Machine Learning Latency Thresholds for Search Selection Validation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Users often unintentionally select incorrect search results due to latency issues, where suggested search queries change before the user can intentionally select a relevant link, leading to irrelevant search results pages.

Innovation Solution

A machine-learning system calculates user-specific thresholds based on latency times to differentiate between intentional and unintentional selections by analyzing the time difference between dynamic list updates and user input, replacing or presenting alternative search queries when an invalid selection is detected.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the dynamic list of search suggestions is updated frequently to provide accurate and relevant suggestions, then the relevance and accuracy of search suggestions is improved, but the latency between updates and user selection increases, causing unintentional selections

Engineering Contradiction:

Improveaccuracy of search suggestionsVSAvoidlatency between list update and user selection

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by updating the search suggestion list before the user completes their query input. This allows the system to anticipate user needs and provide relevant suggestions in advance, improving accuracy while managing latency through proactive updates rather than reactive responses

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by monitoring user interaction patterns and using this information to adjust suggestion updates. The machine learning model analyzes user behavior to determine optimal update timing, creating a feedback loop that balances accuracy with latency to prevent unintentional selections

Inventive Principle:

Principle #23Feedback

2Ease of operation

If the system waits for user input to trigger suggestion updates, then the responsiveness to user needs is improved, but the time delay causes invalid selections of recently changed content

Engineering Contradiction:

Improveresponsiveness to user needsVSAvoidvalidity of selections

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system performs preliminary suggestion updates based on predicted user needs rather than waiting for explicit user input. This proactive approach maintains responsiveness by anticipating user intentions while ensuring selection validity by updating suggestions before the user completes their query, preventing invalid selections of recently changed content

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system engages in self-service by automatically monitoring and updating suggestions based on its own analysis of user behavior patterns. The machine learning model continuously learns from user interactions to optimize update timing, allowing the system to serve itself in determining the optimal balance between responsiveness and selection validity

Inventive Principle:

Principle #25Self-service

3Measurement precision

If the system implements user-specific latency thresholds to prevent invalid selections, then the accuracy of selection validation is improved, but the system complexity increases

Engineering Contradiction:

Improveaccuracy of selection validationVSAvoidsystem complexity for latency analysis

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system applies parameter changes by using machine learning to dynamically determine user-specific latency thresholds rather than employing fixed thresholds. This allows the system to adapt to individual user behavior patterns, improving selection validation accuracy while managing complexity through data-driven parameter optimization rather than rigid rule-based systems

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system performs self-service by automatically learning and adapting to each user's specific latency characteristics through machine learning. Rather than requiring complex manual configuration, the system autonomously analyzes user behavior patterns to establish personalized validation thresholds, reducing operational complexity while maintaining high accuracy in selection validation

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS9104982B2Prevention of invalid selections based on machine learning of user-specific latency

Publication Date: 2015.08.11 GOOGLE LLC

AI summary

The specification relates to a client device utilizing an unintentional-selection module that disambiguates selection events for temporally proximate content. The client device records time stamps indicating a time a dynamic list is first presented and instances when the dynamic list is updated. An input selection indicating that a suggested search query has been chosen from the dynamic list of search suggestions is received and a time stamp for the input selection is recorded. A determination is made to see if the input selection is an unintentional selection. The input selection is determined as the unintentional selection when a difference between a time stamp for presenting a most recent dynamic list update and the time stamp of the input selection satisfies a user-specific threshold. The user-specific threshold is calculated with a machine learning system using user-specific latency times as training data.