Predicting Recipient Attributes in Messaging Data Structures

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Solution Overview

Problem

Messaging data structures often lack attribute information for recipient entities, leading to sub-optimal selection of recipient groups for electronic communications, resulting in inaccurate retrieval of appropriate recipients.

Innovation Solution

A system that predicts attribute values for recipient entities by analyzing known values from member recipient entities sharing common characteristics, such as subscription to the same online content service or domain, and updates the messaging data structure with these predictions to facilitate accurate retrieval of address data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If messaging data structures store only explicitly provided attribute information, then data storage complexity is reduced, but retrieval accuracy of appropriate recipients deteriorates

Engineering Contradiction:

Improveretrieval accuracyVSAvoiddata structure complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by predicting and populating missing attribute values for recipient entities before communication campaigns are executed. The message management application proactively identifies entities with incomplete attribute data and uses distribution analysis to fill these gaps, ensuring that the messaging data structure is fully populated in advance, thereby improving retrieval accuracy without requiring complex real-time queries.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary mechanism - the distribution-based prediction system - that bridges the gap between incomplete attribute data and accurate recipient retrieval. By using known attribute distributions from member entities as intermediaries to infer unknown attributes of target entities, the system achieves high retrieval accuracy without directly storing complete attribute information for all entities.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If messaging data structures include complete attribute information for all recipient entities, then recipient group selection accuracy is improved, but data collection and maintenance difficulty increases

Engineering Contradiction:

Improverecipient selection reliabilityVSAvoiddata collection ease

Core Design Contradiction:

ReliabilityVSEase of manufacture

Solution Approach 1:

The system implements self-service by enabling the messaging data structure to automatically predict and complete its own attribute information. When attribute data is missing for a target recipient entity, the system autonomously queries the messaging data structure for distribution patterns among similar member entities and computes predicted values, eliminating the need for manual data collection and maintenance for each entity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent employs feedback mechanisms where the system continuously queries the messaging data structure to analyze attribute distributions and uses this feedback to refine predictions. The system monitors retrieval accuracy and adjusts prediction strategies based on the observed performance, ensuring high recipient selection reliability while maintaining data collection simplicity.

Inventive Principle:

Principle #23Feedback

3Loss of information

If attribute values are predicted using distribution analysis, then missing attribute information is recovered, but computational processing time increases

Engineering Contradiction:

Improveattribute information completenessVSAvoidprocessing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system applies partial action by selectively predicting attribute values only for target recipient entities that have missing attribute information, rather than reprocessing the entire messaging data structure. This approach recovers necessary attribute information while minimizing computational overhead by focusing only on entities that require attribute completion.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system performs preliminary distribution analysis during idle periods or batch processing, pre-computing attribute distributions from member entities and storing this information in the messaging data structure. When a query requires attribute prediction, the system retrieves pre-computed distributions rather than performing full analysis, significantly reducing processing time while maintaining information completeness.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10817845B2Updating messaging data structures to include predicted attribute values associated with recipient entities

Publication Date: 2020.10.27 INTUIT INC

AI summary

This disclosure involves modifying messaging data having unknown attribute values associated with entities to facilitate retrieval of address data for communications with the entities. For example, a system accesses a mapping of first addresses to an attribute, wherein the first addresses include (1) a target address for a target entity and (2) addresses associated with first entities in turn associated with first known values of the attribute. The system accesses a mapping of second addresses to an attribute, wherein the second addresses include (1) the target address for the target entity and (2) addresses associated with second entities in turn associated with second known values of the attribute. The system determines distributions of the first known values and the second known values, predicts a value of the attribute for the target entity based thereon, updates the messaging data therewith, and services a query for addresses having the predicted value.