# Big Data and AI-Driven Product Design: A Survey
###### Abstract  
As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI-driven product design methodology with a significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review of big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited to aid product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design. product design; big data; AI algorithm; AI-generated content; Konsei engineering; generative design +
Footnote †: journal: Medical Engineering, Guizhou Institute of Technology, Guiyang 550050, China; hongjingwei@126. Currently, customers are increasingly focused on their diversified and personalized spiritual and emotional needs. They pay more and more attention to product appearance [9; 10; 11; 12; 13] in addition to its functions. Traditional product design methods, such as the theory of inventive problem solving (TRIZ), quality function deployment (QFD), KANO model, Kansei engineering, and axiomatic design (AD), have been well-established [2; 14; 15; 16]. However, capturing user requirements, ensuring visibility, and evaluating products remain significant challenges in the field of product design. The fundamental aspect of capturing user requirements and evaluating products is data acquisition. Traditional methods rely on manual surveys [10; 17; 18; 19], which are time-consuming, labor-intensive, and often result in uneven data distribution. Additionally, these data are one-time and cannot be updated, which is a major obstacle in the fast-paced era we live in [20]. Alternatively, relying on designers' or experts' experience and intuition is highly subjective, uninterpretable, and risky [21]. As we will discuss in detail in Section 3, traditional methods also have several other drawbacks. These deficiencies led to lengthy product development cycles, poor predictability, and low success rates, which are crucial to enterprises. However, recent studies have found that big data and AI algorithms can alleviate these problems in an automated and intelligent manner [20; 22]. Especially, the AI-Generated Content (AIGC) can bring disruptive breakthroughs to product design. Big data and AI algorithms have been increasingly applied in the field of product design [18; 23]. With the rapid development of the Internet, IoT, and communication technologies, a large amount of data has accumulated in the product lifecycle (Figure 1), which is expanding exponentially every day [24; 25; 26]. The product lifecycle contains a lot of product feedback information, such as user preferences, market demands, and visual displays [27; 28; 29]. This information is valuable for guiding product design and has sparked increasing interest in both product design and big data fields [30; 31]. On one hand, processing and analyzing such a massive amount of data presents new challenges that need to be appropriately addressed.