Machine Learning Model for Product Satisfaction Prediction

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Solution Overview

Problem

Current techniques lack functionality to determine which product attributes may require modification or further development based on user feedback, due to the complexity of analyzing multiple variables influencing product satisfaction.

Innovation Solution

A multi-dimensional prediction platform using a multiple output classification machine learning model is employed to predict scores for various satisfaction metrics by analyzing historical product satisfaction data and incoming product factors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If current techniques are used to analyze user feedback, then the system can collect satisfaction data, but it cannot determine which product attributes require modification due to complexity of multiple variables

Engineering Contradiction:

Improveproduct attribute identification accuracyVSAvoidvariable analysis complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces manual analysis of multiple variables with a machine learning classification model that automatically processes and analyzes product attributes. The model takes multiple input features (product characteristics, user feedback data) and outputs predicted satisfaction scores, eliminating the need for complex manual analysis while improving measurement precision of which attributes need modification.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If multiple variables are analyzed to determine product satisfaction, then more comprehensive insights can be obtained, but the analysis becomes intractable

Engineering Contradiction:

Improvesatisfaction determination accuracyVSAvoidanalysis efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent uses a machine learning classification model to automatically process multiple variables and provide reliable satisfaction determinations efficiently. The model handles multiple input features simultaneously, maintaining comprehensive analysis while dramatically improving productivity through automated pattern recognition and prediction.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service analysis by automatically processing user feedback and product data through the machine learning model without requiring manual intervention. The model autonomously identifies which product attributes require modification based on the analyzed variables, improving both reliability and productivity.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If design engineers manually evaluate product attributes, then they can make informed decisions, but the process is time-consuming

Engineering Contradiction:

Improveproduct attribute evaluation accuracyVSAvoiddevelopment time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual evaluation by design engineers with an automated machine learning classification model. The model processes product attributes and user feedback to provide accurate evaluations of which attributes need modification, maintaining measurement precision while significantly reducing the time required for analysis and decision-making.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250131461A1Product design prediction using machine learning

Publication Date: 2025.04.24 DELL PROD LP

AI summary

A method comprises receiving a request to predict a plurality of scores for a plurality of satisfaction metrics for a product, wherein the request identifies a plurality of factors associated with the product. The request is input to a multiple output classification machine learning model. Using the multiple output classification machine learning model, the plurality of scores are predicted in response to the request. The multiple output classification machine learning model is trained with at least one dataset comprising historical product satisfaction data corresponding to respective ones of a plurality of products.