Mark Oberemk

DETERMINING PURCHASE SUGGESTIONS FOR AN ONLINE SHOPPING CONCIERGE PLATFORM

AUGMENTED CONTENT GENERATION WITH LANGUAGE MODEL FOR ASSISTING OPERATION OF SMART CART

MESSAGING ONLINE SYSTEM USERS IN RESPONSE TO PREDICTED LIKELIHOODS OF ORDER PICKUP USING MACHINE LEARNING MODEL

CART WITH PHYSICAL SENSOR TO DETECT ITEM REMOVAL AND GENERATE USER INTERFACE WITH ALTERNATIVE OPTION

GENERATING AN ITEM SELECTION SEQUENCE USING A MACHINE LEARNING MODEL FOR IDENTIFYING FOUNDATIONAL ITEMS

DEVICE ERROR PRIORITY ASSIGNMENT GENERATION FOR SMART CART SYSTEMS

USING TRAINED MACHINE-LEARNING MODEL TO DETECT ERRORS BASED ON INTERACTIONS OF USERS OF AN ONLINE SYSTEM WITH PHYSICAL DEVICES

Using Trained Machine-Learning Model to Generate User Interface Prompting User of an Online System to Use Different Conversion Channel

Using machine-learning model of an online system to facilitate performing tasks of new types

USING A MACHINE LEARNING MODEL TO PREDICT A USER'S QUANTITY CEILING FOR DIFFERENT CATEGORIES OF ITEMS IN A CATALOG

Computer-Enabled Cart System Leveraging Machine Learning Models for Content Selection Based on Sensor Data Describing User Interactions

USING A GENERATIVE MACHINE-LEARNING MODEL TO GENERATE A USER INTERFACE WITH VISUALIZATION OF ITEMS OF SELECTED QUANTITIES

Using a Trained Machine-Learning Model for Efficient Packing of Items

SELECTIVELY DISPLAYING VIDEOS BY AN ONLINE SYSTEM

Using a Trained Machine-Learning Model of an Online System to Handle Unclaimed Online Pickup Orders

DISPLAYING A RECIPE PREPARATION SUGGESTION IN AN AUGMENTED REALITY ELEMENT BASED ON A PREDICTED RECIPE BEING PREPARED

User Interface for Selecting Sources for an Online Concierge System with a Quick-Add Option for an Item-Source Pair

RECOMMENDING CONTENT BASED ON A PREDICTED EXPLORATION SCORE FOR AN ONLINE SYSTEM USER

Clustering database items based on output of machine-learning model to link database items that represent the same core item but with a different stored size or form attribute

Machine Learning Prediction of User Type for Generating Personalized User Interface for an Online System

AUTOMATED IDENTIFICATION OF ITEMS PLACED IN A CART AND RECOMMENDATIONS BASED ON SAME

AUTOMATED IDENTIFICATION OF ITEMS PLACED IN A CART AND ROUTING BASED ON SAME

Using a Trained Model of an Online System to Generate Action Recommendations by Predicting Future Demand

AUTOMATIC ROUTING OF USER INQUIRIES USING MACHINE-LEARNING MODELS

Matching Images of Current Inventory with Machine Learning Predictions of User Preferences to Customize User Interface

USING A GENERATIVE ARTIFICIAL INTELLIGENCE MODEL TO GENERATE AN IMAGE OF AN ITEM INCLUDED IN AN ORDER ACCORDING TO A PREDICTED USER PREFERENCE ASSOCIATED WITH THE ITEM

USING A GENERATIVE ARTIFICIAL INTELLIGENCE MODEL TO GENERATE A PERSONALIZED IMAGE OF A CATALOG ITEM

DYNAMIC DETERMINATION OF EVENT TYPE USING MULTITASK CLASSIFICATION MODEL FOR INCREASING EFFICIENCY OF ITEM PIPELINE

MACHINE LEARNING MODEL FOR PREDICTING SCHEDULED DELIVERY FOR A SELF-PICKED ORDER

Triggering Expert Assistance for Item Collection Based on User Preference and Fulfillment Expertise Predicted by a Machine-Learning Model

GENERATING RECOMMENDATIONS FOR ADJUSTMENT ACTIONS USING A TRAINED MODEL OF AN ONLINE SYSTEM

TRAINED COMPUTER MODEL FOR GENERATING AGGREGATED HEALTH SCORE FOR A BUSINESS USER OF AN ONLINE SYSTEM

Route Selection for Obtaining Items in a Warehouse

Routing Based on Cross-Order Image Recognition

GENERATING REPLACEMENTS FOR A MEAL PLAN USING A MACHINE-LEARNING MODEL

DETERMINING PURCHASE SUGGESTIONS FOR AN ONLINE SHOPPING CONCIERGE PLATFORM

COMPUTING ITEM FINDABILITY THROUGH A FINDABILITY MACHINE-LEARNING MODEL

USING GENERATIVE ARTIFICIAL INTELLIGENCE (AI) FOR AUTOMATED DIGITAL FLYER CONTENT GENERATION

EARLY INTERCEPTION AND CORRECTION IN ONLINE CONVERSATIONS USING LARGE LANGUAGE MACHINE-LEARNED MODELS

Replacing Online Conversations Using Large Language Machine-Learned Models

DETERMINING ITEM DESIRABILITY TO USERS BASED ON ITEM ATTRIBUTES AND ITEM EXPIRATION DATE

AUTOMATIC ROUTING OF USER INQUIRIES USING NATURAL LANGUAGE AND IMAGE RECOGNITION MODELS