An online concierge system iteratively makes a batch of one or more orders available to an increasing number of shoppers to choose to fulfill. Each shopper may choose to accept or reject a batch for fulfillment. To improve batch acceptance and matching between batches and shoppers, the batches are scored with respect to expected resource costs, likelihood of acceptance by the shopper, and/or other quality metrics to iteratively offer the batch to an increasing number of shoppers (prioritizing the scoring factors) until a shopper accepts. The number of shoppers notified of the batch and the frequency that additional shoppers are selected may vary based on characteristics of the batch and likelihood the batch will be accepted by a shopper.
A wayfinding application executing on a client device receives a current location of the device within a warehouse and accesses a layout of the warehouse describing locations of items included among an inventory of the warehouse. The application identifies a route from the current location to one or more locations within the warehouse associated with one or more target items, generates augmented reality elements including instructions for navigating the route, and sends the elements to a display area of the device. The application detects a location within the warehouse associated with a target item and determines whether the item is at the location based on an image captured by the device. Upon determining it is not at the location, the application alerts a user of the device to a replacement item by generating an augmented reality element that calls attention to it and sending the element to the display area.
An online concierge system selects content for presentation to a user by using a product scoring engine. The product scoring engine generates a user embedding for user data and a query embedding for query data. The product scoring engine generates an anchor embedding based on the user embedding and the query embedding, where the anchor embedding is an embedding in a product embedding space. The product scoring engine compares the anchor embedding to a set of product embeddings to score a set of products for presentation to a user.
An online system receives a description of a task and generates a prompt for a generative machine-learned model. The prompt includes the description of the task and a request for a sequence of actions associated with the task, and a list of candidate actions and attributes related to the list of candidate actions. The online system provides the prompt to a model serving system deployed with the machine-learning model for execution. The online system obtains a response from the model serving system to extract wherein at least one workflow including the sequence of actions once executed completes a portion of the task. The online system receives, as output from the machine-learned model, the sequence of actions associated with the description of the task. The sequence of actions is executed in order to complete at least a portion of the task.
Self-checkout vehicle systems and methods comprising a self-checkout vehicle having a camera(s), a weight sensor(s), and a processor configured to: (i) identify via computer vision a merchandise item selected by a shopper based on an identifier affixed to the selected item, and (ii) calculate a price of the merchandise item based on the identification and weight of the selected item. Computer vision systems and methods for identifying merchandise selected by a shopper comprising a processor configured to: (i) identify an identifier affixed to the selected merchandise and an item category of the selected merchandise, and (ii) compare the identifier and item category identified in each respective image to determine the most likely identification of the merchandise.
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
A method for predicting customer long-term behavior using LLM-based modeling is described. The online system receives a representation of a stimulus or treatment that is presented to a user and generates a summary of a simulated user profile. The online system performs an inference task in conjunction with the model serving system or interface system to infer one or more actions that will likely be performed in response to the representation of the stimulus based on the simulated user profile. The online system computes a surrogate measure based on the response received from the model serving system and computes a correlation coefficient between the surrogate measure and a true metric of interest from collected experiment data. Responsive to determining a correlation coefficient greater than a threshold value, the online system predicts the true metric of interest based on the surrogate measure.
A system uses a contextual bandit model for query processing. The system receives, from a client device, a user query for identifying one or more items by the system. The user query is described by one or more query features. The system obtains one or more contextual features describing a context of the user query. The system applies a contextual bandit model to the query features and the contextual features to select a query processing model from a plurality of query processing models. The system applies the selected query processing model to the user query to obtain query results. The system transmits the query results for display on the client device.
A computer system uses a machine-learned language model to generate an SQL query for a user query. The system receives a user query comprising a task for performing a database query. The system identifies an embedding for the user query to represent the user query. The system generates a prompt for input to a machine-learned language model, and the prompt specifies the user query, metadata associated with the identified data table and a request to generate one or more SQL statements for performing the database query on the data table. The system provides the prompt to a model serving system and receives an output generated that includes the requested SQL statements for performing the database query. The system presents a response to the user query using the received SQL statements.
G06F 16/908 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06F 16/2457 - Query processing with adaptation to user needs
G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
An online system receives a recipe from a customer mobile device. The online system performs natural language processing on the recipe to determine parsed ingredients. For each of one or more of the determined parsed ingredients, the online system maps the parsed ingredient to a generic item. The online system queries a product database with the mapped generic item to obtain one or more products associated with the mapped generic item. The online system applies a machine-learned conversion model to each of the one or more products to determine a conversion likelihood for the product. The conversion model may be trained based on historical data describing previous conversions made by customers presented with an opportunity to add products to an order. The online system selects a product from the one or more products based on the determined conversion likelihoods and adds the selected product to an order.
An online system maintains item embeddings for items. As a number of items maintained by the online system increases, maintaining a single index of the item embeddings is increasingly difficult. To increase scalability, the online system partitions item embeddings into multiple indices, with each index corresponding to a value of a specific attribute maintained by the online system for items. For example, an online system generates indices that each correspond to a different warehouse offering items. To expedite retrieval of item embeddings, the online system allocates each index to one of a number of shards. When the online system receives a query, the online system determines an embedding for the query and retrieves an index from a shard based on metadata received with the query. Based on distances between the query for the embedding and the item embeddings in the retrieved index, the online system selects one or more items.
An online concierge system generates an item graph connecting item nodes with attribute nodes of the items. When the online concierge system receives a search query to identify one or more items from a customer, the online concierge system parses the search query into combinations of terms and identifies item nodes and attribute nodes related to the search query. The online concierge system may determine that no item nodes meet presentation criteria. The online concierge system may determine that a reformulated search query has a higher conversion probability than the search query received from the customer. The online concierge system reformulates the search query. The online concierge system selects item nodes as search results. The online concierge system transmits the search results to the customer.
A computer system uses a machine-learned language model to generate an SQL query for a user query. The system receives a user query comprising a task for performing a database query. The system identifies an embedding for the user query to represent the user query. The system generates a prompt for input to a machine-learned language model, and the prompt specifies the user query, metadata associated with the identified data table and a request to generate one or more SQL statements for performing the database query on the data table. The system provides the prompt to a model serving system and receives an output generated that includes the requested SQL statements for performing the database query. The system presents a response to the user query using the received SQL statements.
An online system predicts key items for triggering a replacement workflow when determined to be unavailable. The online system receives, from a first client device associated with a first user, an order comprising a list of items to be obtained at the location by a second user. The online system applies a prediction model to the list of items to classify whether each item is a key item. Responsive to the prediction model classifying a first item as being a key item, the online system tags the first item as a key item. The online system transmits the list of items with the key item for display on a second client device associated with a second user. The online system receives a message from the second client device indicating that the key item is unavailable at the location. In response, the online system initiates a high-friction replacement workflow for the key item instead of a low-friction replacement workflow.
A trained model is used to predict a scheduled delivery for a self-picked order. Responsive to receiving an indication from a device associated with a user of an online system that the device is either within a defined vicinity from a location of a retailer or physically present at the location of the retailer, the online system applies a user targeting computer model trained to generate, based on user data and ordering data, a score for the user indicative of a likelihood of the user accepting an offer for the scheduled delivery of the order. Responsive to the score being greater than a threshold score, the online system generates a list of service options for the scheduled delivery of the order and displays the list of service options at a user interface of the device prompting the user to select a service option for the scheduled delivery of the order.
