In Stochastic Equations in Infinite Dimensions, Theorem 4.32 (Google Books), the authors present the following version of an Itō formula:
Given Hilbert spaces $(U,\langle\;\cdot\;,\;\cdot\;\rangle_U)$ and $(H,\langle\;\cdot\;,\;\cdot\;\rangle)$, a $U$-valued Brownian motion $(W_t)_{t\ge 0}$ and $$X_t=X_0+\int_0^t\varphi_s\;{\rm d}s+\int_0^t\Phi_s\;{\rm d}W_s\tag 1$$ for some $H$-valued random variable $X_0$, $H$-valued stochastic process $(\varphi_t)_{t\ge 0}$ and $\mathfrak L(U,H)$-valued$^1$ stochastic process $(\Phi_t)_{t\ge 0}$, we've got
\begin{equation} \begin{split} f(t,X_t)-f(0,X_0)&=\color{red}{\int_0^t\langle\Phi_s{\rm d}W_s,F_x(s,X_s)\rangle}\\ &\quad\color{blue}{+\text{something unimportant for this question}} \end{split}\tag 2 \end{equation}
for all $F:[0,\infty)\times H\to\mathbb R$ with partial Fréchet derivatives $F_t$, $F_x$ and $F_{xx}$.
Question: What's the definition of the $\color{red}{\text{red}}$ term? (They don't give one in the book).
The proof of the statement can be reduced to the case $\varphi_t=\varphi_0$ and $\Phi_t=\Phi_0$. If $0=t_0<\cdots<t_n=t$ is a partition of $[0,t]$, Taylor's theorem yields$^2$
\begin{equation} \begin{split} f(t,X_t)-f(0,X_0)&=\color{red}{\sum_{i=1}^n\langle\Delta X_i,L_i\rangle}\\ &\quad\color{blue}{+\text{something unimportant for this question}} \end{split}\tag 3 \end{equation}
where $\Delta t_i=t_i-t_{i-1}$, $\Delta X_i=X_{t_i}-X_{t_{i-1}}$ and $$L_i:=F_x(t_{i-1},X_{t_{i-1}})\;.$$ Using $(1)$ and our assumption, the $\color{red}{\text{red}}$ term in $(3)$ is $$\color{red}{\sum_{i=1}^n\langle\Phi_0\Delta W_i,L_i\rangle}\color{blue}{+\sum_{i=1}^n\Delta t_i\langle\varphi_0,L_i\rangle}\tag 4\;.$$ Using the definition of the adjoint operator, the $\color{red}{\text{red}}$ term in $(4)$ is $$\sum_{i=1}^n\langle\Delta W_i,\Phi_0^\ast L_i\rangle_U\;.\tag 5$$ In the middle of page 108 they state (our $\color{red}{\text{red}}$ term from $(3)$ is called $I_2$ there) that $(5)$ converges almost surely to $$\int_0^t\langle\Phi_s{\rm d}W_s,F_x(s,X_s)\rangle\tag 6$$ for $n\to\infty$.
Why?
$^1$ Let $\mathfrak L(A,B)$ be the space of bounded, linear operators $A\to B$.
$^2$ Notice that we can make sense of $(3)$, since $L_i\in\mathfrak L(H,\mathbb R)\cong H$ by Riesz' representation theorem.
There is exactly a definition of the term $\int_0^t\langle\Phi_s{\rm d}W_s,F_x(s,X_s)\rangle$.
For $\Phi_s$ taking values in $\mathfrak L_2(Q^\frac{1}{2}U,H)$ and satisfying the condition that the integral of $\Phi_s$-'s square-norm in $\mathfrak L_2(Q^\frac{1}{2}U,H)$ is a.s. finite (just called the "Energe Condition" privately), and for $\Psi_s$ a $H$-valued process, one can prove that the process $\Phi_s^*\Psi_s$ defined by $$(\Phi_s^*\Psi_s)(u)=\langle\Phi_su,\Psi_s\rangle\quad\text{for }u\in Q^\frac{1}{2}U$$ has values in $\mathfrak L_2(Q^\frac{1}{2}U,\mathbb R)$ and satisfies the Energe Condition. Hence we can define $$\int_0^t\langle\Phi_s{\rm d}W_s,\Psi_s\rangle:=\int_0^t(\Psi_s^*\Phi_s){\rm d}W_s.$$
I don't know whether there is any comment like above in Prato's book, yet it is noted in another reference book --- "Stochastic Differential Equations in Infinite Dimensions" by L. Gawarecki & V. Mandrekar, in page 61.