Figure 1.6 is depicting that the hinge-loss used in SVMs (Eqn 1.9):

$$L_i^{svm} = \text{max}\{1 - y_i (\overline{W} \cdot \overline{X}_i, 0\} \tag{1.9}$$

is a shifted version (+1 to the right) of the loss function (Eqn 1.8) used to optimize the perceptron criterion (see footnote (1)):

$$L_i = \text{max}\{-y_i (\overline{W} \cdot \overline{X}_i), 0\} \tag{1.8}$$

Using Figure 1.6 and the accompanying discussion in Section 1.2.1.2, the author (Charu C. Aggarwal) is trying to show that if you make the implicit training process of the perceptron explicit (with an objective function, in his case, a loss function defined by Eqn 1.8), then one realises (in his words):

that the perceptron is fundamentally not very different from well-known machine learning algorithms like the support vector machine in spite of its different origins.

Footnotes

(1) The perceptron criterion was introduced in Section 3.5.1 of Neural Networks for Pattern Recognition by Christopher Bishop as a continuous, piecewise linear error function given by:

$$E^{\text{perc}}(\bf{w}) = - \sum_{\phi^n \in \mathcal{m}} w^T (\phi^n t^n)$$

Bishop suggested that this objective function is minimised when training a perceptron. Aggarwal references this work as an example of how the perceptron training process implicitly minimises this error function, using it as a segue into his own derivation of Eqn 1.8 before encouraging the reader to check that the gradient of Eqn 1.8 leads to the perceptron update (i.e. $\overline{W} \Leftarrow \overline{W} - \alpha \nabla_{W} L_i$).