BACKGROUND

Field of the Invention

This invention relates to a method and system for predictive application of virtual personality renderings and more particularly relates to a method of creating a geometric model representative of a personality profile of an individual; and comparative reference of the personality profile with other personality profiles to predict aptitudes, personality patterns, skill sets, and compatibilities, including buying preferences, employment potential, criminal proclivities, and dating matches.

Description of the Related Art

Trait psychology, or dispositional theory, rests on the idea that different people differ from one another on many basic trait dimensions that persist over time and across varying situations. Trait psychology is still in its infancy, and quite often seeks to label individuals with binary traits or characteristics such as extroverted or introverted, intelligent or dumb, nice or mean, hardworking or lazy. Only a very basic level does emerging trait theory start to recognize that, in some respects, individuals are better rated not in binary terms, but across a spectrum, or continuum, with respect to these traits. Trait psychology views personality traits in a largely static manner, and has not yet recognized that personality traits may be dynamic functions of changing environments, stimuli, genetics, and conditioning—much less identified a means of measuring dynamic personality traits and making practical use of those measurements. In general, a person's personality traits, such as generosity and reliability, reflect characteristic patterns of thoughts, intelligence, feelings, and behaviors which are outgrowths of environmental conditioning and genetics.

There have been some attempts in the art analyze personality traits by a merchant, psychologist, or researchers to predict basic preferences or traits. For instance, Amazon® has algorithms in place with attempt to predict which product an individual may be interested in purchasing in the future from past purchases. Psychologist have crude methods of attempting to predict whether criminals will recidivate from past criminal convictions, surveys and conditioning. Even credit reporting agencies attempt to quantify, using credit scores, the likelihood a consumer will pay back future loans based on past actions.

It is increasingly common for third-party aggregators to resell aggregated purchasing, credit, criminal history, income and demographic information to merchants wanting to use the information for commercial purposes, including product marketing.

Proposals for predicting behavior traits of an individual for potential consumerism or in a research project are not yet well-developed. One problem with these behavior tools is that they information gathered with respect to only one personality trait, which is often binary in nature, without any information on how one personality trait influences another.

Even though the above cited systems for predicting behavior traits meet some of the needs of the market, a holistic system for predictive application of virtual personality data; and subsequently use of that personality data in comparison, prediction, ranking and qualification is unknown in the art. It is an object of the present invention to cure these deficiencies as further described below.

SUMMARY

From the foregoing discussion, it should be apparent that a need exists for a method and system for creating virtual personality profiles. The method and system taught below are configured to generate a personality model for an individual that is depicted in the form of a geometric object representing the personality profile for the individual.

A method of creating a virtual personality rendering exclusively associated with a single individual is provided, the steps of the method comprising: manipulating a base shape in virtual three-dimensional (3D) space to create a 3D model of the individual's personality; dividing a surface of the base shape into a plurality of surface regions, each surface region indicative of a personality trait of the individual; subdividing each surface region into a plurality of vectors emanating from the surface region and plotted in the virtual 3D space, each vector indicative of a first datapoint in computer-readable memory exclusively associated with the individual; extending a surface of the base shape outwardly along a vector to an extent indicated by a first datapoint exclusively associated with the vector, the extent defining a terminal end of the first datapoint; and forming a 3D surface intersecting each terminal end of each first data point such that the 3D surface encompasses all plotted first datapoints.

The base shape may comprise a virtual singularity. The singularity may comprise a default set of null first datapoints.

The one of more of the personality traits may comprise extraversion, conscientiousness, risk aversion, novelty seeking, sexuality, humility, intelligence, education, perfectionism, alexithymia, and disinhibition.

In some embodiments, the method may further comprise creating one or more dynamic secondary base shapes in 3D at a terminal end of a first datapoint from which secondary vectors emanate, each secondary vector indicative of a secondary datapoint in computer-readable memory exclusively associated with the individual and the first datapoint.

The method may further comprise creating one or more dynamic secondary regions in 3D at a terminal end of a first data point from which secondary vectors emanate, each secondary vector indicative of a secondary datapoint in computer-readable memory exclusively associated with the individual.

In some embodiments, the method further comprises correlating one first datapoint with a second first datapoint, and adjusting an extent of a terminal end of the one first datapoint to correlate to the terminal end of the second first datapoint.

The method may further comprise correlating a first region with a second region and adjusting a surface of the second region to have correlation with first datapoints in the first region.

The method, in some embodiments, further comprises a step of normalizing each datapoint such that the datapoint represents a plurality of metrics collectively indicated in percentile form, the datapoint exclusively associated with the individual, the datapoint indicating a relative metric to other individuals between 0% and 100%.

The rendering may be formed from a personality profile consisting of a tree data structure, wherein the regions are exclusively associated with nodes within the tree data structure, wherein the datapoints are children of nodes within the tree data structure.

One objective of the disclosure is to provide a virtual personality model that depicts unique personality traits for an individual.

Another objective is to change the shape and dimensions of the base personality model with vectors that represent the magnitude of the personality traits, such that the vectors provide a quick, visual indication of the personality traits for the individual.

Yet another objective is to convert the virtual personality model into a virtual rendering of personality profiles that can be stored in a data storage unit, accessed with a personal communication device, and requested by a merchant in regard to consumers.

Reference throughout this specification to features, advantages, or similar language does not imply that all of the features and advantages that may be realized with the present invention should be or are in any single embodiment of the invention. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present invention. Thus, discussion of the features and advantages, and similar language, throughout this specification may, but do not necessarily, refer to the same embodiment.

Furthermore, the described features, advantages, and characteristics of the invention may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize that the invention may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the invention.

These features and advantages of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth hereinafter.