In accordance with one or more aspects of the disclosure, a managed marketplace analyzes marketplace statistics across different sub-markets to identify a target supply-demand ratio for each sub-market that balances the degree of supply (e.g., for a service such as product delivery) with the degree of consumer demand so as best to achieve a balance of different objectives. In each of various sub-markets, metric values are generated by corresponding prediction models for each of the supply-demand ratios for that sub-market, and the metric values are combined into a single score to determine how well that particular supply-demand ratio achieves the overall objectives of the managed marketplace. For each sub-market, the candidate supply-demand ratio leading to the greatest score is selected as the target ratio. Policies of one or more downstream subsystems are adjusted so as to shift the current supply-demand ratio of the sub-market toward the target optimal supply-demand ratio.
A system uses both a present cost model and a future cost model trained on logged order data to compute a prediction of costs for delivering orders either without further delay, or with delay to allow time to potentially batch orders for delivery with other orders (and thereby reduce delivery cost). A comparison of the outputs of the present and future cost models is used to determine whether to delay assigning the order in expectation of batching order with other orders. Calculations may additionally be performed for the constituent orders of an order batch to apportion the delivery cost saving resulting from batching among the different orders. The system can analyze previously-logged data associated with prior orders to obtain features that characterize the prior orders. Using these features, and the known actual delivery costs from the prior completed deliveries, the system can train the present and future cost models.
An online concierge system receives orders and allocates orders to pickers who obtain items in an order from a retailer and deliver the items to a customer from whom the order was received. When an item included in an order is unavailable, the online concierge system suggests one or more replacement items for the item. To select a replacement item for an unavailable item, the online concierge system uses a set of models trained to predict a probability or each of a set of events, including one or more negative events, for a candidate replacement item. The online concierge system generates a score for a candidate replacement item as a weighted combination of the predicted probabilities, with negative weights applied to the probabilities for negative events. Based on the scores for various candidate replacement products, the online concierge system selects one or more candidate replacement items for an item.
An online concierge system receives orders and allocates orders to pickers who obtain items in an order from a retailer and deliver the items to a customer from whom the order was received. When an item included in an order is unavailable, the online concierge system suggests one or more replacement items for the item. To select a replacement item for an unavailable item, the online concierge system generates replacement scores for each of a set of candidate replacement items. A replacement score for a candidate replacement item is generated from a probability of the customer performing a positive action when the unavailable item is replaced by the candidate replacement item and a predicted probability of a picker finding the candidate replacement item at the retailer. Based on replacement scores for various candidate replacement items for an unavailable item, the online concierge system selects one or more candidate replacement items.
An online system performs an inference task in conjunction with the model serving system and/or interface system to generate relevant product images for query auto-completion and query suggestion to help users better navigate their search experience. The online system generates a collection of query suggestions using search query log mining. For each query suggestion in the collection of query suggestions, the online system retrieves one or more catalog images that depict the query suggestion from a product catalog. The online system constructs a prompt to a text-to-image model including the query suggestion, and a request to generate one or more query images based on the query suggestion. The online system receives the query images from the text-to-image model and ranks the catalog and query images to identify an image to display to the user in association with the query suggestion.
An online system performs inference requests in conjunction with the model serving system to perform AI (text-based LLM or multi-modal transformer)-generated integration test variants. Instead of rigid code-specified integration tests, the LLM creates integration test variants that follow a specification. Given one or more files from the codebase of an application and a specification for integration testing, the LLM compiles an integration test including a series of actions (e.g., API calls) as runnable code and assertions about the state of the application after the actions are executed.
An online concierge system receives an order including one or more items from a client device associated with a user and retrieves user data for the user. The system accesses and applies a machine-learning model to predict a measure of preference of the user associated with each item category associated with the items based on the user data and identifies an item associated with at least a threshold predicted measure of preference. The system retrieves picker data for a picker assigned to collect the items and predicts a level of expertise of the picker associated with collecting the identified item based on the picker data. If the predicted level of expertise is less than a threshold, the system identifies an expert picker based on picker data for the expert picker and sends a prompt to a client device associated with the expert picker to assist with collecting the identified item.
An online concierge system generates a plurality of bundles based in part on a seed list of contexts by generating one or more prompts that are provided to a machine learned model, where for a first context of the seed list of contexts, a prompt instructs the machine learned model to determine: a first bundle for the given context, where the first bundle includes a list of products, a title, and an explanation. The plurality of bundles is stored in a datastore. The system selects, for a customer, the first bundle from the plurality of bundles in the datastore, and generates a list of items from an online catalog that corresponds to the list of products of the first bundle. The system provides the list of items, the title, and the explanation for presentation to a customer client device of the customer.
An off-policy evaluation system performs episodic off-policy evaluations to perform off-policy evaluation (OPE) for multiple, joint episodes. For a single episode, a first machine learning model outputs a propensity for each action for the user and selects a first action for the user from the set of propensities. For a second episode, a second machine learning model outputs a propensity for each action for the user and selects a first action for the user from the set of propensities. The second machine learning model is evaluated by determining an importance weight for the first model and the second model to determine the inverse propensity score of the second machine learning model.
An online concierge system dynamically determines types of shopping events. The types may be used in various ways to increase efficiency of an item pipeline. The system may monitor interactions of a customer with an ordering interface on a customer client device associated with the customer. The monitoring may be during a shopping event that is categorized by a type, wherein the type describes a purpose of the shopping event. Responsive to a monitored interaction being an interaction from a set of trigger interactions, the system may determine a type of shopping event by applying the monitored interaction and content of a shopping cart of the ordering interface to a type prediction model. The system may assign an updated type to be the determined type, and perform an action based in part on the updated type.
A trained computer model is used to generate content for recommendation to a user of an online system based on prediction of a future travel of the user. The online system accesses a computer model trained to output a likelihood of the user conducting a travel within a future time period. The computer model outputs, based on user data associated with the user, the likelihood of the user conducting the travel within the future time period. Responsive to the likelihood of the user conducting the travel being above a threshold value, the online system generates, based on information about conversion by the user of a set of items during a past time period, a list of items for recommendation to the user. The online system causes a device associated with the user to display a user interface with the list of items for inclusion into a cart of the user.
A trained model is used to generate a post-delivery effort-based tip increase recommendation for a user of an online system. The online system applies the computer model to predict, based on information about an original order placed by the user and data describing an effort required to fulfill the order, a tip amount that is likely to lead to satisfaction of a picker associated with the online system who fulfilled the order. Responsive to information about a sentiment of the user in relation to the fulfillment process, the online system generates, based on the predicted tip amount and an original tip amount provided by the user before the fulfillment process for the order was completed, a tip adjustment amount. The online system causes a user interface of a device associated with the user to display the tip adjustment amount prompting the user to adjust the original tip amount.
