ML Representation Generator with Component Contribution Analysis
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Solution Overview
Problem
Existing machine learning techniques for generating representations, such as images or words, struggle to consistently produce effective content due to increasing opacity, making it difficult for users to gauge and select effective representations, especially when aiming for specific user engagement metrics like click-through rates or purchases.
Innovation Solution
A method involving training a machine learning model with data sets of representations and effectiveness indicators, allowing users to input desired properties and receive predicted effectiveness scores and component contributions, enabling the generation and selection of representations with improved user engagement by identifying and modifying lower-scoring components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning techniques are used to generate representations, then effectiveness may be improved, but opacity increases making it difficult for users to gauge applicability
Solution Approach 1:
The system implements feedback by providing users with effectiveness scores and component contribution analyses for generated representations. This feedback loop allows users to understand why certain representations are predicted to be effective, transparently communicating the model's reasoning and enabling informed selection of representations.
Solution Approach 2:
The patent introduces an intermediary analysis layer that bridges the opaque machine learning model and the user. This intermediary component breaks down the model's predictions into understandable component contributions, translating complex model outputs into interpretable information about which representation elements drive effectiveness.
2Productivity
If machine learning models generate representations, then selection consistency may be improved, but model opacity makes consistent selection difficult
Solution Approach 1:
The system segments the representation into analyzable components and evaluates each component's contribution to effectiveness. By breaking down representations into discrete elements and assessing their individual impacts, the system provides structured, consistent evaluation criteria that users can apply uniformly across different representations.
Solution Approach 2:
The patent changes the parameter space by introducing effectiveness scores and component contribution metrics as new evaluation parameters. These quantitative parameters provide consistent, objective criteria for representation selection, replacing subjective judgment with measurable standards that enhance selection consistency.
3Measurement precision
If all representations are processed and analyzed, then comprehensive evaluation is achieved, but processing time and resource usage increase
Solution Approach 1:
The system applies partial action by providing effectiveness scores and component analyses for selected representations rather than all possible representations. Users can choose to evaluate only the most promising candidates based on initial filtering, achieving sufficient comprehensiveness without processing every possible representation, thus reducing time and resource consumption.
Data Source
AI summary
A system for generating effective representations and for providing a quantitative indication of the effectiveness of different portions of the representations. The system includes: a user input for inputting a user desired property for a representation; machine learning circuitry trained with a data set of representations and indications of effectiveness relative to different properties, the machine learning circuitry being configured to provide predicted effectiveness scores that indicate effectiveness relative to the desired property; analysing circuitry configured to analyse of the data set of representations to determine a contribution to the predicted effectiveness scores arising from the components. Generative machine learning circuitry may be used with the set of representations, the predicted effectiveness scores and the component contributions to generate a candidate set of representations. The system is configured to transmit the generated candidate set of representations to the trained machine learning circuitry and then to the analysing circuitry; the trained machine learning circuitry predicting effectiveness relative to the desired at least one property for the candidate set of representations and the analysing circuitry analysing components of the candidate set of representations to determine a contribution to the predicted effectiveness scores arising from the components for at least some of the representations in the set of representations. The system further has a display for outputting the candidate set of representations processed by the trained machine learning circuitry and the analysing circuitry along with an indication of predicted effectiveness and of contributions of different components to the predicted effectiveness determined by the trained machine learning circuitry and the analysing circuitry.