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.
A search module for an online concierge system executes searches in response to a search query with respect to item databases of retailers. The search module dynamically configures a recall set size that controls a number of search results returned for a search query based in part on a query entropy representing an estimated breadth of the search term. The query entropy may be determined relative to a diversity of items in a retailer's database. The recall set size may be configured relative to the query entropy in a manner that manages a tradeoff between latency of search execution and search result quality.
An online concierge system receives location information associated with pickers and actual orders associated with a geographical zone. A model trained to predict a likelihood an actual order associated with the zone will be available for servicing within a timeframe is accessed and applied to forecasted orders. Each picker is matched to an order for servicing by minimizing a value of a function that is based on a difference between a location associated with each picker matched to an actual order and an associated retailer location, a difference between the location associated with each picker matched to a forecasted order and an associated retailer location, and the predicted likelihood. Recommendations for accepting an actual order, moving to a retailer location associated with a forecasted order, or checking back later with the system are generated based on the matches and sent for display to a client device associated with each picker.
A language model is used to generate autosuggestions to complete or revise a user's partial search query. An initial partial query is applied to the language model to generate query candidates for completing the search query. The language model may generate the query candidates as additional or alternate tokens for the partial search query. When the user revises the partial query, the previously-generated candidates can be re-used to reduce subsequent processing time for generating additional candidates. The previously-generated candidates are compared with the revised partial query to select which of the candidates to be re-used and expanded for generating additional tokens. Additional tokens can be generated in parallel for the previously-generated candidates or with model values from the previous generation, enabling the tokens to be generated effectively with reduced latency consistent with user expectations for search-related autosuggestions.
An automated checkout system uses a shopping cart that is automatically charged when stacked into another shopping cart. Each shopping cart has a front charging connector and a rear charging connector. When a first shopping cart is stacked into a second shopping cart, the front charging connector of the first shopping cart connects with the rear charging connector of the second shopping cart. Electrical power can flow to the first shopping cart via the second shopping cart to charge a battery of the first shopping cart. The second shopping cart may be similarly stacked into a third shopping cart, wherein the second shopping cart receives electrical power from the third shopping cart. The second shopping cart may use this electrical power to charge its own battery or may provide some or all of the electrical power to the first shopping cart to charge the first shopping cart's battery.
B62B 3/14 - Hand carts having more than one axis carrying transport wheelsSteering devices thereforEquipment therefor characterised by provisions for nesting or stacking, e.g. shopping trolleys
A47F 10/04 - Furniture or installations specially adapted to particular types of service systems, not otherwise provided for for self-service type systems, e.g. supermarkets for storing or handling self-service hand-carts or baskets
B60L 53/16 - Connectors, e.g. plugs or sockets, specially adapted for charging electric vehicles
B62B 5/00 - Accessories or details specially adapted for hand carts
USING MACHINE-LEARNING MODEL OF AN ONLINE SYSTEM TO GENERATE A SOURCE-RELATED CONFIDENCE SCORE FOR SERVICING A LIST OF COMPONENTS REQUESTED BY AN ONLINE PLATFORM
An online system uses a trained machine-learning model to generate a confidence score for servicing a list of components (e.g., recipe) at a specific source. Upon receiving the list of components from an online platform, the online system identifies a set of candidate items that match each component from the list. The online system further identifies a matching score and a number of matches for each component. The online system then applies the machine-learning model to the matching score, the number of matches, and user’s conversion data to generate a confidence score for the list of components that is indicative of a likelihood that the list of components are located at the source. The online system selects the list of components for the source and sends the list of components and an identification of the source for displaying at a user’s device.
TRAINING MACHINE-LEARNING MODEL OF AN ONLINE SYSTEM TO DETERMINE A LEVEL OF MATCHING BETWEEN IDENTIFIERS OF ITEMS STORED IN A DATABASE OF ONLINE SYSTEM
An online system trains a machine-learning model to identify a level of matching (equivalency or similarity) between item identifiers (e.g., brands) stored in a database. The online system receives search data including information about a search query including a series of identifiers. The online system further receives conversion data including information about a search query in relation to a first identifier that is followed by conversion of an item having a second identifier. The online system further receives communication data exchanged between a servicing agent and a user with information about a message including identifiers of items. The online system generates, based on the search data, the conversion data, and/or the communication data, training data for the machine-learning model. The online system trains, using the training data, the machine-learning model to identify the level of matching between an identifier searched for by a user and a replacement identifier.
