appeasement score. The online system adjusts the appeasement score based on the query specificity score such that a more specific query weights the appeasement score lower than a less specific query. The online system may then compute a ranking score based on the adjusted appeasement score and display the candidate items to the user based on their ranking scores.
GENERATING A SUGGESTED SHOPPING LIST BY POPULATING A TEMPLATE SHOPPING LIST OF ITEM CATEGORIES WITH ITEM TYPES AND QUANTITIES BASED ON A SET OF COLLECTION RULES
An online system generates a template shopping list for a user by accessing a machine learning model trained based on historical order information associated with the user, applying the model to predict likelihoods of conversion for item categories by the user, and populating the template shopping list with one or more item categories based on the predicted likelihoods. The system ranks one or more item types associated with each item category in the template shopping list and determines a set of collection rules associated with one or more item categories/types based on the historical order information. The system generates a suggested shopping list by populating each item category in the template shopping list with one or more item types and a quantity of each item type based on the ranking and rules and sends the suggested shopping list and rules for display to a client device associated with the user.
An online system may include a multi-agent code generator that receives webpage data describing a webpage with target content, identifies the target content by analyzing the structure of the webpage, and generates a script configured to extract the target content. The online system can execute the script to extract the target content and store the extracted data in a database for later access by the online system. For example, a chatbot of the online system can reference the stored data describing the target content to generate a response to a query.
G06F 8/35 - Création ou génération de code source fondée sur un modèle
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
54.
Evaluating Output From Natural Language Processing System
An online system interfaces with an LLM to evaluate chatbot responses to user inputs in a conversation. The online system divides the conversation into portions and prompts the LLM to separately evaluate the chatbot's latest response in each portion. These conversation portions may include different amounts of the conversation and may build off of one another such that some portions include inputs/responses of other portions. To evaluate a chatbot's latest response in a portion, the online system may prompt the LLM to generate a score for the chatbot's response in the portion according to a conversation criterion. The prompt may instruct the LLM to consider the context of previous inputs/responses in that potion to generate the score. The online system reviews the scores and determines if any of the scores are below corresponding criteria thresholds. If so, the online system may perform a remedial action for the entire conversation.
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
A system and a method are disclosed for predicting future user engagement with a mobile device application based on a discrepancy detected between two physical objects. In an embodiment, a physical object provider receives, based on user input into the application, a request for delivery of a first physical object. A discrepancy is detected, the discrepancy reflecting that a second physical object is detected in place of the first physical object. A first set of features of the first physical object and a second set of features of the second physical object are inputted into a machine learning model. The machine learning model outputs a measure of predicted future engagement of the user with the application based on the discrepancy. The application is instructed to output an intervention based on the measure of predicted future engagement of the user.
An online system customizes documents for a particular context, user, or set of users. The online system receives an image of a physical document and extracts components, such as text, titles, items and their metadata, from the physical document. The online system may apply rules to the metadata for one or more items to determine whether to modify at least a portion of the metadata. The online system also applies a model to generate an affinity score for a context or a user and each component of the document. If the score for a component is below a threshold, the online system prompts a generative model to generate replacement content for the component. Subsequently, the online system applies the model to the generated replacement content and updates the document with the generated replacement content for the component if the score of the generated replacement content is higher.
An online concierge system generates the value for an impression by predicting future behavior by users beyond a current conversion. The predicted future behavior attributes incremental value of subsequent conversions by the user. The online concierge system gathers feature information about the user. Based on experimental data, the online concierge system generates a baseline curve describing expected user behavior for a category of users. Based on feature information of the user, the online concierge system applies a computer model to generate modifiers for the baseline curve to customize the baseline curve for the user. The modified curve is used to predict future actions by the user, and consequently a long-term incremental conversion value for the impression.
An online concierge system receives an order including one or more items from a customer and a picker obtains the items from a retailer. Upon completing obtaining items from the order and moving to checkout from the retailer, the picker updates an order status via a picker application. Via the picker application, the picker may capture an image of a shelf of items in the checkout line. The online concierge system identifies one or more items in the image using image processing and ranks the identified items for the customer from whom the order was received. The online concierge system includes a subset of the identified items ordered based on the ranking in a message to the customer via a communication interface between the customer and the picker. The message indicates the customer can add one or more of the identified items before the picker completes a checkout process.
