Bid Test Component for Automated Content Auction Optimization

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Solution Overview

Problem

Entities face inefficiencies and resource wastage in content distribution due to inadequate advice from content recommendation auction engines, leading to missed opportunities in displaying relevant content to users, resulting in time and computing resource wastage.

Innovation Solution

A bid optimization method involving a bid test component that automatically manages and adjusts bidding parameters, including generating test content schemes, adjusting budgets, and ranking bid levels to identify optimal performing parameters, thereby eliminating manual guesswork and errors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual bidding parameter adjustment is used, then entities can control their budget and bidding strategy, but it leads to time consumption, computing resource wastage, and error-prone operations

Engineering Contradiction:

Improvemanual bidding managementVSAvoidtime for monitoring and adjusting bidding

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system enables self-service by automatically managing bidding parameters through the bid test component. The component autonomously generates test content schemes, adjusts budgets, and optimizes bid levels without requiring manual intervention, thereby eliminating the time and computational resources previously spent on manual bidding management while maintaining effective budget control

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual process of bidding parameter adjustment with an automated computational system. The bid test component uses algorithms to automatically generate, test, and optimize bidding strategies, substituting human manual operations with automated computational processes that reduce time consumption and eliminate human errors

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If inadequate bidding advice is provided by auction engines, then entities can participate in content recommendation opportunities, but it results in missed opportunities and resource wastage

Engineering Contradiction:

Improvecontent distribution effectivenessVSAvoidcomputing resources wasted on ineffective bids

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The bid test component implements feedback mechanisms by continuously monitoring the performance of different bid levels and content schemes. It uses this feedback to automatically adjust bidding strategies, ensuring that computing resources are allocated to the most effective bids and preventing waste on ineffective advertising opportunities

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary actions by pre-testing different bid levels and content schemes through automated bid tests before full-scale implementation. This preliminary testing identifies optimal bidding parameters in advance, preventing resource wastage on ineffective bids and maximizing the effectiveness of content distribution efforts

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11430055B2Bid optimization

Publication Date: 2022.08.30 TOAST AI LLC

AI summary

One or more techniques and/or systems are provided for bid optimization. A bid test component is configured to receive a notification from a content recommendation auction engine that a user has requested that the bid test component test various bidding parameters for a content scheme used to bid on opportunities to show content to users. Accordingly, a set of test content schemes are created. Varying bid levels, budget allocations, and/or other bidding parameters are set for the test content schemes. The test content schemes are submitted to the content recommendation auction engine for bidding on opportunities to show content. Test content scheme statistics, regarding the performance of the test content schemes, are collected. Recommendations are providing and automated content scheme management is facilitated based upon the test content scheme statistics.