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Design Enhanced User Experience based on Personalized Metrics validated by Historical Engagement Results

IP.com Disclosure Number: IPCOM000248677D
Publication Date: 2016-Dec-24
Document File: 4 page(s) / 332K

Publishing Venue

The IP.com Prior Art Database

Abstract

A method for enhancing user experience based on personalized metrics validated by historical engagement results is disclosed.

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Design Enhanced User Experience based on Personalized Metrics validated by Historical Engagement Results

Disclosed is a method for enhancing user experience based on personalized metrics validated by historical engagement results.

The problem addressed in this disclosure is that of identifying best design modules (or tasks) and design module variations that would be most appealing (or engaging) to a target audience. Common techniques include an iterative approach where the solution team comes up with different possible designs. The solution team then discusses these possible designs with a sample of potential end users, captures their feedback, and iterate until the solution is appealing to that sample of end users. The primary problem with that approach is that the end solution ends up being static, rigid, non-personal and not necessarily the most engaging to the largest possible audience. Instead, the idea is to capture user experiences in one or more environments (applications, games, …) and use that to identify preferred design modules to improve user experience and design practice in new environments. The method learns from users' experiences in a variety of environments and determines what works best for an individual based on his expertise, character, preferred learning method, preferred play style, and personality profile. The method uses that information to identify preferred design modules to maximize users' engagement.

An embodiment for this method follows in the context of game design and focusing on personality profile for users/players. However, the idea/method extends beyond game design to app/solution design to maximize user engagement as well as to other metrics of interest beyond personality profile. Some other metrics of interest include: Tactile Interface, Level of Subject Matter Content (Critical Thinking), Characters, Types of Learning (Auditory, Visual, Kinesthetic), Preferred Play Style (Sensorimotor play also known as functional play characterized by repetition of activity, Role Play from x to y years of age, Rule Based Play where authoritative prescribed codes of conduct are primary, Construction Play which involves experimentation and building, and Movement play also known as physical play).

Game designers take extra care in defining the different levels and tasks to maintain high engagement from the players and appeal to the largest number of potential players. However, there is always a challenge in getting approval from all potential players because people are different and they play the game for different reasons. To overcome that challenge, the method leverages player experiences in earlier games (or web environments) to identify best selection or variation of a given level in the game to achieve maximum engagement for the players.

To be able to leverage players’ experiences in other games, one step is required which is to map different levels in the game to a category of appeal to the players. To do so, a cl...