An online concierge system selectively replaces default static item displays with dynamic item displays to represent items. The dynamic item displays encourage a viewing user of the online concierge system to purchase the items and may be selected based on item or user preferences or characteristics. The online concierge system applies a machine learning model to determine display scores describing the expected benefit of dynamic item displays and bandwidth scores describing resource usage of dynamic item displays. The online concierge system selectively replaces default static item displays with dynamic item displays based on the display and bandwidth scores so as to maximize benefit while ensuring that performance of the online concierge system is not negatively impacted by the resource usage.
An online concierge system receives, from a client device associated with a user of the online concierge system, order data associated with an order placed with the online concierge system, in which the order data describes a delivery location for the order. The online concierge system receives information describing a set of attributes associated with the delivery location and accesses a machine learning model trained to predict a difference between an arrival time and a delivery time for the delivery location. The online concierge system applies the model to the set of attributes associated with the delivery location to predict the difference between the arrival time and the delivery time for the delivery location and determines an estimated delivery time for the order based at least in part on the predicted difference. The online concierge system sends the estimated delivery time for the order for display to the client device.
An online concierge system uses a machine learning click through rate model to select promoted items based on user embeddings, item embeddings, and search query embeddings. Embeddings obtained by an embedding model may be used as inputs to the click through rate model. The embedding model may be trained using different actions to score the strength of a customer interaction with an item. For example, a customer purchasing an item may be a stronger signal than a customer placing an item in a shopping cart, which in turn may be a stronger signal than a customer clicking on an item. The online concierge system generates a ranking of candidate promoted items based on the search query and using the click through rate model. Based on the ranking, the online concierge system displays promoted items along with the organic search results to the customer.
INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL
An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.
A system receives an order for fulfillment from a customer device, the order associated with a delivery time. The system determines a base compensation value for the order and sends the order and base compensation value to devices of one or more fulfillment agents. If the order is not accepted within a predetermined time, the system applies a trained machine learning model to updated input features of the order and the fulfillment agents to predict an amount of lateness time past the delivery time. Based on the predicted amount of lateness time, the system determines an updated lateness value, determines an updated compensation value, and sends the order with the updated compensation value to the fulfillment agents. The system repeats prediction, lateness value determination, and compensation adjustment until the order is accepted.
An online system predicts time to park at a fulfillment location in fulfillment of an order by a fulfillment user. The online system receives an order from a requesting user, and applies a timeliness prediction model to the order, the parking configuration of the corresponding fulfillment location, to other contextual factors, or some combination thereof to predict the time to park at the fulfillment location. The timeliness prediction model is trained on historical orders with their associated completion times and known parking configurations of the respective fulfillment locations. The online system may batch orders together to optimize fulfillment efficiency in consideration of the predicted lag time for the order. The online system assigns and transmits the batches to fulfillment users to fulfill at the fulfillment locations.
An online system uses benchmarking tests to identify indexing algorithms for an embedding database. To perform these benchmarking tests, the online system receives a set of parameters for configuring an embedding database. For example, the parameters may include a performance parameter and a latency parameter. The online system generates algorithm scores for a set of candidate indexing algorithms based on the parameters. Specifically, the online system tests each of the candidate indexing algorithms by generating a testing database based on a subset of the entries for the full database and by performing benchmarking tests on the testing database. The online system uses these tests to compute performance metrics for each candidate indexing algorithm and uses those performance metrics to compute an algorithm score for each indexing algorithm. The online system uses the computed algorithm scores to select an indexing algorithm for the embedding database.
An online system receives a query from a user of the online system. The online system identifies a candidate set of cold start results to the query defined as having been presented to the user less than a threshold number of times. The cold start results are then filtered based on their relevance to the query to generate a final set of cold start results and a score is generated for each cold start result without interaction data using a scoring baseline common to standard results with interaction data. Accordingly, the online system ranks the cold start results with a set of standard results based on the score for each cold start result using the scoring baseline and presents the same for display to the user.
A smart system, such as a smart shopping cart system, uses an efficient selection algorithm to select an item identifier prediction for an item. The smart cart system uses a set of machine-learning models to generate identifier predictions based on images. To select an item identifier, the smart system applies an efficient selection algorithm to the predictions from the machine-learning models. An efficient selection algorithm is an algorithm that requires minimal computational resources to perform. For example, the efficient selection algorithm may be a simple majority algorithm that selects the identifier prediction generated by a majority of the models or a weighted voting algorithm where each model's vote is weighted by some metric. The smart system applies the efficient selection algorithm to select an item identifier prediction from the ones generated by the models. The smart system may display content related to the item associated with the item identifier prediction.
