Picker Highlight Reel Generation With Templates and LLM Prompts

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Solution Overview

Problem

Conventional online concierge systems fail to provide adequate recognition to pickers for their order fulfillment, leading to decreased interaction and productivity, and generating personalized content for pickers becomes impractical due to increased data and computational resource demands.

Innovation Solution

An online concierge system uses a generative model to create personalized highlight reels for pickers, selecting templates based on picker characteristics and generating content efficiently using prompts, while omitting non-significant data to maintain engagement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If personalized content is manually generated for each picker to highlight their achievements, then picker engagement and motivation improve, but the time and computational resources required increase significantly

Engineering Contradiction:

ImprovePicker engagementVSAvoidContent generation time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system enables pickers to automatically generate their own highlight reels by providing them with access to their fulfillment data and template selection capabilities, eliminating the need for manual content creation while maintaining personalization and motivational value

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses template-based copying where pre-designed highlight reel templates are automatically populated with each picker's specific fulfillment data, allowing rapid generation of personalized content without manual creation effort while maintaining consistency and quality

Inventive Principle:

Principle #26Copying

2Adaptability or versatility

If comprehensive picker fulfillment data is collected and processed to create personalized content, then content personalization quality improves, but computational resource consumption increases

Engineering Contradiction:

ImproveContent personalizationVSAvoidComputational resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system extracts only the most relevant fulfillment metrics and achievements from the comprehensive picker data using predefined criteria and thresholds, processing only essential information rather than analyzing entire datasets, thereby reducing computational load while maintaining personalization quality

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system transforms raw fulfillment data into standardized achievement parameters and categories that can be efficiently processed and matched against templates, changing the data representation to optimize computational efficiency while preserving the information needed for personalization

Inventive Principle:

Principle #35Parameter changes

3Reliability

If detailed order fulfillment information is displayed to pickers, then picker recognition and motivation improve, but data processing complexity and resource usage increase

Engineering Contradiction:

ImprovePicker recognitionVSAvoidData processing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments comprehensive fulfillment data into distinct achievement categories (e.g., fastest delivery, highest customer satisfaction, most orders fulfilled) and displays them as separate, organized highlight reel sections, making the information more manageable and easier to process while enhancing picker recognition

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system selectively displays only the most significant achievements and omits minor or redundant fulfillment details, applying partial action by focusing on key highlights rather than processing and displaying all available data, thereby reducing complexity while maintaining recognition effectiveness

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12475326B2Generating content highlighting work experience using a generative model

Publication Date: 2025.11.18 MAPLEBEAR INC

AI summary

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.