
    In a fraud detection system that builds layered transaction analysis patterns, calculate the total number of data points examined when analyzing n layers of historical transactions, where each new layer incorporates all previous layers' data points in an expanding triangular pattern to form a comprehensive fraud detection model. Each layer i contains i rows, and each row j contains j transaction data points, creating a tetrahedral structure of analysis points.
    

    
def tetrahedral_number(n):
    """
    In a fraud detection system that builds layered transaction analysis patterns, calculate the total number of data points examined when analyzing n layers of historical transactions, where each new layer incorporates all previous layers' data points in an expanding triangular pattern to form a comprehensive fraud detection model. Each layer i contains i rows, and each row j contains j transaction data points, creating a tetrahedral structure of analysis points.
    

    """
def tetrahedral_number(n):
    """
    In a fraud detection system that builds layered transaction analysis patterns, calculate the total number of data points examined when analyzing n layers of historical transactions, where each new layer incorporates all previous layers' data points in an expanding triangular pattern to form a comprehensive fraud detection model. Each layer i contains i rows, and each row j contains j transaction data points, creating a tetrahedral structure of analysis points.
    

    """
    return n * (n + 1) * (n + 2) / 6