G06V 30/224 - Reconnaissance de caractères caractérisés par le type d’écriture de caractères imprimés pourvus de marques de codage additionnelles ou de marques de codage
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
An online system uses a trained machine-learning model to predict hard-to-find items, which may facilitate picking of these items. The online system receives, from one or more devices of one or more pickers, a device of a source, one or more devices associated with one or more users, and/or a computing system associated with a physical receptacle utilized by at least one user for shopping in a location of the source, data with information about an item. The online system applies the trained machine-learning model to output, based on the received data, a findability score for the item indicative of a findability of the item. Based on the findability score, the online system generates and communicates one or more action signals to a device of a picker, the device of the source, and/or a device associated with a user prompting one or more actions in relation to the item.
G06Q 10/087 - Gestion d’inventaires ou de stocks, p. ex. exécution des commandes, approvisionnement ou régularisation par rapport aux commandes
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source
70.
Display panel of a programmed computer system with a graphical user interface
An online system uses a trained model for intelligent handling of unclaimed online pickup orders. After identifying that an order placed by a user of the online system is unclaimed at a location of a source, the online system obtains, from a device of a picker associated with the online system and/or a device associated with the source, signals with information about each item in each bundle of the unclaimed order. The online system applies the trained model to identify, based on the obtained signals, a preferred method for disposal of each bundle. Based on the identified preferred method for disposal of each bundle, the online system generates a disposal decision signal and communicates the disposal decision signal to the device associated with the source that prompts personnel at the location of the source to dispose each bundle of the unclaimed order using the identified preferred disposal method.
An online concierge system generates suggested instructions for presentation to a user. The online concierge system access instruction examples corresponding to a target item category and generates candidate instruction representations based on instruction messages within each instruction example. The online concierge system generates preliminary scores for the candidate instruction representations that are directly related to an intra-category frequency of use of the instruction tokens of the candidate instruction representation within the target item category. The online system normalizes these preliminary scores for the candidate instruction representations based on the inter-category frequency of use of the instruction tokens in all item categories to generate final scores for the candidate instruction representations. The online concierge system selects a set of instruction representations based on these final scores and generates suggested instructions based on the set of instruction representations.
An online system automatically identifies item attributes of an item. The online system prompts a set of outputs from a set of multi-modal large language models with an image of the product and a request to determine if the details of size information is present in the image. The online system receives a set of outputs, wherein an output describes whether the size information is present in the image. The system then prompts the set of language models with a request to extract the value of the size information in the image. Responsive to determining that a threshold number of outputs have matching values of size information that is present in the image, the system updates the item attribute data with the matching values of size information of the product.
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
74.
Machine learning approach to provide adaptive search result page load size and layout
An online system receives, at a search interface, a search query from a user. The online system determines a recall set size for search results of the search query. The online system determines a page load size to display at least a portion of the search results by determining a query entropy associated with the search query, inputting a plurality of signals into a machine learning model, the plurality of signals comprising the query entropy, and receiving, from the machine learning model, the page load size. The online system selects a set of physical object identifiers based on the page load size. The online system generates for display a user interface that groups the selected physical object identifiers. The online system causes a device associated with the user to display the generated user interface.
A trained model is used to determine a price sensitivity feature for a user of an online system. The online system generates input data by gathering replacement data via a user interface at a device associated with the user and/or in-store behavior data related to replacement of items performed by the user at a location of a retailer when using a physical receptacle in communication with the online system. The online system applies a price sensitivity model to predict, based on the input data, a price sensitivity score for the user indicative of the price sensitivity feature of the user. The online system identifies, based on the price sensitivity score, one or more actions related to prompting the user to convert one or more items. The online system applies the one or more actions to prompt the user to convert the one or more items.
Based on orders fulfilled by shoppers of an online concierge system, the online concierge system identifies items in an order that are difficult to find in a warehouse in which the order is fulfilled. When a shopper obtains a difficult to find item from the warehouse, the online concierge system prompts the shopper to provide information for finding the difficult to find item in the warehouse. The online concierge system stores the information for finding the difficult to find item from the shopper in association with the difficult to find item and with the warehouse. Subsequently, when a different shopper is fulfilling an order from the warehouse including the difficult to find item, the online concierge system displays the information for finding the difficult to find item in the warehouse to the different shopper.