A trained model is used to generate market adjustment recommendations for a retailer associated with an online system. Upon displaying an item to a user of the online system for replacing an originally requested item and collecting user's engagement data in relation to the replacement item, the online system accesses a market adjustment model that is trained to generate a score for the user indicative of an affinity of the user in relation to the replacement item and generate one or more market adjustment recommendations for the retailer. The online system applies the market adjustment model to generate, based on the engagement data, behavioral information of the user and/or contextual information associated with the user, the score for the user and the one or more market adjustment recommendations for the retailer. The online system provides the one or more market adjustment recommendations to a computing system associated with the retailer.
Advertising, marketing, and promoting the goods and services of others via a global computer network, namely, advertising and promoting the availability of goods for selection, ordering, purchase, and delivery; Comparison shopping services; Promoting the sale of goods and services of others by providing special offers and online catalogs; Administration of a customer loyalty program which provides discounts on products and services; Inventory management; Inventory control; Inventorying merchandise
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing a website featuring non-downloadable software using artificial intelligence (AI) for enabling autonomous checkout; Development of new technology for others in the field of retail store services, namely, robotics for retail stores; Providing online non-downloadable computer software platforms for use in database management, sales, customer tracking and management, and inventory management for the e-commerce, wholesale and retail industries; Providing a website featuring non-downloadable software using artificial intelligence (AI) for empowering cameras to identify and understand human activities; Providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for enabling self-service electronic checkout and purchasing of goods by users; Providing temporary use of on-line non-downloadable software for users to perform electronic business transactions via global computer network; Providing temporary use of on-line non-downloadable software for use in processing credit and debit card payments; Providing temporary use of on-line non-downloadable software development tools for allowing third parties to integrate retailer applications, e-commerce sites and retail fulfillment backend systems to cloud-based consumer marketplace or a locally deployed server; Providing temporary use of on-line non-downloadable software for image recognition and matching images so analyzed with other image data; Providing temporary use of on-line non-downloadable software for use in extracting visual attributes from images that may be downloaded from a global computer network; Providing temporary use of on-line non-downloadable software for use in acquiring shopping information and price comparisons that may be downloaded from a global computer network; Providing temporary use of on-line non-downloadable software for providing weight, dimension, shape, and location data for digitally scanned products; Providing temporary use of on-line non-downloadable software for users to identify and locate online stores where goods that have been digitally photographed and interpreted by image recognition software can be purchased; Providing temporary use of on-line non-downloadable software for tracking a shopper's location inside a store and displays it to users via a screen interface while shopping; Providing temporary use of on-line non-downloadable software for use in inventory tracking and data analytics in the field of retail stores; Providing temporary use of on-line non-downloadable software for analyzing retail store customer data; Providing temporary use of on-line non-downloadable software for analyzing consumer behavioral data; Providing temporary use of on-line non-downloadable software for collecting and aggregating consumer behavior data within a retail store; Providing temporary use of on-line non-downloadable software for providing dynamic pricing of items throughout the store; Providing temporary use of on-line non-downloadable software for providing recommendations of items for purchase based on shopper preferences; Providing temporary use of on-line non-downloadable software for use in advertising products and brands via electronic displays throughout retail stores; Development of new technology for others in the field of retail store services, namely, robotics and automation for order fulfillment and inventorying merchandise; Providing temporary use of on-line non-downloadable software for allowing users to access coupons and discounts on the products and services of others
09 - Scientific and electric apparatus and instruments
Goods & Services
Recorded computer programs using artificial intelligence (AI) for checkout system comprised of customer self-service electronic checkout stations for point of sale; Point-of-sale terminals; Recorded computer programs using artificial intelligence (AI) for checkout systems comprised of cameras to track and process purchases of goods from stores; Self-checkout terminals; Battery chargers for use with electric shopping carts; Computer hardware and recorded software systems for the wholesale and retail store industries, namely, downloadable point-of-sale software and computer hardware for inventory management, operating computer systems, processing of sale transactions, data and accounting management, customer relationship management, transmission of payment information, inventory management, management of consumer loyalty programs and gift card processing; Optical readers; Computer monitors; Radio receivers and transmitters; Central processing units (CPU); Cameras; Electronic payment terminal; Downloadable software in the nature of a mobile application for allowing users to execute shopping-related demands in the nature of building a shopping list, placing an order for groceries and retail products online, and selecting recipes according to groceries purchased; Barcode readers; Downloadable computer application software for mobile phones, handheld computers, and tablets, featuring image recognition, optical decoding, audio recognition, audio decoding, and code technology, namely, software for use in reading scanned advertisements in print, mobile, online, and radio format, displaying consumer product information about goods or services, providing consumers with purchase options that include customer loyalty and rewards programs offerings, personalized sales offers, coupons, and recommendations, and providing consumers with the ability to order, purchase, and obtain delivery of general consumer merchandise and services; Interactive computer kiosk systems comprised primarily of computers, computer hardware, computer peripherals, and computer touchscreens for use in shopping carts, shopping baskets, and any form of container used in a retail store setting for purchasing goods; Weighing scales; Weighing apparatus and instruments; Computer hardware; Bar code scanners; Touchscreen monitors; Radio-frequency receivers; Mobile computers; Downloadable software development kits (SDK); Downloadable computer software for use in payment processing; Recorded computer software for use in payment processing; Downloadable software in the nature of a mobile application for commerce, namely, software that allows users to perform electronic business transactions via a global computer network; Consumer coupons downloaded from a global computer network; Downloadable computer software for enabling users to access coupons and discounts on the products and services of others; Computer hardware and recorded software systems for performing electronic business transactions via a global computer network; Downloadable computer software for processing electronic payment transactions; Downloadable computer programs for processing electronic payment transactions
36 - Financial, insurance and real estate services
Goods & Services
Payment processing services, namely, credit card and debit card transaction processing services; Charge card and credit card payment processing services; Processing of electronic wallet payments; Processing of credit card payments via near field communication (NFC) technology-enabled devices
A language model is utilized to generate a recommendation for a user of an online system to update a current order. The user is grouped into a cluster of users based on how likely is that the user will not use a credit before expiration. A prompt for input into the language model includes information about the cluster, content of a cart, and information about the credit. Based on the prompt, the language model generates a risk score for the user representing a likelihood of the user not using the credit. The online system identifies, based on the risk score and the content of cart, a quantity of an item for recommendation to the user. A user client device displays a user interface with a suggestion for the user to include the quantity of the item in the cart and use the credit for purchasing the suggested quantity of item.
To balance deficiencies between large-language models (LLM) and machine-learning models, an online system uses a query specificity score to dynamically determine an appropriate model for generating item groupings for a received search query. The query specificity score is a score that measures the specificity of a search query. If the query specificity score is below a threshold, the online system utilizes an LLM to determine a description of a pre-generated item grouping associated with the search query. If the query specificity score is below a threshold, the online system utilizes an LLM to generate a set of item groupings and a description of the generated item groupings. The computed query specificity score enables the online system to dynamically identify which search queries can be effectively addressed using an LLM.
G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
36.
User Interface for Implementing Modifications to a Content Campaign Suggested by a Large Language Model
An online system publishes sponsored content items to users. To enable a publishing user to evaluate performance of a campaign including sponsored content items and identify modifications to improve the campaign, the online system trains a large language model (LLM). Information about previous campaigns and their performance, previously asked questions about the campaigns, and actions for modifying the campaigns are used to train the LLM. For a particular ad campaign, the online system generates a prompt for the LLM to generate a list of suggestions and corresponding actions. The online system generates an interface including the suggestions in conjunction with interface elements causing performance of one or more of the actions when selected by the publishing user.
