Sampled Clickstream Insight Ranking for Resource-Efficient UX Optimization

Solution Overview

Problem

Existing systems face challenges in efficiently processing large volumes of consumer data to automatically discover high-quality insights for improving user experiences, particularly in travel-related services, due to the resource-intensive nature of manual programming and the varying consumer demand, which requires significant computing resources and time.

Innovation Solution

An actionable insight recommendation system that samples a representative portion of clickstream data into a small data store, generates insights based on comparisons between display options, and validates these insights against larger datasets to optimize user experiences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual programming is used to process consumer data and discover insights, then high-quality insights can be obtained, but significant computing resources and time are consumed

Engineering Contradiction:

Improveinsight qualityVSAvoidcomputing resources

Core Design Contradiction:

Measurement precisionVSUse of energy by stationary object

Solution Approach 1:

The patent segments the large consumer data set into multiple smaller partitions or batches. Each partition is processed independently to discover local insights, which are then aggregated to form comprehensive insights. This segmentation reduces the computational burden on any single processing unit while maintaining insight quality through systematic combination of results.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary data processing and filtering operations before the main insight discovery process. By pre-processing the data to remove duplicates, filter irrelevant information, and organize data into structured formats, the system reduces the volume of data requiring intensive computational analysis, thereby conserving computing resources while preserving insight quality.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If manual programming is used to process consumer data and discover insights, then high-quality insights can be obtained, but the process is time-consuming

Engineering Contradiction:

Improveinsight qualityVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the large consumer data set into multiple smaller partitions or batches. Each partition is processed independently to discover local insights, which are then aggregated to form comprehensive insights. This segmentation reduces the computational burden on any single processing unit while maintaining insight quality through systematic combination of results.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements continuous parallel processing of multiple data partitions simultaneously. By maintaining continuous computational action across multiple processing units working in parallel, the system significantly reduces total processing time compared to sequential processing, while ensuring comprehensive coverage of the entire data set for high-quality insight discovery.

Inventive Principle:

Principle #20Continuity of useful action

3Use of energy by stationary object

If sampled data is used to generate insights, then computing resources are conserved, but the representative accuracy may be reduced

Engineering Contradiction:

Improvecomputing resourcesVSAvoidinsight accuracy

Core Design Contradiction:

Use of energy by stationary objectVSMeasurement precision

Solution Approach 1:

The patent segments the large consumer data set into multiple smaller partitions or batches. Each partition is processed independently to discover local insights, which are then aggregated to form comprehensive insights. This segmentation reduces the computational burden on any single processing unit while maintaining insight quality through systematic combination of results.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements feedback mechanisms where insights generated from sampled data are validated against additional data samples or validation criteria. This feedback loop ensures that only high-quality, representative insights are selected, maintaining measurement precision while allowing the use of sampled data for resource-efficient processing.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12450626B2Automated actionable insight recommendations

Publication Date: 2025.10.21 EXPEDIA INC

AI summary

Systems and methods of recommending insights to optimize an experience. In some implementations, a request is received to optimize an experience based on a display option of a plurality of display options, one or more filters, and/or one or more metrics. Based on the request, the systems and methods can access (e.g., from a sampled data set) metric data associated with each of the display options. Metric data corresponding to each display options can be compared to generate insights based on each comparison. The systems and methods can determine whether an insight corresponds to a positive improvement and rank the insights based on preconfigured criteria. In addition, insights can be implemented automatically and/or recommended to a user in order to optimize the experience (e.g., based on the one or more metrics).