An online system uses a trained model to predict incremental sales caused by a sample counter for in-store free sampling of an item. Upon receiving signals related to in-store purchases of the item, the online system applies the trained model to output, based on the received signals, a ranked list of locations of a source and a ranked list of timeslots for placing the sample counter. The online system selects, from the ranked list of locations and the ranked list of timeslots, a location of the source and a timeslot for placing the sample counter, and generates a decision signal based on the selected location and the selected timeslot. The online system communicates, via the network to a device associated with the source, the decision signal prompting the source to place the sample counter for free sampling of the item at the selected location and during the selected timeslot.
A client device or an online system communicating with the device receives video data captured by a camera of the device, in which the video data depicts a field of view of a display area of the device. The device/system detects an object within the field of view based on the video data and applies one or more machine-learning algorithms to identify the object as an item available at a source. The device/system accesses item data for items available at the source and selects one or more supplemental items associated with the identified item based on item data for the identified item and each supplemental item. The device/system generates an augmented reality element including a listing of the supplemental item(s), as well as information or a selectable option associated with each supplemental item. The augmented reality element is then displayed in the display area of the device.
A client device, or an online system communicating with the device, receives video data depicting a field of view of a display area of the device and applies machine-learning algorithms to the video data to detect objects, including portions of a body of a user of the device, within the field of view and to determine a series of body poses. The device/system uses machine-learning models to predict an action performed by the user based on the series of poses and to predict a recipe being prepared based on the objects and a predicted series of actions performed by the user. The device/system selects a suggestion associated with preparing the recipe based on candidate suggestions associated with preparing the recipe, the objects, or the predicted series of actions, and generates an augmented reality element describing the suggestion. The augmented reality element is displayed in the display area of the device.
An online system retrieves a set of user data including information describing one or more interactions by a user with the system. The system accesses and applies a machine-learning model to predict an exploration score for the user based on the set of user data, in which the score describes a likelihood of a set of interactions by the user with content associated with less than a threshold measure of familiarity to the user. Upon receiving a request from a client device associated with the user to access a user interface including content recommended to the user, the system selects content to recommend to the user based on the score and information describing a set of previous interactions by the user with the content. The system generates the user interface including the selected content and sends the user interface to the client device where it is displayed.
A device may obtain historical order data comprising orders submitted by users to an online system, each order indicating a retailer location and a timestamp. A device may generate a first set of training examples, each training example indicating order demand at a retailer location during one period of time from a first set of periods of time. A device may train the demand forecast prediction model with the first set of training examples. A device may apply the demand forecast prediction model to a second set of periods of time to predict order demand for each period of time in the second set of periods of time. A device may track order demand across each period of time in the second set of periods of time. A device may generate a second set of training examples, each training example indicating a difference between the predicted order demand and the tracked order demand at the retailer location during each period of time from the second set of periods of time. A device may retrain the demand forecast prediction model with the second set of training examples.
A trained model is used to generate a user interface of an online system based on predicted nutritional preferences for a user of the online system. Upon receiving a signal indicating interaction of the user with the online system, the online system applies the trained model to output, based on user's features, item features and/or session features, a vector of scores for the user, where each score is indicative of a preference of the user for a respective nutritional attribute of a set of nutritional attributes. Responsive to a score being greater than a threshold score, the online system generates, based on the received signal, a user interface of a device associated with the user that includes a label about the nutritional attribute associated with the score. The online system causes the device associated with the user to display the user interface with the label about the nutritional attribute.
A user interface of an online system is generated based on search for relevant items that match ingredients of a recipe. After receiving, from a device associated with a user of the online system, a query for an ingredient of a recipe, the online system identifies, based on one or more attributes in the query, a set of candidate items for the ingredient. The online system generates a recipe relevance score for each candidate item by applying a weighted sum of scores, ranks the identified candidate items based on their recipe relevance scores, and selects one or more items for presentation to the user. The online system then generates a user interface of the device with a recipe page including the ingredient of the recipe and the one or more items that match the ingredient of the recipe.
