Personality Diagnostics via Behavioral Manifestation Analysis
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Solution Overview
Problem
Current solutions for predicting and targeting individual customer preferences in retail and technology industries rely on statistical analysis of customer groups, leading to impersonalized marketing and product suggestions, failing to account for unique personal traits and behaviors.
Innovation Solution
An automated system that uses computer perception to analyze observable behavioral manifestations from various data sources, generating personalized profiles through ontological graphs and machine learning algorithms to provide tailored product and service recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If statistical analysis of customer groups is used, then productivity increases through automated processing, but measurement precision deteriorates resulting in impersonalized targeting
Solution Approach 1:
The patent segments the statistical customer group data into individual behavioral manifestations. Instead of treating customers as homogeneous statistical units, the system divides the analysis into individual-level observable behaviors (clicks, purchases, page views) that can be separately processed and aggregated to reveal unique personality traits for each customer.
Solution Approach 2:
The patent introduces an intermediary layer between statistical data and personality attribution. The system uses observable behavioral manifestations as intermediaries that bridge raw statistical data and personality traits. These behavioral manifestations serve as measurable proxies that enable automated processing while maintaining individual-level precision in personality detection.
2Measurement precision
If all available customer data is collected, then measurement precision improves for personality prediction, but device complexity increases due to data management requirements
Solution Approach 1:
The patent extracts only the essential observable behavioral manifestations from the vast array of available customer data. Instead of attempting to process all possible data types, the system identifies and extracts specific behavioral signals (purchases, clicks, page views) that are most informative for personality prediction, thereby reducing data management complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent creates a universal data processing framework that handles multiple data sources through a unified approach. The system processes diverse data types (transaction data, browsing behavior, demographic information) through a common analytical model based on observable behavioral manifestations, reducing the complexity that would arise from managing separate processing pipelines for each data type.
3Ease of operation
If statistical group targeting is implemented, then ease of operation improves through standardized processes, but adaptability deteriorates by ignoring individual personal traits
Solution Approach 1:
The patent implements dynamic adaptability within the automated marketing system. Instead of static statistical group assignments, the system dynamically adjusts targeting strategies based on continuously updated personality predictions derived from individual behavioral manifestations. This allows the system to maintain automated operation while adapting to each customer's unique personality traits and evolving preferences.
Solution Approach 2:
The patent applies local quality by customizing marketing approaches for each individual customer based on their specific personality traits, while maintaining standardized processes at the system level. The system processes data through uniform automated pipelines but generates personalized outcomes for each customer, achieving both ease of operation through automation and adaptability through individualized targeting.
Data Source
AI summary
The invention relates to a method of predicting personality based on data generated from multiple data sources and determining a recommended item based on the predicted personality. A platform server receives, from a plurality of source devices, personality data associated with a user. Using the received personality data, the platform server generates a set of diagnostic features. At least one model is applied to the set of generated diagnostic features to generate at least one personality measurement. The personality measurements are used to generate a personal profile. The personal profile is matched to at least one recommended item, and the recommended item is transmitted to the user.