Model-Based Favorite Order Forecasting Interface for Agent Scheduling

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Solution Overview

Problem

The challenge in online systems is matching the availability of fulfillment agents with the order timing of requesting users, particularly when users favor specific agents for their orders.

Innovation Solution

Implementing a trained prediction model to forecast the likelihood of fulfillment agents receiving favorite orders in discretized time slots, using historical data and contextual information, and providing a scheduling interface with visual indications to optimize agent availability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If fulfillment agents are manually scheduled without prediction models, then scheduling simplicity is maintained, but order fulfillment timing reliability deteriorates

Engineering Contradiction:

Improveorder fulfillment timing reliabilityVSAvoidscheduling system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by training prediction models on historical data before actual scheduling occurs. The models predict future order patterns and agent availability in advance, enabling proactive scheduling decisions that improve fulfillment timing reliability without requiring complex real-time computation during scheduling moments.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The prediction model acts as an intermediary between historical data and scheduling decisions. It processes past order patterns, agent performance data, and contextual information to generate forecasts that guide scheduling algorithms, thereby improving reliability while keeping the overall system architecture manageable through a clear data-processing pipeline.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If detailed prediction models are trained on extensive historical data, then scheduling accuracy improves, but data processing time and computational resources increase

Engineering Contradiction:

Improvescheduling accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system segments the data processing workflow into distinct phases: historical data collection, model training, and real-time prediction generation. By separating these phases, the system can pre-process and store historical patterns during off-peak times, then quickly generate predictions during scheduling operations, improving accuracy while reducing real-time computational burden.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Extensive data processing and model training are performed as preliminary actions during off-peak hours or in batch modes. The system pre-computes patterns from historical data and stores them for quick retrieval during actual scheduling operations, thereby achieving high measurement precision without sacrificing real-time responsiveness.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If fulfillment agents are assigned to favorite orders based solely on user preferences, then user satisfaction improves, but agent workload balance deteriorates

Engineering Contradiction:

Improveuser preference fulfillmentVSAvoidagent workload balance

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system changes the parameters used for agent assignment by incorporating multiple factors beyond simple user preferences. It adjusts assignment decisions based on predicted order patterns, agent availability, historical performance metrics, and workload distribution, thereby maintaining user satisfaction while achieving better workload balance across the agent team.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The assignment system operates dynamically by continuously adjusting agent assignments based on real-time and historical data. It adapts to changing patterns in user preferences and agent availability, allowing the system to balance workload while still honoring favorite order requests through intelligent, data-driven decision-making rather than static rules.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250315786A1User interface for displaying model-based prediction of order features

Publication Date: 2025.10.09 MAPLEBEAR INC

AI summary

An online system manages the availability schedules of fulfillment agents utilizing a favorite order prediction model to predict likelihood of receiving a favorite order. The system receives a request from a fulfillment agent to set an availability schedule for a forthcoming time period. The system applies a prediction model to each of a plurality of discretized time slots of the time period to predict the favorite order likelihood. The model may be trained by the system: retrieving a profile for the fulfillment agent comprising a list of requesting user(s) that have favorited the fulfillment agent, and training the model based on order histories of the list of requesting user(s). The system generates and provides an interface displaying the time slots with a visual indication for each time slot based on its predicted likelihood, e.g., a heat map of likelihoods of receiving a favorite order across the time slots.