An online concierge system fulfills orders placed by users. When a user notifies the online concierge system of a problem with order fulfillment, the online concierge system performs one or more remedial actions (e.g., a credit, a discount, a free delivery). To provide a proactive remedial action before receiving a notification of a problem with order fulfillment from a user, the online concierge system trains a proactive remediation model that predicts, for an order having an event during fulfillment, a likelihood of loss of interaction by the user with the online concierge system (i.e., “churn” of the user) without performing a proactive remedial action. When fulfilling an order, in response to determining an event during fulfillment, the online concierge system applies the proactive remediation model to determine the likelihood of churn of the user if no proactive appeasement is performed, for determining whether to perform a proactive remedial action.
An online system displays an interface to users including slots in which sources from a list of sources of items (e.g., physical items, content items) are presented. The user may select a source via the interface to view items provided by, or associated with, the source. To simplify a user identifying a desired source, the online system includes sources that a user is likely to select as well as new sources in the list. To balance the competing interests of relevance of sources with which the user previously interacted and discovery of new sources, the online system selects an allocation of slots for new sources and for sources with prior interaction based on interactions by users in the geographic regions with different allocations of slots. Based on the selected allocation of slots, the online system selects specific retailers for each slot using ranking models corresponding to different slots.
Personalized recommendations matching a list of item descriptors to catalog products from is described. A list associated with a user is received that includes item descriptors. The item descriptors correspond to catalog products stored in a catalog database that includes a plurality of catalog products. Linking data for the user is retrieved. For at least one of the item descriptors in the list, a model is applied to the linking data to generate a score for each of a set of candidate catalog products. A list of recommended catalog products for the user is built by, for each of the item descriptors in the list, selecting one of the set of candidate catalog products based on the generated scores. The list of recommended catalog products is provided to a user client device associated with the user. The user client device is configured to display the list of recommended catalog products.
An online concierge system receives, from a client device associated with a user, a request to access a user interface including a listing of sources associated with the system, in which each source is associated with a catalog of items. The system retrieves user data describing interactions by the user with items available at the sources and accesses and applies a machine-learning model to predict a user engagement score for each item-source pair associated with the sources based on the user data, in which the score indicates a likelihood of an interaction by the user with an item available at a source. The system selects a set of item-source pairs based on the scores and generates the user interface including the listing and a selectable option to add an item associated with each selected pair to a shopping list. The system then sends the user interface to the client device.
A computer system uses clustering and a large language model (LLM) to normalize attribute tuples for items stored in a database of an online system. The online system collects attribute tuples, each attribute tuple comprising an attribute type and an attribute value for an item. The online system initially clusters the attribute tuples into a first plurality of clusters. The online system generates prompts for input into the LLM, each prompt including a subset of attribute tuples grouped into a respective cluster of the first plurality. Based on the prompts, the LLM generates a second plurality of clusters, each cluster including one or more attribute tuples that have a common attribute type and a common attribute value. The online system maps each attribute tuple to a respective normalized attribute tuple associated with each cluster. The online system rewrites each attribute tuple in the database to a corresponding normalized attribute tuple.
G06F 16/21 - Conception, administration ou maintenance des bases de données
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06F 16/28 - Bases de données caractérisées par leurs modèles, p. ex. des modèles relationnels ou objet
Clustering database items based on output of machine-learning model to link database items that represent the same core item but with a different stored size or form attribute
A trained machine-learning model is used to group items in a database of an online system that represent a same core item (i.e., product) but of different attributes. The online system applies, for pairs of items from a specific chunk of the database, the machine-learning model to metadata for each pair of items and category data for each pair of items to generate a clustering score for each pair of items that indicates a likelihood that both items belong to a cluster of items that identifies the core item. The online system then requests a language model to generate a response including a list of attributes in a structured form for each item from the cluster. The online system stores, in an entry of the database associated with each item from the cluster, the list of attributes in the structured form and an identification of the cluster.
An online concierge system may use images received from shopping carts within retailers to determine the availability of items within those retailers. A shopping cart includes externally-facing cameras that automatically capture images of the area around the shopping cart as the shopping cart travels through a retailer. The online concierge system receives these images, which depict displays within the retailers from which a picker or a retailer patron can collect items. The online concierge system determines which items should be depicted in the images and which items are actually depicted in the images. The online concierge system identifies which items should be depicted, but are not depicted, and determines that these items are unavailable (e.g., out of stock) at that retailer. The online concierge system updates an availability database to indicate that these items are unavailable and may notify the retailer that the item is unavailable.
A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.