An online system receives, at a search interface, a search query from a user. The online system determines a recall set size for search results of the search query. The online system determines a page load size to display at least a portion of the search results by determining a query entropy associated with the search query, inputting a plurality of signals into a machine learning model, the plurality of signals comprising the query entropy, and receiving, from the machine learning model, the page load size. The online system selects a set of physical object identifiers based on the page load size. The online system generates for display a user interface that groups the selected physical object identifiers. The online system causes a device associated with the user to display the generated user interface.
An online system performs an atypical replacement recommendation task in conjunction with a model serving system or the interface system to make recommendations to a user for replacing a target item with an atypical replacement item. The online system receives a search query from a user and identifies a target item based on the search query. The online system identifies a set of candidate items for replacing the target item. The online system may select one or more atypical replacement items in the set of candidate items, and generate an explanation for each atypical replacement item. The explanation provides a reason for using the atypical replacement item to replace the target item. The online system provides the atypical replacement items and the corresponding explanations as a response to the search query.
An online system adjusts a guardrail setting used by a user treatment engine based on conditions faced by the online system. The online system simulates the performance of the user treatment engine using different candidate guardrail settings and computes a score for each of the guardrail settings based on the performance of the user treatment engine using each of the guardrail settings. The online system selects a new guardrail setting for the user treatment engine based on the performance scores for the candidate guardrail settings. Furthermore, the online system generates simulated training examples to initially train a user treatment engine. The online system uses a treatment performance model to simulate the effect of treatments applied to users and generates simulated training examples based on the predicted effect of the treatments. The online system retrains the user treatment engine on real training examples that are generated based on actual treatments.
A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.
G01G 19/12 - Weighing apparatus or methods adapted for special purposes not provided for in groups for incorporation in vehicles having electrical weight-sensitive devices
G01G 19/40 - Weighing apparatus or methods adapted for special purposes not provided for in groups with provisions for indicating, recording, or computing price or other quantities dependent on the weight
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
Use of a language model to automatically perform visual assessment of quality of an item being fulfilled by a picker. The online system receives an image of the item and identifies a set of potential problems associated with the item. The online system generates a plurality of prompts for input into the language model including the image and one or more questions each corresponding to a respective potential problem of the set potential problems. The online system requests the language model to generate, based on the plurality of prompts, a feedback response for each potential problem. The online system generates an aggregated output by aggregating the feedback response for each potential problem, and based on the aggregated output, a second message that identifies one or more relevant problems associated with the item. The online system causes a device of the picker to display the second message.
An artificial intelligence (AI) agent generates responses customized to a user based in part on monitored actions of the user. The AI agent, formed from a machine-learning model, is instantiated with inputs that include a set of objectives, an online catalog of items, and user data associated with a user of an online system. Actions performed by a user on the online system are monitored. Action types of at least some of the monitored actions are determined. Responsive to a determination that an action of the monitored actions has an action type of a set of predetermined types of actions, the AI agent is prompted with a description of the action and a request to suggest a response to the action. The determined response is based in part on one or more of the set of objectives. The response suggested by the AI agent is invoked.
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 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
16.
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.
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 is configured to manage messaging between artificial intelligence (Al) agents. A service request (such as a request to order items) is received at an online system from a user client device. A system Al agent and a user Al. agent are instantiated with inputs that include a. set of objectives or constraints that guides each of the system Al agent and the user Al agent during messaging with the other. The online system manages rounds of messaging between the system Al agent and the user Al 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 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.
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.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
36.
IDENTIFYING ITEMS IN IMAGES USING EMBEDDINGS GENERATED FROM THE IMAGES AND RANKING CANDIDATES USING A LANGUAGE MODEL
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.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
37.
ARTIFICIAL INTELLIGENCE AGENT USING LANGUAGE MODEL AND REINFORCEMENT LEARNING MODEL TO GUIDE PICKING PROCESS
An artificial intelligence (Al) agent is disclosed that assists an entity to complete a task. The entity is assigned to complete a task. The Al agent monitors events to detect an occurrence of an event associated with the task. A machine learning model of the Al 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 Al 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.
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.