An online system provides a search interface for a user to identify items. The search interface may present suggested search queries to the user, allowing the user to select a suggested search query rather than manually entering search terms to form a search query. To identify search queries most likely to be selected by the user, the online system gets a set of candidate search queries and generates a relevance score for each candidate search query by applying a trained query relevance model to each candidate search query. The scored candidate search queries are selected and ranked using the relevance scores, and the selected candidate search queries are displayed using the ranking in the search interface. The query relevance model is a transformer-based small language model receiving a user sequence of prior search queries and items with which the user interacted and the candidate search terms as input.
An online system receives a request from a client device associated with a user to generate a recipe. Based on the request and data for the user, the system generates a prompt to generate the recipe, provides the prompt to a large language model, extracts the recipe from an output of the model, and displays an interface describing the recipe. Upon receiving an additional request to modify the recipe, a process including generating another prompt to modify the recipe, providing this prompt to the model, extracting a modified recipe from another output of the model, and updating the interface to describe the modified recipe, is performed and repeated for each additional request. When a recipe is accepted, the system predicts an availability of each associated item and updates the interface to include an option to add a set of the items to a shopping list based on the predicted availability.
G06F 40/40 - Processing or translation of natural language
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
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
An online system uses Large Language Models (LLM's) to simulate the behavior of real customers in such tests as a low-cost way to simulate A/B tests. A prompt is constructed for a given customer. The prompt is sent to the LLM with a request to infer the predicted outcome of a treatment. The online system collects the output. Statistical analyses are run based on the output of the previous step to determine which treatment to select for the user.
An online system may obtain an image of a receipt from a user, wherein the receipt includes a list of item identifiers and associated charges. The online system may provide a prompt to a first machine learning model including the image or extracted information from the image, and a request to provide descriptors of a list of items corresponding to the item identifiers in the image. The online system may receive from the first machine learning model as a response, the list of items and associated charges. The online system maps the list of items to one or more items in an item catalog of an online system. The online system may add the one or more items to an order associated with the user.
An online concierge system maintains various attributes for each item. To optimize information about items displayed in an interface, the online concierge system selects a subset of attributes of an item for display based on an item category including the item. The online concierge system applies an attribute selection model to combinations of an item category and attributes associated with the item category. The attribute selection model selects one or more attributes for an item category using one or more of an engagement model trained from prior interactions by customers and an output of a large language model prompted to select relevant attributes based on the item category. When generating an interface including an item, the online concierge system includes the subset of attributes selected for an item category including the item in conjunction with the item.
An online system sends content items for display to client devices associated with users and detects actions associated with the content items performed by the users. The system accesses and applies machine-learning models to predict metrics for a set of users and generates a set of optimal values for the set of users based on the metrics, one or more objectives, and a set of constraints, in which each optimal value indicates whether a user is eligible to be presented with a content item. Responsive to identifying an opportunity to present the content item to a user of the set of users, the system determines whether the user is eligible to be presented with the content item based on an optimal value determined for the user and sends the content item for display to a client device associated with the user if the user is eligible.
A trained computer model is used to adjust a revenue objective weight based on a current session of a user of an online system. In response to a user's search query, the online system retrieves a set of candidate items and applies a multi-objective ranking computer model to generate a set of weights for each candidate item, each weight associated with one specific objective of a set of objectives. The online system then applies a revenue adjustment computer model trained to adjust, based in part on content of a cart, a weight that is associated with a revenue objective. The online system generates a ranking score for each candidate item by applying the set of weights including the adjusted weight to the set of objectives. Based on the ranking scores, the online system selects one or more items from the set of the candidate items for recommendation to the user.
Disclosed are visual recognition and sensor fusion weight detection system and method. An example method includes: tracking, by a sensor system, objects and motions within a selected area of a store; activating, by the sensor system, a first computing device positioned in the selected area in response to detecting a presence of a customer within the selected area: identifying, by the sensor system, the customer and at least one item carried by the customer; transmitting, by the sensor system, identifying information of the customer and the at least one item to a computing server system via a communication network; measuring, by the first computing device, a weight of the at least one item; transmitting, by the first computing device, the weight to the computing server system via the communication network; and generating, by the computing server system, via the communication network, transaction information of the at least one item.
G07G 1/14 - Systems including one or more distant stations co-operating with a central processing unit
A47F 9/04 - Check-out counters, e.g. for self-service stores
G01G 19/415 - 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 using electromechanical or electronic computing means using electronic computing means only combined with recording means
G01G 21/22 - Weigh-pans or other weighing receptaclesWeighing platforms
An online concierge shopping system fulfills orders using workers who pick items at a warehouse to complete an order and workers to deliver the orders to a customer's location. To optimize the staffing of workers for each task, the system uses a trained model to predict the number of workers needed to achieve an optimal outcome based on an input set of contextual information. The system also schedules specific workers to various shifts using the predicted number of workers needed and then searching a feasibility space for an optimal solution. The trained model may be updated based on performance observations.
An online system receives a query from a user. An online system generates a prompt to provide to a first machine learning model to determine tags associated with the query. An online system provides the prompt to the first machine learning model. An online system receives as output a set of query tags associated with the query. An online system obtains a list of ranked product tags associated with the query, wherein the product tag is ranked according to a conversion rate of products matching the product tag when users submitted a historical search query. An online system identifies a set of candidate products for the search query. An online system, for each candidate product, provides the set of features to a second machine-learning model to generate a score for the candidate and selects at least a subset of the candidate products based on the generated scores for the candidate products.
A system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples. The system may receive item predictions generated by the prompt being applied to the machine learned model, the item predictions may have corresponding item identifiers. The item predictions are processed to identify a recommended item from the item predictions. The processing includes determining item information for the recommended item using an item identifier associated with the recommended item. The item information is provided to the customer client device.
Disclosed herein relates to a self-checkout anti-theft vehicle system, comprising: a self-checkout vehicle having a plurality of sensors and components implemented thereon, the self-checkout vehicle being used by shoppers for storing selected merchandises in a retail environment; and a centralized computing device. The centralized computing device is configured to: obtain information related to each merchandise selected and placed into the self-checkout vehicle by a shopper by exchanging data with the plurality of sensors and components via a first communication network, identify each merchandise via a second, different communication network based at least upon the information obtained from the plurality of sensors and components, and process payment information of each merchandise.
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
G06K 7/10 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation
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
G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
G06N 3/044 - Recurrent networks, e.g. Hopfield networks
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
An online system generates personalized content carousels and personalized content items in conjunction with large language models (LLMs). The personalized carousels and items are generated subject to consent from users. In one or more embodiments, a content item is a recipe page, coupon, incentive, advertisement, sponsored page, or sponsored item, accessed via the online system. The online system generates one or more carousel themes for the user based on the order history of the user by prompting the LLM. For each carousel theme, the online system also generates a set of content item names. The online system applies an embedding model to identify content items that are relevant to each content name. One or more content carousels including the set of content items are presented to the user.