An automated checkout system modifies received images of machine-readable labels to improve the performance of a label detection model that the system uses to decode item identifiers encoded in the machine-readable labels. For example, the automated checkout system may transform subregions of an image of a machine-readable label to adjust for distortions in the image's depiction of the machine-readable label. Similarly, the automated checkout system may identify readable regions within received images of machine-readable labels and apply a label detection model to those readable regions. By modifying received images of machine-readable labels, these techniques improve on existing computer-vision technologies by allowing for the effective decoding of machine-readable labels based on real-world images using relatively clean training data.
G06K 7/14 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation électromagnétique, p. ex. lecture optiqueMéthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire utilisant la lumière sans sélection des longueurs d'onde, p. ex. lecture de la lumière blanche réfléchie
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
An online system trains a machine-learned lift prediction model configured as a neural network. The machine-learned lift prediction model can be used during the inference process to determine lift predictions for users and items associated with the online system. By configuring the lift prediction model as a neural network, the lift prediction model can capture and process information from users and items in various formats and more flexibly model users and items compared to existing methods. Moreover, the lift prediction model includes at least a first portion for generating control predictions and a second portion for generating treatment predictions, where the first portion and the second portion share a subset of parameters. The shared subset of parameters can capture information important for generating both control and treatment predictions even when the training data for a control group of users might be significantly smaller than that of the treatment group.
An online system receives a user query for execution by at least one of a set of generative artificial intelligence (AI) models. The online system assigns the user query to one or more query categories of a set of query categories. The online system accesses a dataset stored in a database. For each query category, the dataset stores a preferred generative AI model for the query category among the set of generative AI models. The online system selects a preferred generative AI model for the user query from the database based on the one or more query categories assigned to the user query. The online system provides a prompt to a model serving system hosting the selected generative AI model. The online system receives, from the model serving system, a response to the user query generated by executing the prompt.
An online system performs an inference task in conjunction with the model serving system infer seasonality of items in an item catalog hosted by the online system. The online system generates and provides a prompt to a machine-learned language model to output a list of item categories predicted to be in season for a particular time period and a particular geographical location, e.g., associated with a requesting user. The language model outputs the list of item categories predicted to be in season. The online system validates the list by leveraging the language model and/or historical user engagement data. The online system maps items in the item catalog to the seasonal item categories and tags the mapped items with an in-season badge for display with the item in an ordering interface to the requesting user.
A trained model is used to predict a type of a user of an online system to generate a personalized user interface of the online system. Upon receiving data related to a current session of the user with the online system, the online system applies the trained model to output, based on the session data, a score for the user indicative of a predicted type of the user for the current session. The online system compares the score with a threshold score, and responsive to the score being greater than the threshold score, the online system identifies, based on the score, user data, and information about the current session, a set of user interface elements arranged in a specific order for presentation to the user. The online system then generates a user interface of the device associated with the user that includes the arranged user interface elements.
A machine learned model for item recommendations following failed attempts to purchase those items. During a session, an online system receives a request to fulfill an order from a user device. The system receives a message indicating that an item from the order was not fulfilled. The system logs the item in connection with a profile of the user stored in a database of the online system. During a subsequent session with the user device, the system determines that the logged item is available for fulfillment. The system applies the model to output an intent score indicative of an intent of a user of the user device to acquire the logged item. The logged item is ranked based on the intent score, and a user interface is generated that includes a recommendation to acquire the logged item. The system causes the user device to display the generated user interface.
An online concierge system maintains a graph of items available for purchase. The graph maintains edges between items, where an edge between an item and an additional item indicates that one or more customers have previously replaced the item with the additional item. The edge between the item and the additional item also identifies a number of times customers have replaced the item with the additional item. When a customer orders an item, the online concierge system traverses the graph of items to identify candidate replacement items for the ordered item and identifies one or more of the candidate replacement items to the customer. When identifying the candidate replacement items, the online concierge system accounts for distance between the ordered item and different candidate replacement items in the item graph.
An online concierge system receives item data for an item included among an inventory at a retailer location, in which the item data includes a set of real-time item data for the item and a set of constraints. The system accesses and applies a first machine-learning model to predict a freshness satisfaction score for the item based at least in part on the item data. The system updates the item data to include the score and accesses and applies a second machine-learning model to predict an elasticity of demand for the item based at least in part on the updated item data. The system determines an optimal value associated with the item based at least in part on the freshness satisfaction score, the elasticity of demand, and the set of constraints. A value associated with the item is then adjusted based at least in part on the optimal value.
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