Naval Shah

Smart cart issue prediction with remedial workflow

Creating Personalized Online Orders Using a Voice Augmented Language Model

MULTIMODAL MACHINE-LEARNING MODEL FOR PREDICTING ITEM USAGE

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

CLUSTERING OF ITEMS FOR MULTI-SOURCE SERVICING OF AN AGGREGATED LIST OF ITEMS

USING MACHINE-LEARNING MODEL OF AN ONLINE SYSTEM TO GENERATE A SOURCE-RELATED CONFIDENCE SCORE FOR SERVICING A LIST OF COMPONENTS REQUESTED BY AN ONLINE PLATFORM

TRAINING MACHINE-LEARNING MODEL OF AN ONLINE SYSTEM TO DETERMINE A LEVEL OF MATCHING BETWEEN IDENTIFIERS OF ITEMS STORED IN A DATABASE OF ONLINE SYSTEM

Machine Learning-Based Ingredient Classification and Filtering System for Item Database

USING A LARGE LANGUAGE MODEL FOR ALTERNATIVE INGREDIENT DETERMINATION

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

Generation of a Meta-Catalog Using a Large Language Model

Artificial Intelligence Agent Using a Machine-Learning Model and Reinforcement Learning Model to Guide Picking Process

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

Predicting whether temperature-sensitive items will transition outside of a target temperature range during transport using a machine learning model

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

GENERATING A REGION- AND SOURCE-AGNOSTIC DATABASE OF ITEMS AVAILABLE IN MULTIPLE REGIONS

Natural Language Processing for Extracting Specific Items from a List of Ingredients

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

USING A LARGE LANGUAGE MODEL TO GENERATE CONTENT BASED ON IMAGES CAPTURED AT A SOURCE LOCATION

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

Using a Trained Machine-Learning Model to Facilitate Picking Items in a Warehouse

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

Personalized Recommendations Matching a List of Item Descriptors to Catalog Products from a Database

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

Machine-Learning Prediction of Nutritional Preferences for a User of an Online System

Using a Trained Model to Predict a User's Price Sensitivity Based on Data Acquired from In-Store Sensors

Ranking Search Results Based on User Intent for Recipe Ingredients

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 LEARNED MODEL FOR ITEM RECOMMENDATIONS FOLLOWING FAILED ATTEMPTS

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

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

USING A TRAINED MODEL TO GENERATE ACTION RECOMMENDATIONS BY PREDICTING METRICS RELATED TO ITEMS ORDERED AT AN ONLINE SYSTEM

Using a Trained Model to Predict a User's Price Sensitivity Based on Data Acquired from In-Store Sensors

Using a trained model to predict a user's price sensitivity based on data acquired from in-store sensors

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

USING A TRAINED MODEL OF AN ONLINE SYSTEM FOR POST-DELIVERY EFFORT-BASED TIP INCREASE RECOMMENDATION