An online system maintains various items and maintains values for different attributes of the items, as well as an item embedding for each item. When the online system receives a query for retrieving one or more items, the online system generates an embedding for the query. Based on measures of similarity between the embedding for the query and item embeddings, the online system selects a set of items. The online system identifies a specific attribute of items and generates a whitelist of values for the specific attribute based on measures of similarity between item embeddings for items in the selected set and the embedding for the query. The online system removes items having values for the selected attribute outside of the whitelist of values from the selected set of items to identify items more likely to be relevant to the query.
A receipt capture device can collect transaction information from transactions conducted at a point of sale system by capturing receipt data transmitted from the point of sale system for the purpose of printing receipts at an external receipt printer. The receipt capture device can then send the collected receipt data to an online system for analysis. At the online system, received receipt data can be decoded from the printer-readable format it is transmitted in and used to enhance the online system's understanding of transactions occurring at a retailer associated with the point of sale system. For example, the online system can determine an approximate inventory of items available at purchase at the retailer by aggregating items recently purchased in transactions at the point of sale system.
A ranking computer model is trained based on grouping a collection of users of an online system into different buckets based on intended likelihoods of presenting a set of content items to the collection of users, wherein a contextual bandit model is employed to compute the intended likelihoods. The online system applies the ranking computer model to generate, based on user data for a user of the online system and contextual data associated with a current session of the user, a ranking score for each content item in a set of content items. The online system selects, based on the ranking score for each content item, one or more content items from the set of content items. The online system causes a device associated with the user to display a user interface with the one or more content items for recommendation to the user.
An online concierge system predicts how available tasks will be for a particular assistant in the assistant's current context. Task availability is computed differently in different embodiments. In a first embodiment, the task availability assessment functionality predicts an expected gap between demand for task performance and supply of assistants to perform those tasks. This expected gap is compared to historical gap values in a market segment (e.g., a particular geographical region during a particular span of time) to make a rough assessment of task availability relative to the average of that market segment. In a second embodiment, a set of features relevant to nearby retailer locations, the current geographic location, and/or the particular assistant is input to a deep learning model, which accordingly predicts a specific amount of time until the assistant receives a first task assignment.
A ranking computer model is trained based on grouping a collection of users of an online system into different buckets based on intended likelihoods of presenting a set of content items to the collection of users, wherein a contextual bandit model is employed to compute the intended likelihoods. The online system applies the ranking computer model to generate, based on user data for a user of the online system and contextual data associated with a current session of the user, a ranking score for each content item in a set of content items. The online system selects, based on the ranking score for each content item, one or more content items from the set of content items. The online system causes a device associated with the user to display a user interface with the one or more content items for recommendation to the user.
The online system is configured to efficiently handle user requests by choosing a suitable prompt from a pre-curated library and selecting one of a plurality of large language models (LLMs) to respond to the user queries. These prompts are tailored for compatibility with different LLMs. When a user query is received, the system simultaneously forwards it to multiple LLMs and receives diverse responses. Performance metrics are then generated based on these multiple responses, aiding in the selection of the most suitable LLM. The chosen LLM is used for processing subsequent queries from the same user. This approach not only ensures that users receive high-quality, prompt responses but also optimizes the system's performance by dynamically selecting the most efficient LLM based on both quality and speed.
An online system receives information describing a physical retail store, in which the information includes attributes of physical elements within the store and their arrangement. A request is received from a user to generate a rendering of the store in a virtual reality environment. A profile of the user describing the user's geographic location and a set of historical actions performed by the user are accessed, in which the set of historical actions is associated with one or more of the physical elements. Based on the information describing the store and the profile, the rendering is generated to include virtual reality elements representing a set of the physical elements arranged based on the arrangement of the physical elements, and the rendering is sent for display to the user. When an update to the information describing the store is received, the rendering is updated and sent for display to the user.
An online concierge system suggests replacement items when an ordered item may be unavailable. To promote similarity of sources between the replacement item with the ordered item, candidate replacement items are scored, in part, based on a source similarity score based on a source of the candidate replacement item and a source of the ordered item. The source similarity score may be determined by a computer model based on user interactions with item sources. The similarity score may be based on source embeddings that may be determined based on respective item embeddings or may be determined by training source embeddings directly from user-source interactions. The similarity score for a candidate replacement item may be combined with a replacement score indicating the user's likelihood of selecting the candidate replacement item as a replacement to yield a total score for selection as suggestion as a replacement for the ordered item.
An online concierge system assigns shoppers to fulfill orders from users. To allocate shoppers, the online concierge system predicts future supply and demand for the shoppers' services for different time windows. To forecast a supply of shoppers, the online concierge system trains a machine learning model that estimates future supply based on access to a shopper mobile application through which the shoppers obtain new assignments by shoppers. The online concierge system also forecasts future orders. The online concierge system estimates a supply gap in a future time period by selecting a target time to accept for shoppers to accept orders and determining a corresponding ratio of number of shoppers and number of orders. The online concierge system may adjust a number of shoppers allocated to the future time period to achieve the determined ratio number of shoppers and number of orders.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
60.
SUGGESTING REPLACEMENT ITEMS BY INFERRING INTENT OF A USER OF AN ONLINE SYSTEM USING A TRAINED MODEL
A recipe prediction model is used to suggest replacement items by inferring intent of a user of an online system. Upon receiving a signal indicating that an item in a cart requested by the user is not available and responsive to identifying a failure of a replacement model to identify suitable replacement items according to defined criteria, the online system applies the recipe prediction model trained to infer a recipe that is potentially associated with the item and output a recipe's name and a replacement item category. The online system collects, based on the recipe's name, a set of recipes from a database. The online system identifies, from the set of recipes, based on the replacement item category, a set of candidate replacement items. A device associated with the user displays a user interface with one or more replacement items selected for recommendation to the user.
An online system may provide an instruction prompt to a machine-learned language model. The instruction prompt may include an instruction to generate an evaluation label of a training sample of a classification model and a textual format related to how data is arranged. The evaluation label may be used in a supervised training of the classification model. The online system may provide a batch of evaluation request prompts to the machine-learned language model. Each evaluation request prompt includes data that is at least partially arranged in the textual format described in the instruction prompt. The online system may receive a plurality of responses from the machine-learned language model. Each response includes the evaluation label corresponding to each evaluation request prompt. The online system may store at least evaluation labels and the data in the evaluation request prompts as training samples for the supervised training of the classification model.
Classifying results of a user's search query using a trained classification model. In response to the search query, an online system retrieves a set of candidate search results, each candidate search result associated with a respective item of a plurality of items. The online system accesses the classification model that is trained to compute a probability of classification of each item into each class of a plurality of classes, each class associated with a type of relevance to the search query. The online system applies the classification model to generate, for each item, a classification score associated with each class. The online system classifies, based on the classification score, each item into a corresponding type of relevance to the search query. The online system selects, based on the classification of each item, a list of items for displaying at a user interface of a device associated with the user.
