ML Treatment Recommendation Engine for Personalized User Engagement

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Solution Overview

Problem

Businesses fail to effectively engage consumers through digital platforms due to a lack of personalized interaction based on individual consumer data, resulting in inefficient communication strategies.

Innovation Solution

A system utilizing a treatment selection engine that employs machine learning models to analyze user data and generate personalized treatment recommendations, including the use of deep learning and transformer models to create user and treatment embeddings, which are then scored for likelihood of user engagement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If businesses use standardized templated emails and notifications for all consumers, then the ease of operation and implementation is improved, but the effectiveness of consumer engagement and conversion rates deteriorate

Engineering Contradiction:

Improveease of implementing communication strategyVSAvoidconsumer engagement effectiveness

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system applies local quality by customizing treatment characteristics (channel, timing, content) according to individual consumer attributes and behaviors. Each consumer receives a personalized treatment recommendation based on their specific profile, including preferred communication channels, optimal timing windows, and tailored content types, rather than a uniform standardized approach.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system changes parameters of treatment delivery by dynamically adjusting treatment characteristics based on consumer data analysis. The machine learning model modifies parameters such as treatment channel (email, SMS, push notification), timing (optimal delivery window), and content personalization to optimize engagement effectiveness for each consumer segment.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If businesses analyze detailed consumer data to personalize treatments, then the effectiveness of consumer engagement is improved, but the device complexity and computational requirements worsen

Engineering Contradiction:

Improveconsumer engagement effectivenessVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system introduces an intermediary treatment selection engine that acts as a mediator between consumer data and treatment delivery. This engine includes a machine learning model trained on historical consumer data that automatically generates treatment recommendations, reducing the complexity burden on the broader system while maintaining high personalization effectiveness.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary action by pre-training the machine learning model on extensive historical consumer data before deployment. This preliminary training phase allows the model to learn optimal treatment patterns and consumer preferences in advance, enabling efficient real-time treatment recommendations without requiring complex computational resources during actual treatment delivery.

Inventive Principle:

Principle #10Preliminary action

3Ease of operation

If businesses send treatments at predetermined times using standard templates, then the ease of operation is improved, but the loss of information about optimal treatment timing and personalization opportunities worsens

Engineering Contradiction:

Improveease of implementing communication strategyVSAvoidloss of treatment timing and personalization information

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The system applies dynamics by making treatment timing and characteristics flexible and adaptive rather than fixed. The machine learning model dynamically determines optimal treatment timing windows and personalization parameters based on real-time consumer data and historical patterns, allowing the treatment strategy to adapt to changing consumer behaviors and preferences.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements feedback mechanisms by analyzing consumer responses to treatments and using this information to refine future treatment recommendations. The machine learning model continuously learns from treatment outcomes and consumer interactions, adjusting timing and personalization parameters to improve effectiveness over time while reducing information loss about optimal treatment strategies.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260073428A1Systems and Methods For Preparation And Retrieval Of User-Level Treatment Recommendations From A Machine Learning Model

Publication Date: 2026.03.12 AUXIA INC

AI summary

Disclosed herein is a computerized method including operations of receiving a request for a treatment recommendation, where the request includes a user ID and where a treatment is a notification, alert, or message to be provided by a network device. The operations further include retrieving a set of user data from an aggregated user data table according to the user ID and treatment data comprised of data related to a set of predefined treatments from a treatment datastore, determine a score for each of the set of candidate treatments through processing of the set of user data, a set of candidate treatments, and a configured objective as input by a machine learning model, generating a ranked list of treatments based on the scoring provided by the machine learning model, and providing the ranked list of the set of candidate treatments and the set of candidate treatments to the network device.