The statement that $\phi$ is "an arbitrary determination of its derivative" means that if $x \in I$, then $\phi(x) \in \partial \Phi(x)$, where $\partial \Phi(x)$ is the subdifferential of $\Phi$ at $x$.
Edit:
I should have explained what "subdifferential" means. If a convex function $f:\mathbb R^n \to \mathbb R \cup \{\infty\}$ is finite and differentiable at $x_0$, then it can be shown that $f(x) \geq f(x_0) + \langle \nabla f(x_0),x - x_0 \rangle$ for all $x \in \mathbb R^n$. Even if $f$ is not differentiable at $x_0$, usually there exist vectors $g$ such that
$$
\tag{$\spadesuit$} f(x) \geq f(x_0) + \langle g, x - x_0 \rangle \quad \text{for all } x \in \mathbb R^n.
$$
A vector $g$ which has this property is called a "subgradient" of $f$ at $x_0$. So even if $f$ does not have a gradient at $x_0$, at least $f$ has subgradients at $x_0$, and that is better than nothing.
The set of all subgradients of $f$ at $x_0$ is called the subdifferential of $f$ at $x_0$, and is denoted $\partial f(x_0)$. For example, let $f:\mathbb R \to \mathbb R$ such that $f(x) = | x |$ for all $x \in \mathbb R$. Then $\partial f(0) = [-1,1]$.
It can be shown that, under mild assumptions, a convex function $f$ is differentiable at $x_0$ if and only if $\partial f(x_0)$ is a singleton. In this case, $\partial f(x_0) = \{ \nabla f(x_0) \}$.