B62B 5/00 - Accessories or details specially adapted for hand carts
B62B 3/14 - Hand carts having more than one axis carrying transport wheelsSteering devices thereforEquipment therefor characterised by provisions for nesting or stacking, e.g. shopping trolleys
G01C 21/20 - Instruments for performing navigational calculations
B62B 3/00 - Hand carts having more than one axis carrying transport wheelsSteering devices thereforEquipment therefor
G01C 21/00 - NavigationNavigational instruments not provided for in groups
39.
Using machine-learning model to generate a user interface with personalized combined filters for search results
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 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 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 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 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 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.
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.
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
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 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.
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 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 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.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
59.
AUTOMATICALLY ESTABLISHING SESSIONS BETWEEN USERS AND SHOPPING CARTS
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.
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.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patternsGenerating dictionaries
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/20 - ScenesScene-specific elements in augmented reality scenes
61.
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 - Editing figures and textCombining figures or text
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
63.
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 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06T 11/60 - Editing figures and textCombining figures or text
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of 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.
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.
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
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.
B62B 3/14 - Hand carts having more than one axis carrying transport wheelsSteering devices thereforEquipment therefor characterised by provisions for nesting or stacking, e.g. shopping trolleys
A47F 9/04 - Check-out counters, e.g. for self-service stores
B62B 5/00 - Accessories or details specially adapted for hand carts
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 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 9/451 - Execution arrangements for user interfaces
G06F 40/40 - Processing or translation of natural language
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
73.
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.
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.
B62B 5/00 - Accessories or details specially adapted for hand carts
H04W 4/02 - Services making use of location information
G01C 21/20 - Instruments for performing navigational calculations
76.
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.
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 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
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.
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.
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 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.
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.
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 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.
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 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
96.
Display panel of a programmed computer system with a graphical user interface
An online system displays an interface to users including slots in which sources from a list of sources of items (e.g., physical items, content items) are presented. The user may select a source via the interface to view items provided by, or associated with, the source. To simplify a user identifying a desired source, the online system includes sources that a user is likely to select as well as new sources in the list. To balance the competing interests of relevance of sources with which the user previously interacted and discovery of new sources, the online system selects an allocation of slots for new sources and for sources with prior interaction based on interactions by users in the geographic regions with different allocations of slots. Based on the selected allocation of slots, the online system selects specific retailers for each slot using ranking models corresponding to different slots.
A client device or an online system communicating with the device receives video data captured by a camera of the device, in which the video data depicts a field of view of a display area of the device. The device/ system detects an object within the field of view based on the video data and applies one or more machine-learning algorithms to identify the object as an item available at a source. The device/system accesses item data for items available at the source and selects one or more supplemental items associated with the identified item based on item data for the identified item and each supplemental item. The device/system generates an augmented reality element including a listing of the supplemental item(s). as well as information or a selectable option associated with each supplemental item. The augmented reality element is then displayed in the display area of the device.
G06T 19/00 - Manipulating 3D models or images for computer graphics
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06V 10/72 - Data preparation, e.g. statistical preprocessing of image or video features
G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
99.
Using a Trained Machine-Learning Model of an Online System to Handle Unclaimed Online Pickup Orders
An online system uses a trained model for intelligent handling of unclaimed online pickup orders. After identifying that an order placed by a user of the online system is unclaimed at a location of a source, the online system obtains, from a device of a picker associated with the online system and/or a device associated with the source, signals with information about each item in each bundle of the unclaimed order. The online system applies the trained model to identify, based on the obtained signals, a preferred method for disposal of each bundle. Based on the identified preferred method for disposal of each bundle, the online system generates a disposal decision signal and communicates the disposal decision signal to the device associated with the source that prompts personnel at the location of the source to dispose each bundle of the unclaimed order using the identified preferred disposal method.
An online concierge system generates suggested instructions for presentation to a user. The online concierge system access instruction examples corresponding to a target item category and generates candidate instruction representations based on instruction messages within each instruction example. The online concierge system generates preliminary scores for the candidate instruction representations that are directly related to an intra-category frequency of use of the instruction tokens of the candidate instruction representation within the target item category. The online system normalizes these preliminary scores for the candidate instruction representations based on the inter-category frequency of use of the instruction tokens in all item categories to generate final scores for the candidate instruction representations. The online concierge system selects a set of instruction representations based on these final scores and generates suggested instructions based on the set of instruction representations.
