Probabilistic Digital Component Extension Selection

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Solution Overview

Problem

Existing digital component distribution systems are limited in their ability to reformat digital components using extensions that would exceed the distribution parameter limit, preventing the provision of formats with higher user interaction rates and limiting user experience.

Innovation Solution

The implementation of probabilistic techniques to select digital component extensions for reformatting, determining probabilities based on distribution parameter limits and base selection requirements to ensure aggregate selection requirements remain within the allowed limit, allowing for the use of extensions that would otherwise exceed the limit at specified probabilities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If digital component extensions are selected to provide formats with higher user interaction rates, then user experience and interaction rates are improved, but the aggregate selection requirement exceeds the distribution parameter limit

Engineering Contradiction:

Improveuser interaction rateVSAvoidaggregate selection requirement

Core Design Contradiction:

Ease of operationVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts the selection probability of digital component extensions based on real-time conditions. By making the selection process probabilistic rather than deterministic, the system can adapt to varying distribution parameter limits and base selection requirements, allowing high-interaction formats to be selected more frequently when resources permit while maintaining compliance with distribution limits on average.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the parameter of selection probability for different digital component extensions. By adjusting the probability parameter, the system optimizes the balance between user interaction rates and distribution parameter limits, allowing flexible control over which extensions are selected without exceeding aggregate selection requirements.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If digital component extensions are used to reformat digital components, then formats with higher user interaction rates are provided, but the combined selection requirement exceeds the distribution parameter limit

Engineering Contradiction:

Improveformat varietyVSAvoidcombined selection requirement

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The system uses dynamic probabilistic selection to determine which extensions are applied to digital components. This allows the system to provide diverse formats adaptively, selecting extensions that maximize format variety while keeping the expected combined selection requirement within distribution parameter limits through probability-based control.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system adjusts the selection probability parameter for each extension based on the distribution parameter limit and base selection requirement. By changing this parameter, the system can control the expected resource consumption while maintaining adaptability in providing various digital component formats.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If extensions are selected without probabilistic constraints, then user experience is maximized, but distribution parameter limits are exceeded

Engineering Contradiction:

Improveuser experienceVSAvoiddistribution parameter compliance

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system incorporates feedback by continuously monitoring the relationship between selected extensions and distribution parameter limits. The probabilistic selection mechanism uses this feedback to adjust selection probabilities, ensuring that while user experience is maximized through diverse format provision, the expected aggregate selection requirement remains compliant with distribution parameter limits.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically balances user experience and compliance by adjusting selection probabilities in real-time. When distribution parameter headroom is available, higher-interaction extensions are selected more frequently to improve user experience. When limits are approached, selection probabilities are adjusted to maintain compliance, creating a dynamic equilibrium between these competing objectives.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11307859B2Probabilistic techniques for formatting digital components

Publication Date: 2022.04.19 GOOGLE LLC

AI summary

Methods, systems, and apparatus, including an apparatus for using probabilistic techniques to provide reformatted versions of digital components. In one aspect, a process includes obtaining data specifying a distribution parameter limit for a given reformattable digital component that is eligible for reformatting using a set of digital component extensions. For each of multiple digital component requests, a determination is made that a given digital component extension has an additional selection requirement that, when combined with a base selection requirement for the given reformattable digital component, would exceed the distribution parameter limit. A determination is made, using a probabilistic technique, a probability at which the given digital component extension will be selected for use in generating a reformatted version of the given reformattable digital component such that an aggregate selection requirement for distributing the given reformattable digital component in response to requests over time is within the distribution parameter limit.