Autoencoder AI Model for Personality Profile Prediction
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Solution Overview
Problem
Conventional personality questionnaires require users to answer a large number of questions, which can be time-consuming and may lead to inaccurate results due to user fatigue or loss of focus.
Innovation Solution
The development of an artificial intelligence model that can predict a user's likely answers to questions based on their prior answers, using a self-attention layer and autoencoder to encode and decode user data, thereby reducing the need for extensive question answering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional personality questionnaires require consumers to answer large numbers of questions to gather a full profile, then measurement precision of consumer personality and preferences is improved, but loss of time and user fatigue increase
Solution Approach 1:
The patent uses autoencoders to create compressed latent vector representations (copies) of consumer questionnaire answers. Instead of requiring consumers to answer all questions, the system answers a subset of questions and uses the autoencoder to generate latent vectors that capture the essential personality profile information, effectively creating a compressed copy of the full profile data
Solution Approach 2:
The patent pre-trains autoencoder models on large datasets of complete questionnaire answers before deployment. This preliminary training establishes the latent space mapping and encoding/decoding capabilities, so that during actual use, the system can quickly generate accurate personality profiles from partial answers without requiring consumers to complete lengthy questionnaires in real-time
2Loss of time
If consumers rush through answering a large number of questions, then loss of time is reduced, but measurement precision deteriorates due to loss of focus
Solution Approach 1:
The patent extracts only the most critical subset of questionnaire questions needed to capture essential personality traits. By identifying and removing redundant questions, the system reduces the total number of questions consumers must answer while maintaining measurement precision through the autoencoder's ability to reconstruct the full profile from the essential subset
Solution Approach 2:
The patent transforms the questionnaire answering task from requiring complete explicit answers to all questions into generating latent vector representations that capture the essential information. This parameter change in the data representation space allows the system to maintain measurement precision while reducing the cognitive load and time required from consumers
3Measurement precision
If an artificial intelligence model uses a self-attention layer and autoencoder to predict user answers, then measurement precision is improved with fewer questions, but device complexity increases
Solution Approach 1:
The patent divides the AI model into distinct functional segments: an autoencoder component for encoding questionnaire answers into latent vectors, a self-attention layer for processing the encoded representations, and a decoding component for generating predicted answers. This segmentation allows each component to be optimized independently and facilitates understanding of the complex overall system
Solution Approach 2:
The patent introduces latent vectors as an intermediary representation between the input questionnaire answers and the output predicted answers. The self-attention layer operates on these latent vectors rather than raw answers, creating an intermediate processing space that simplifies the overall transformation and improves measurement precision while managing model complexity
Data Source
AI summary
The present disclosure is directed to methods for training and using an artificial intelligence model for use with predicting a task output. The method includes generating a plurality of individual datasets, each of the plurality of individual datasets comprising data of at least one answer to at least one of a plurality of questions of each of a plurality of questionnaires, generating a first batch of individual datasets, the first batch of individual datasets comprising one or more of the plurality of individual datasets, inputting the first batch of individual datasets into the artificial intelligence model, and encoding the data of the first batch of individual datasets with an autoencoder.