Target Recommendation System Using Weighted Historical Data

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Solution Overview

Problem

Business travelers often prioritize convenience over cost when booking travel arrangements, leading to overspending for organizations, as existing solutions fail to effectively present context-sensitive low-cost options, and historical pricing data lacks organizational context.

Innovation Solution

A method and system that determine a target recommendation for transactions based on historical transaction data, using a target recommendation policy to generate a cost-effective option by retrieving subsets of historical data, applying weights, and calculating a confidence level to align traveler and organizational interests.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If business travelers book based on convenience, then travel arrangement quality is improved, but organizational cost increases

Engineering Contradiction:

Improvetravel arrangement convenienceVSAvoidorganizational travel expense

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The system implements feedback by presenting travelers with historical pricing data and target recommendations that show the cost implications of their choices. Travelers receive information about typical pricing patterns and are encouraged to adjust their preferences to align with organizational cost targets, creating a feedback loop that balances convenience with cost efficiency.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes the parameter of price transparency by dynamically calculating and presenting target prices based on historical data. Instead of showing only actual prices, the system transforms raw pricing data into meaningful target recommendations that guide travelers toward cost-effective choices while maintaining convenience preferences.

Inventive Principle:

Principle #35Parameter changes

2Device complexity

If third party services present averaged pricing data, then data processing complexity is reduced, but organizational context accuracy deteriorates

Engineering Contradiction:

Improvedata processing complexityVSAvoidpricing data organizational context accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system segments pricing data by organization, traveler type, route, and time period rather than presenting aggregated averages. This segmentation allows the system to maintain data processing efficiency while providing organization-specific contextual accuracy that generic averaged data cannot deliver.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by pre-processing and storing historical transaction data in organized segments before it is needed. Historical pricing patterns are calculated and stored in advance, allowing the system to quickly retrieve context-specific data without complex real-time processing when travelers make booking decisions.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If detailed historical transaction data is analyzed, then pricing accuracy is improved, but data processing time increases

Engineering Contradiction:

Improvepricing recommendation accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of historical transaction data during off-peak periods, pre-calculating pricing patterns, averages, and target recommendations. This advance processing stores processed insights that can be quickly retrieved during actual travel booking, maintaining high pricing accuracy without adding delay to the traveler's decision process.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system extracts only the essential pricing patterns and target values from detailed historical data that are needed for decision-making. Instead of processing and presenting all raw historical transactions, the system extracts key insights such as typical price ranges, seasonal patterns, and organization-specific targets, reducing processing time while maintaining accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS11507892B1Determining a target recommendation based on historical transaction data

Publication Date: 2022.11.22 INTUIT INC

AI summary

Certain aspects of the present disclosure provide techniques for determining a target recommendation for a transaction based on historical transaction data. An example technique for determining the target recommendation for a transaction based on historical data includes receiving a request for a target recommendation and transaction parameters from a user. Based on the transaction parameters, one or more subsets of historical transaction data are retrieved, and a respective weight applied to generate the target recommendation. Based on which of the one or more retrieved subsets of historical transaction data and respective weight(s) applied generate the target recommendation, a confidence level is generated. A determination is made whether the confidence level meets a minimum confidence level. Based on the determination that the confidence level meets the minimum confidence level, the target recommendation is provided.