An online system displays search results in response to a query by receiving a query from a customer. An online system accesses a set of candidate items and computes a relevance score and personalization score for each item. The online system computes the relevance score based on query data and item data and may normalize the relevance score. The online system computes the personalization score based on item data, such as an item embedding, and user data, such as a user embedding. The online system computes a query specificity score and adjusts the personalization score with the query specificity score such that generic queries have high personalization scores and specific queries have low personalization scores. The online system combines the relevance and personalization scores for each candidate item into a ranking score and displays the candidate items to the customer based on their ranking scores.
An online system includes an interface which facilitates communication between customers and pickers who are servicing the user's order. The customer may request a modification to their order through the interface. The online system performs an inference task in conjunction with the model serving system or the interface system to continuously monitor conversations between users and pickers to infer whether a customer requested to modify their order to maintain an updated order and an updated in-store transaction estimate for the order. The online system determines if the order has been updated to account for the requested changes. If the order has not been updated, the online system automatically updates the customer's order and computes an updated in-store transaction estimate based on the changes made.
An online shopping concierge platform receives data indicating one or more customer interactions associated with a particular item offered by the online shopping concierge platform; identifies a plurality of different and distinct images of the particular item; generates, based at least in part on multiple different and distinct machine learning (ML) models and for each image of the plurality of different and distinct images, a composite score for the image; selects, based at least in part on its respective composite score, an image of the particular item to be presented to the customer; generates data describing a graphical user interface (GUI) comprising a listing of the particular item including the selected image; and communicates to a computing device associated with the customer the data describing the GUI such that the computing device associated with the customer renders and displays the listing.
An online system displays an ordering interface, and responsive to receiving a request from a client device to place an order including one or more items to be collected from a retailer location, the system retrieves data associated with each item. The system accesses and applies a machine-learning model to predict a likelihood of each item being a predictable availability item having at least a threshold measure of fluctuating availability throughout the day at the retailer location based on data associated with a corresponding item. The system identifies a set of predictable availability items based on the predicted likelihood(s) and predicts an availability of each identified predictable availability item at the retailer location during a future timeframe. The system then updates the ordering interface to describe the predicted availability of each predictable availability item at the retailer location during the future timeframe.
An online concierge system receives two types of orders, one of which requires fulfillment in a specific time interval, while the other can be fulfilled anytime up to a specific time interval. A machine learning model, trained on historical data about available shoppers in discrete time intervals, is used to predict how many shoppers will be available to fulfill orders in each time interval. For each time interval, the system retrieves the relevant orders of both types and creates candidate groups including orders of both types. For each group, the system determines a fulfillment cost based on items in the orders. The candidate group with the lowest cost is selected, and the orders in the selected group are sent to devices of available shoppers in that interval, prompting the shoppers to view and fulfill the orders.
Systems and methods for a contract-based offer generator is provided. The method includes receiving data describing an offer on a product at an offer generation system and extracting relevant details using a machine learning language model. A plurality of test offers stored in an offer bank and transaction logs associated with the product are accessed. Each test offer is scored using a reinforcement learning model trained on past transaction data to predict the likelihood of achieving an offer objective. The forecast score is adjusted based on differences between the extracted offer and test offers. A subset of test offers is selected to maximize orthogonality of product-related variables and transmitted to client devices. User responses to the test offers are collected and stored, capturing engagement and purchase behaviors. The reinforcement learning model is then retrained based on these responses, enabling continuous improvement in offer selection and effectiveness.
A system generates a set of embeddings for known treatments by applying a machine-learned embedding model to descriptions of the known treatments, where these embeddings form a vector space. The system generates an embedding for a new treatment and mapping it within the vector space, and identifies one or more known treatments with embeddings that exceed a similarity threshold with the new treatment embedding. The system accesses performance data for the selected known treatments to assess user response, and identifies a subset of users for the new treatment based on this performance data. The system also creates a content item that incorporates the new treatment, and transmits instructions to client devices of the targeted users to cause the client devices to display the content item.
An online system may receive a registration of an application for a language model gateway configured as an intermediary between users and a first machine-learned language model. The online system may monitor a conversation associated with the application using the language model gateway. The conversation is between a user of the application and the first machine-learned language model and includes a prompt from the user directed toward the first machine-learned language model. The online system may extract the prompt and compile an input for a second machine-learned language model that is fine-tuned to improve prompts. The input may be the prompt and one or more criteria to improve the prompt. The online system may provide the input to the second machine-learned language model. The online system may determine a suggested improvement to the prompt using the second machine-learned language model and provide the suggested improvement to the user.
An online concierge system detects acquired items included among an inventory of a customer and identifies one or more candidate available items from the acquired items based on a predicted perishability of each item and a predicted amount of each item that was used. The system retrieves recipes, matches the item(s) likely to be available to a set of recipes based on their ingredients, and identifies any remaining items for each matched recipe not likely to be available. The system retrieves a set of attributes associated with the customer and the set of recipes and computes a suggestion score for each recipe based on the attributes. The system ranks the recipes based on their scores, identifies one or more recipes for suggesting to the customer based on the ranking, and sends the recipe(s) and any remaining items for each recipe to a client device associated with the customer.
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
G06K 7/10 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation
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
G06Q 30/0202 - Market predictions or forecasting for commercial activities
An online system receives orders including items from customers and allocates the orders to pickers. A picker obtains items included in an order from a customer and delivers the items to the customer to fulfill the order. To provide encouragement for pickers fulfilling orders, the online system generates a highlight reel of accomplishments of a picker fulfilling orders. The online system generates prompts for a generative model, such as a large language model, based on stored information describing order fulfillment by a picker. Content, such as text, generated in response to a prompt by the generative model includes one or more portions of the stored information describing order fulfillment is included in the highlight reel. A template for the highlight reel is selected for the picker, with the template identifying content displayed to the picker and an order in which the content is displayed.
An online concierge system selects picker expertise tags that showcase abilities or experiences of pickers that fulfill orders for the system. The online concierge system establishes a set of user-order cohorts based on characteristics of orders and users placing the orders. When an order is received, the online concierge system identifies a relevant user-order cohort and applies a trained model to predict, in the context of the user-order cohort, the performance of various candidate picker expertise tags applicable to the order. The trained model may be generated via a training and testing process in which different picker expertise tags are tested in the context of a user-order cohort, and performance metrics are observed to learn which picker expertise tags perform best in the context of a user-order cohort.
An online concierge system may update, responsive to a request from a user client device, a shopping list with a food item. The system may query a recipe database based in part on the food item to obtain one or more recipes that use the food item as a key ingredient, where the key ingredients in a recipe are tagged in the recipe database. The key ingredients for each of the corresponding recipes are identified using a machine learned model. The system ranks the one or more recipes based on one or more ranking criteria, such as a number of key ingredients of the recipe that are present in the shopping list. The system may provide the one or more ranked recipes to the user client device for presentation.
An online system displays an ordering interface and, responsive to receiving a request from a client device associated with a user to place an order, retrieves information describing a set of unused credits provided to the user by each of one or more programs. The system identifies a set of the program(s), wherein the set of unused credits provided by each identified program is eligible to be used for acquiring an item in the order. The system accesses and applies a machine-learning model to predict an expiration of the set of unused credits provided to the user by each identified program based on the retrieved information and a current time. The system ranks the set of programs based on the prediction(s), determines a default allocation of a subset of each set of unused credits to the order based on the ranking, and updates the interface to include the default allocation.
