AI Picking Agent Combining ML and RL for Continuous Guidance

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Solution Overview

Problem

Conventional online systems struggle to provide continuous assistance to entities completing tasks due to the limitations of discrete machine learning models, which are inefficient in handling diverse situations and require excessive resources.

Innovation Solution

An AI agent equipped with a machine learning model and reinforcement learning model monitors events and generates tailored recommendations for entities, optimizing their actions to enhance task completion efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple discrete machine learning models are used to assist entities in different situations, then the system can handle diverse scenarios, but the resource consumption (processing power and memory) increases significantly

Engineering Contradiction:

Improveability to handle diverse situationsVSAvoidprocessing power and memory

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent combines multiple discrete machine learning models into a single unified AI agent that can handle diverse situations. The AI agent integrates the functionality of what would otherwise require separate models, allowing it to assist entities through entire task durations without requiring multiple distinct models to be loaded and executed simultaneously.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The AI agent is designed as a universal system that can perform multiple functions across different situations and task types. Rather than creating specialized models for each scenario, the single AI agent adapts to handle various events and provide appropriate assistance, reducing the overall computational overhead while maintaining versatility.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Reliability

If discrete machine learning models are used for specific situations, then assistance can be provided for particular tasks, but the system cannot assist entities throughout the entire task duration when multiple different situations arise

Engineering Contradiction:

Improvecontinuous assistance capabilityVSAvoidsystem architecture

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent merges multiple situation-specific models into a single AI agent that operates continuously throughout the task duration. This unified approach ensures reliable assistance across the entire workflow without requiring complex coordination between multiple discrete models.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The AI agent employs dynamic prompting and reinforcement learning to adapt its behavior in real-time based on the current situation and task progress. This dynamic capability allows the single agent to effectively handle diverse scenarios that would otherwise require multiple static models, maintaining reliability throughout the entire task duration.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260080355A1Artificial Intelligence Agent Using a Machine-Learning Model and Reinforcement Learning Model to Guide Picking Process

Publication Date: 2026.03.19 MAPLEBEAR INC

AI summary

An artificial intelligence (AI) agent is disclosed that assists an entity to complete a task. The entity is assigned to complete a task. The AI agent monitors events to detect an occurrence of an event associated with the task. A machine learning model of the AI agent is prompted to generate a set of candidate actions based in part on the detected event and data about the entity. A reinforcement learning model of the AI agent scores each candidate action from the set to tailor the candidate actions to the entity. A scored action is selected as a recommended response to the event and is communicated to a client device of the entity which causes the entity to perform the selected action.