Psychological Factor Vector Content Selection
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Solution Overview
Problem
Computing systems face challenges in identifying and leveraging psychological factors to effectively influence user behavior and decision-making in response to creative content, as existing methods lack precision in selecting content based on cognitive biases and heuristics.
Innovation Solution
A method and system that generate person vectors and psychological factor vectors to combine into input vectors, which are used to predict action probabilities, allowing for the selection and presentation of content that maximizes user interaction by incorporating factors like social proof, effort aversion, anchoring, and urgency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content selection is based on traditional methods without psychological factor analysis, then the system is simpler to implement, but the precision of predicting user behavior and the effectiveness of influencing user decisions deteriorates
Solution Approach 1:
The patent segments the content selection process into distinct components: generating person vectors from user records, generating psychological factor vectors from content, combining them into input vectors, and producing action scores. This segmentation allows each component to be optimized independently while maintaining overall system precision.
Solution Approach 2:
The patent introduces psychological factor vectors as intermediary elements that bridge user characteristics and content features. These vectors serve as mediators that quantify psychological factors (such as cognitive biases and heuristics) to enable precise prediction of user behavior without requiring direct complex analysis of raw data.
2Productivity
If the system analyzes multiple psychological factors to select content, then user interaction effectiveness improves, but the computational complexity and data processing requirements worsen
Solution Approach 1:
The patent transforms psychological factors into quantifiable vector parameters that can be processed computationally. By converting abstract psychological concepts (cognitive biases, heuristics) into numerical vectors with specific dimensions and values, the system enables efficient computational analysis while maintaining the nuanced understanding of user psychology.
Solution Approach 2:
The patent replaces traditional content selection methods with a machine learning-based vector processing system. Instead of relying on manual or rule-based selection, the system uses automated vector generation, combination, and scoring mechanisms that efficiently handle multiple psychological factors simultaneously through computational algorithms.
3Measurement precision
If the system uses detailed person vectors and psychological factor vectors, then the accuracy of content selection improves, but the data processing time and computational resources worsen
Solution Approach 1:
The patent generates person vectors and psychological factor vectors as preprocessed intermediate representations that capture essential user and content characteristics. These pre-generated vectors serve as ready-to-use inputs for the content selection process, eliminating the need for repeated complex analysis during actual content delivery and reducing real-time processing time.
Solution Approach 2:
The patent creates vector representations (copies) of user profiles and content features that preserve the essential information needed for accurate content selection. These vector copies enable rapid comparison and matching operations without requiring access to or processing of the original complex data structures, significantly reducing computational overhead.
Data Source
AI summary
A method implements content selection using psychological factors. A person vector is generated from a record. The person vector identifies a set of data representing a person. A psychological factor vector is generated from a content of a plurality of content. The psychological factor vector identifies a set of psychological factors from the content. The person vector and the psychological factor vector are combined to generate an input vector. An action score is generated from the input vector. The action score identifies a probability of an action on the content by the person. The content is selected from the plurality of content using the action score. The content is presented.