An online concierge system receives multiple images of an item from a first client device associated with a shopper associated with the online concierge system, in which each of the images of the item is captured from a different angle and/or position and the item is included among an inventory of a warehouse associated with a retailer associated with the online concierge system. Based in part on the images of the item, the online concierge system generates a three-dimensional image of the item, in which the three-dimensional image of the item includes a dimension of the item and/or a color of the item. The online concierge system then sends the three-dimensional image of the item to a second client device associated with a customer of the online concierge system, in which a perspective of the three-dimensional image is modifiable within a display area of the second client device.
An online system uses a machine-learned language model (e.g., an LLM) to improve multilingual search capabilities. The system generates a prompt for the LLM that includes a set of search queries in a first language along with their context, as well as a request for translating these queries into a second language. This prompt is sent to a model serving system, which executes it through the LLM and returns translated queries in the second language. Additionally, the concierge system accesses a first set of features derived from the search results in the first language, and updates these features based on the newly translated search queries to create a second set of features. These translated queries and the second set of features are then used to train a search model optimized for queries in the second language.
An online concierge system identifies churn of a customer, which occurs when the customer does not perform a specific action within a threshold time period. The online concierge system determines an event causing churn of the customer based on characteristics of the customer and attributes describing prior fulfillment of an order for the customer. To mitigate different events causing churn, the online concierge system maps areas of expertise of pickers for different aspects of order fulfillment to corresponding events. Through a trained picker scoring model, the online concierge system determines picker scores for different pickers fulfilling an order for a customer using characteristics of pickers, including an expertise, characteristics of the customer, and an event causing churn of the customer. Based on the picker scores, the online concierge system selects a specific picker for fulfilling a subsequent order from the customer.
An online system receives a user request from a client device through the interface, identifies one or more featured products based on the query, and generates a prompt for input to a machine-learned generative language model. The prompt specifies both the user's request and a request to suggest the featured products in association with a response to the user request. This prompt is fed into a machine-learned language model via a model serving system for execution. The online system receives a response generated by the model, generates a query response based on the response generated by the model, and transmits instructions to the client device to display the query response. The online system collects data on user interactions with the uses the collected data to fine-tune the machine-learned generative language model.
A trained computer model to identify a list of representative previously purchased items for recommendation to a user of an online system. The online system clusters, based on a similarity score for each pair of items, a set of previously purchased items into multiple clusters. The online system accesses a computer model trained to predict a likelihood of engagement by the user for each item in each cluster, and applies the computer model to predict, based on one or more features of each item, the likelihood of engagement for each item in each cluster. The online system generates, based on the likelihood of engagement, a score for each item in each cluster. The online system selects, based on the score for each item, a representative item from each cluster. The online system causes a device associated with the user to display the representative item from each cluster.
A system generates item images using an item image generation model. The system receives a prompt for the model. The prompt is configured to request the model generate item images for an item. The system executes the model using the prompt to generate a set of item images. The system evaluates each of the set of item images to determine performance data of each of the set of item images. The system iteratively improves the set of item images by performing the following steps. The system updates the prompt based on the performance data of each of the set of item images to obtain a new prompt. The system executes, using the new prompt, the model to generate a new set of item images, and the system evaluates the new set of item images to determine performance data of each of the new set of item images.
A language model is used to generate autosuggestions to complete or revise a user's partial search query. An initial partial query is applied to the language model to generate query candidates for completing the search query. The language model may generate the query candidates as additional or alternate tokens for the partial search query. When the user revises the partial query, the previously-generated candidates can be re-used to reduce subsequent processing time for generating additional candidates. The previously-generated candidates are compared with the revised partial query to select which of the candidates to be re-used and expanded for generating additional tokens. Additional tokens can be generated in parallel for the previously-generated candidates or with model values from the previous generation, enabling the tokens to be generated effectively with reduced latency consistent with user expectations for search-related autosuggestions.
An online system updates the labels on negative examples to account for the possibility that the example is a false negative. The system generates a set of initial training examples that each include a query input by the user and item data for an item presented as a result to the user's query. Each training example also includes an initial label, which represents whether the user interacted with the item presented as a search result. The online system updates the initial label for a negative training example by identifying a set of bridge queries and computing a similarity score between the query for the training example and the bridge queries. The online system computes an updated label for the negative example based on the similarity scores and updates the training example with the updated label.
G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
84.
DATABASE SEARCH BASED ON MACHINE LEARNING BASED LANGUAGE MODELS
An online system receives information describing a set of items requested by a user and an indication via a chat interface that a particular item needs replacement. The online system generates one or more prompts configured to request a machine learned language model to identify the particular item that needs replacement and to identify one or more replacement items for the particular item. The online system receives a set of item identifiers from the machine learned language model and selects a replacement item from a database based on the set of item identifiers. The online system may also receive an order and a communication history associated with a user including a message with a request to modify the a. The online uses the machine-learning language model to map the request type to the set of API requests for updating the order to reflect the request from the user.
An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.
A warehouse from which shoppers fulfill orders for an online concierge system maintains an online concierge system-specific portion for which the online concierge system specifies placement of items in regions. To place items in the online concierge system-specific portion, the online concierge system accounts for co-occurrences of different items in orders and measures of similarity between different items. From the co-occurrences of items, the online concierge system generates an affinity graph. The online concierge system also generates a colocation graph based on distances between different regions in the online concierge system-specific portion. Using an optimization function with the affinity graph and the colocation graph, the online concierge system selects regions within the online concierge system-specific portion for different items to minimize an amount of time for shoppers to obtain items in the online concierge-system specific portion.
A trained computer model for automatic identification of a wrong delivery location for an order placed at an online system. The online system receives, via a user interface, a user input that includes a delivery location for the order. The online system compares the received delivery location with a stored delivery location for the user. Responsive to identifying that the received and stored delivery locations are different, the online system accesses and applies a computer model to predict, based on features of the order, a likelihood of the received delivery location being correct. The online system generates, based on the predicted likelihood, a confidence score of the received delivery location being correct. Responsive to the confidence score being below a threshold score, the online system causes a device of the user to display a user interface with a message prompting the user to verify accuracy of the received delivery location.
