Impression Effectiveness Modeling With Granular Time and Location Data

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Solution Overview

Problem

Existing systems for measuring impression effectiveness are constrained by limited data granularity and delayed analysis, often due to disparate data sources and privacy regulations, leading to irrelevant or non-targeted assessments.

Innovation Solution

A system and method using machine learning to analyze impression data with specified location and time granularity, enabling frequent and targeted assessments by isolating data to smaller regions and shorter time intervals, and integrating privacy-friendly data collection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If data is collected from disparate sources with minimum granularity, then data coverage is improved, but analysis precision and timeliness deteriorate

Engineering Contradiction:

Improvedata coverageVSAvoidanalysis precision

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

The patent segments impression data by multiple dimensions including geographic location (country, state, city, zip code), time (date, time of day, day of week), device type, and campaign parameters. This segmentation enables precise analysis at granular levels while maintaining comprehensive data coverage across all dimensions.

Inventive Principle:

Principle #1Segmentation

2Quantity of substance

If data spans weeks or years following impression campaign conclusion, then data completeness is improved, but assessment timeliness deteriorates

Engineering Contradiction:

Improvedata completenessVSAvoidassessment timeliness

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

The system performs preliminary data aggregation and organization during the impression campaign itself, structuring data by location, time, and campaign parameters as it is collected. This preliminary action enables immediate analysis upon campaign conclusion, eliminating delays associated with post-campaign data consolidation.

Inventive Principle:

Principle #10Preliminary action

3Quantity of substance

If third-party cookies are used to collect impression data, then data collection capability is improved, but data privacy compliance deteriorates

Engineering Contradiction:

Improvedata collection capabilityVSAvoiddata privacy compliance

Core Design Contradiction:

Quantity of substanceVSObject-affected harmful factors

Solution Approach 1:

The patent introduces an intermediary layer that collects impression data through alternative mechanisms such as device identifiers, IP addresses, and first-party cookies instead of relying on third-party cookies. This intermediary approach maintains data collection capability while ensuring compliance with evolving privacy regulations.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If analysis is performed at broad geographic and time levels, then processing efficiency is improved, but campaign optimization precision deteriorates

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidcampaign optimization precision

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements dynamic analysis that automatically adjusts the level of granularity based on campaign needs and data availability. Users can select from multiple geographic levels (country to zip code) and time intervals, allowing the system to optimize between processing efficiency and analysis precision dynamically rather than being fixed at one level.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12482017B2Impression effectiveness with greater location and time granularity

Publication Date: 2025.11.25 BLISS POINT MEDIA INC

AI summary

Introduced herein are methods and systems for use of machine learning to measure effectiveness of an impression with specified granularity. For example, the methods and systems herein involve inputting impression data associated with an impression into a machine learning model to assess effectiveness of an impression under specific and narrow time and location parameters, thereby enabling assessments to be conducted in a more frequent and targeted manner.