Human-Activity Kinetic Alignment (HAKA)

User grouping is a prevalent need in many industries that involve customer interaction or user behavior analysis, such as marketing, social sciences, gaming, and user experience design. Traditional methods for user grouping have primarily relied on demographic data or on user self-labeling or direct inputs, all of which may not accurately reflect true behaviors or similarities. The problem with current grouping methods is that they often suffer from biases, such as the subjective bias from user inputs, and lack the needed fluidity to accurately reflect real-time behavioral patterns or changes, thus making the formed groups static and rigid. Moreover, these methods can not utilize valuable data beyond basic demographics or self-attested interests/preferences, leaving much useful data untapped.

Technology Description

This method for identifying groups of users from a larger pool is based on the analysis of response data, specifically motion data, during a specified time period. After a challenge has been issued to the users, motion data generated in response to this challenge is thoroughly analyzed. The system identifies users with substantially identical, or closely matching, motion data and forms a group from this subset of users. This technology stands out because it leverages the power of motion data to categorize users efficiently. Unlike traditional methods that rely heavily on demographic data or direct user inputs, this approach offers a greater degree of dynamism and variation, as motion behavior could be more distinctive and identifying than typical metrics. The use of challenge-based data collection introduces an element of relevant and context-specific interaction, enhancing the effectiveness and accuracy of group formation.