Administration of a customer loyalty program which provides discounts on products and services; Advertising, marketing, and promoting the goods and services of others via a global computer network, namely, advertising and promoting the availability of goods for selection, ordering, purchase, and delivery; Comparison shopping services; Providing advertising and advertisement services; On-line ordering services featuring consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; On-line wholesale and retail store services featuring a wide variety of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise featuring delivery to home, office, and other designated locations; Event planning and management for marketing, branding, promoting or advertising the goods and services of others
09 - Scientific and electric apparatus and instruments
Goods & Services
(Based on 44(d) Priority Application)(Based on Intent to Use) Downloadable computer e-commerce software to allow users to perform electronic business transactions via a global computer network; Downloadable software in the nature of a mobile application for commerce, namely, software that allows users to perform electronic business transactions via a global computer network; Downloadable software in the nature of a mobile application for allowing users to execute shopping-related demands in the nature of building a shopping list, placing an order for groceries, food, snacks, beverages, and alcoholic beverages online, and selecting recipes according to groceries purchased; Downloadable software for browsing and purchasing consumer goods of others; Downloadable software for engaging and coordinating personal shopper and delivery services; Downloadable software for providing information on available same-day transportation and delivery services; Downloadable software for searching for and accessing, creating, publishing and browsing information in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Downloadable software for shopping in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Downloadable software for event planning (Based on Intent to Use) Downloadable software in the nature of a mobile application for splitting and reimbursing costs and expenses
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for enabling self-service electronic checkout and purchasing of goods by users; Providing a web site featuring technology that enables users to search, browse, and purchase a wide variety of consumer goods of others; Software as a service (SAAS) services featuring software for designing, developing, hosting, implementing and maintain web sites for others that enable, collect data with respect to, and process the selection, ordering, billing, delivering, and advertising of consumer goods and services, and for use in advising retailers regarding the use of such websites by others in the field of retail, ordering, and delivery services featuring consumer goods; Providing temporary use of on-line non-downloadable software for users to perform electronic business transactions via global computer network; Providing temporary use of on-line non-downloadable software for analyzing consumer behavioral data; Providing temporary use of on-line non-downloadable software for analyzing retail store customer data; Providing a website featuring non-downloadable software using artificial intelligence (AI) for enabling autonomous checkout; Providing temporary use of on-line non-downloadable software for use in acquiring shopping information and price comparisons that may be downloaded from a global computer network; Providing temporary use of on-line non-downloadable software for use in advertising products and brands via electronic displays throughout retail stores; Providing online non-downloadable computer software platforms for use in database management, sales, customer tracking and management, and inventory management for the e-commerce, wholesale and retail industries; Providing temporary use of on-line non-downloadable software for use in inventory tracking and data analytics in the field of retail stores; Providing temporary use of on-line non-downloadable software for providing recommendations of items for purchase based on shopper preferences; Providing temporary use of on-line non-downloadable software for providing dynamic pricing of items throughout a retail store; Providing temporary use of on-line non-downloadable software for browsing, comparing, and purchasing a wide variety of consumer goods of others; Providing temporary use of on-line non-downloadable software for facilitating, coordinating and scheduling delivery in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Providing temporary use of on-line non-downloadable software for engaging and coordinating personal shopper and delivery services; Providing temporary use of on-line non-downloadable software for ordering delivery services; Providing temporary use of on-line non-downloadable software for providing information on available same-day transportation and delivery services; Providing temporary use of on-line non-downloadable software for event planning; Providing temporary use of on-line non-downloadable software for splitting and reimbursing costs and expenses
Administration of a customer loyalty program which provides discounts on products and services; Advertising, marketing, and promoting the goods and services of others via a global computer network, namely, advertising and promoting the availability of goods for selection, ordering, purchase, and delivery; Comparison shopping services; Providing advertising and advertisement services; On-line ordering services featuring consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; On-line wholesale and retail store services featuring a wide variety of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise featuring delivery to home, office, and other designated locations; Event planning and management for marketing, branding, promoting or advertising the goods and services of others
09 - Scientific and electric apparatus and instruments
Goods & Services
Downloadable computer e-commerce software to allow users to perform electronic business transactions via a global computer network; Downloadable software in the nature of a mobile application for commerce, namely, software that allows users to perform electronic business transactions via a global computer network; Downloadable software in the nature of a mobile application for allowing users to execute shopping-related demands in the nature of building a shopping list, placing an order for groceries, food, snacks, beverages, and alcoholic beverages online, and selecting recipes according to groceries purchased; Downloadable software for browsing and purchasing consumer goods of others; Downloadable software for engaging and coordinating personal shopper and delivery services; Downloadable software for providing information on available same-day transportation and delivery services; Downloadable software for searching for and accessing, creating, publishing and browsing information in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Downloadable software for shopping in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Downloadable software for event planning; Downloadable software in the nature of a mobile application for splitting and reimbursing costs and expenses
45 - Legal and security services; personal services for individuals.
Goods & Services
Personal shopping for others; Personal shopping in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise for others
42 - Scientific, technological and industrial services, research and design
Goods & Services
(Based on 44(d) Priority Application)(Based on Intent to Use) Providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for enabling self-service electronic checkout and purchasing of goods by users; Providing a web site featuring technology that enables users to search, browse, and purchase a wide variety of consumer goods of others; Software as a service (SAAS) services featuring software for designing, developing, hosting, implementing and maintain web sites for others that enable, collect data with respect to, and process the selection, ordering, billing, delivering, and advertising of consumer goods and services, and for use in advising retailers regarding the use of such websites by others in the field of retail, ordering, and delivery services featuring consumer goods; Providing temporary use of on-line non-downloadable software for users to perform electronic business transactions via global computer network; Providing temporary use of on-line non-downloadable software for analyzing consumer behavioral data; Providing temporary use of on-line non-downloadable software for analyzing retail store customer data; Providing a website featuring non-downloadable software using artificial intelligence (AI) for enabling autonomous checkout; Providing temporary use of on-line non-downloadable software for use in acquiring shopping information and price comparisons that may be downloaded from a global computer network; Providing temporary use of on-line non-downloadable software for use in advertising products and brands via electronic displays throughout retail stores; Providing online non-downloadable computer software platforms for use in database management, sales, customer tracking and management, and inventory management for the e-commerce, wholesale and retail industries; Providing temporary use of on-line non-downloadable software for use in inventory tracking and data analytics in the field of retail stores; Providing temporary use of on-line non-downloadable software for providing recommendations of items for purchase based on shopper preferences; Providing temporary use of on-line non-downloadable software for providing dynamic pricing of items throughout a retail store; Providing temporary use of on-line non-downloadable software for browsing, comparing, and purchasing a wide variety of consumer goods of others; Providing temporary use of on-line non-downloadable software for facilitating, coordinating and scheduling delivery in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise; Providing temporary use of on-line non-downloadable software for engaging and coordinating personal shopper and delivery services; Providing temporary use of on-line non-downloadable software for ordering delivery services; Providing temporary use of on-line non-downloadable software for providing information on available same-day transportation and delivery services; Providing temporary use of on-line non-downloadable software for event planning (Based on Intent to Use) Providing temporary use of on-line non-downloadable software for splitting and reimbursing costs and expenses
45 - Legal and security services; personal services for individuals.
Goods & Services
Personal shopping for others; Personal shopping in the field of consumer goods, groceries, food, snacks, beverages, alcoholic beverages, and general merchandise for others
100.
GENERATING DIVERSE DATASETS USING MACHINE-LEARNED LARGE LANGUAGE MODELS (LLMS) BASED ON VECTOR DISTANCE CONSTRAINTS
An online system augments a dataset in conjunction with a model serving system. The online system accesses a dataset for training a machine-learning model. The online system generates a prompt to generate candidate samples in the training dataset to the model serving system. The online system receives a response comprising one or more candidate samples. The online system compares the one or more candidate samples to at least one existing sample of the dataset to determine whether the one or more candidate samples are within a threshold level of similarity to an existing sample. If a candidate sample received from the machine-learning language model is not within the threshold level of similarity to an existing sample, the online system updates the dataset with the candidate sample.
