Adaptive Advertising Scheduling via Dynamic Cost Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current scheduling systems for advertising spots in various media are inefficient and ineffective due to their inability to consider multiple parameters, handle complex scheduling demands, and adapt to changing conditions, leading to inefficiencies and high costs.
Innovation Solution
An adaptive scheduling system that uses a ranking algorithm to select optimal commercial break locations based on user-defined fixed and relative factors, allowing for dynamic assignment of spots across multiple networks and channels, and includes a microprocessor-based system for determining costs and prioritizing spot placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current scheduling systems use only a few inputs (rate and contract line priority) for determining spot scheduling, then the system operation is simple, but the scheduling effectiveness and adaptability are insufficient
Solution Approach 1:
The patent transforms the scheduling approach by changing from fixed, static parameters to dynamic, multi-factor parameters. The system now considers multiple inputs including rate, contract line priority, and numerous additional factors (program type, audience demographics, spot position, network characteristics) that can be adjusted and weighted dynamically to adapt to different scheduling scenarios and changing conditions.
Solution Approach 2:
The scheduling system transitions from static priority assignments to dynamic priority calculations. Priorities are no longer set once and remain fixed, but are continuously recalculated based on current conditions, available breaks, and multiple competing factors. This allows the system to adapt automatically to changing circumstances without manual intervention.
2Ease of operation
If current scheduling systems use static priorities that require large amounts of time and effort to change, then the system structure is simple, but the responsiveness to changing conditions is poor
Solution Approach 1:
The scheduling system performs automatic priority recalculation and schedule optimization without requiring manual intervention. When conditions change or when higher-priority spots need to be inserted, the system autonomously evaluates all factors, recalculates priorities, and rearranges the schedule to accommodate changes, eliminating the need for operators to manually adjust priorities and reducing both time and effort required.
3Productivity
If current scheduling systems manually rearrange schedules to accommodate higher-priority spots, then the system logic is simple, but the scheduling efficiency and cost are poor
Solution Approach 1:
The patent replaces manual mechanical scheduling operations with an automated computational system. Instead of operators manually reviewing and rearranging schedules, the system uses algorithms to automatically evaluate multiple factors, calculate optimal placements, and generate revised schedules, dramatically improving efficiency and reducing the time required for schedule adjustments.
Solution Approach 2:
The system continuously monitors scheduling conditions and automatically adjusts priorities and placements based on feedback from multiple factors. When a higher-priority spot needs to be inserted, the system evaluates the impact on existing schedules, recalculates optimal positions for all spots, and implements adjustments automatically, creating a responsive feedback loop that maintains scheduling optimality without manual intervention.
4Quantity of substance
If current scheduling systems cannot handle multiple networks and channels efficiently, then the system complexity is low, but the capability to meet burgeoning advertising demand is insufficient
Solution Approach 1:
The scheduling system is designed to handle multiple networks, channels, and diverse advertising spots simultaneously through a unified multi-factor evaluation framework. The same core algorithm and factor-weighting mechanism apply across all networks and channels, allowing the system to scale to handle increasing quantities of spots and networks without requiring separate specialized systems for each.
Data Source
AI summary
The disclosed embodiments describe an automatic, adaptive system and method for efficiently and effectively scheduling advertising spots in commercial break locations across various networks, zones, channels, dates, times, and specific products, for example. The disclosed embodiments make use of fixed and relative factors, that may be user-defined, which assign a “cost” to one or more particular breaks which thereby allow for quick and accurate scheduling of spots. The “costs” may represent a value, or desirability, of a break for the advertiser and may be a function of both the fixed and relative factors. The fixed and relative factors may be configurable and may change for different advertising clients, different contract lines, different networks, different spot placement, etc.The placement of spots may be accomplished through the use of an ordered list which may be generated based on a number of inputs that may be user-selected. A non-limiting example of user inputs may include: spot length, spot cost, contract line priority, beginning date/time of contract line, ending date/time of contract line, a predefined value index for the client, and contract line number, among others.