# Product Innovation Design Based on Deep Learning and Kansei Engineering
## 1 Introduction  
Creativity is a rising topic in current society. The emphasis on creativity is valued as a key factor for success in all areas, including, but not limited to, politics, economics, culture, arts and product design. Effective approaches that inspire product designers are thus strongly desirable. One of the effective approaches is visualization. [PERSON] said: _\"Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn't really do it, they just **save something**._\" In this study, we propose a novel image-to-image visual generation approach for product innovation by combining deep learning-based neural style transfer algorithm with Kansei engineering. In recent years, deep learning methods, such as convolutional neural networks (CNN), have made breakthroughs in many fields, such as computer vision, and are widely applied in object recognition, detection and segmentation [1; 2; 3]. In the area of product design, [PERSON] et al. [4] proposed to train a CNN with standard usability heuristics for evaluating usability, which is an easy method for evaluating usability in thermostats, based on images. [PERSON] et al. [5] used a scalable deep learning approach to predict and interpret customer perceptions of design attributes for heterogeneous markets. [PERSON] et al. [6] present a deep learning-based approach to automatically link customer needs to product design parameters. [PERSON] et al. [7; 8] extended CNN to generative adversarial networks (GAN) and built a system that can implement the following three applications: (1) manipulating an existingproduct photo based on an underlying generative model to achieve different looks (shape and color); (2) \"generative transformation\" of one product image onto another product; (3) generating a new product image from scratch based on user's scribbles and warping user interface (UI). In addition, [PERSON] et al. [PERSON] et al. (2018) achieved automatic coloring of product sketches. [PERSON] et al. (2018) generated a new product image of one domain given an image from another domain. For example, they took a handbag (or shoe) image as input, and generated its corresponding shoe (or handbag) image. The above studies have been successful. They can generate product images directly. However, they only focus on the exploration of image generation while ignoring user requirements for products. In this paper, we propose a Kansei engineering-based neural style transfer for product innovation (KENPI) framework, which can directly generate high-quality product images and meet user preferences. Kansei engineering emerged in Japan in the 1970s with the purpose of connecting customers' affective responses to the design process of products, in an attempt to translate emotions into measurable and physical design specifications [PERSON] et al. (2018). Many scholars pointed out that satisfying the requirements of users is the key to product design [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). As a user-driven method, Kansei engineering has been widely applied in various product designs [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018), such as USB flash drives, running shoes, in-vehicle rubber keypads, and so on. [PERSON] et al. [PERSON] et al. (2018) used Kansei engineering to construct a relationship model between user requirements and steering wheel design parameters. Through this model, a steering wheel was designed to meet the user's preferences. Although Kansei engineering can maximize user satisfaction, it can only provide theoretical guidance for designers and cannot directly generate products, which is a fatal defect of product form design. Deep learning-based neural style transfer technology can generate images with high quality. In 2015, [PERSON] et al. [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018) demonstrated that representations of content and style are separable. We can manipulate both of them independently.