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We introduce a novel actor-critic framework that utilizes vision-language models (VLMs) and large language models (LLMs) for design concept generation, particularly for producing a diverse array of innovative solutions to a given design problem. By leveraging the extensive data repositories and pattern recognition capabilities of these models, our framework achieves this goal through enabling iterative interactions between two VLM agents: an actor (i.e., concept generator) and a critic. The actor, a custom VLM (e.g., GPT-4) created using few-shot learning and fine-tuning techniques, generates initial design concepts that are improved iteratively based on guided feedback from the critic—a prompt-engineered LLM or a set of design-specific quantitative metrics. This process aims to optimize the generated concepts with respect to four metrics: novelty, feasibility, problem–solution relevancy, and variety. The framework incorporates both long-term and short-term memory models to examine how incorporating the history of interactions impacts decision-making and concept generation outcomes. We explored the efficacy of incorporating images alongside text in conveying design ideas within our actor–critic framework by experimenting with two mediums for the agents: vision language and language only. We extensively evaluated the framework through a case study using the AskNature dataset, comparing its performance against benchmarks such as GPT-4 and real-world biomimetic designs across various industrial examples. Our findings underscore the framework’s capability to iteratively refine and enhance the initial design concepts, achieving significant improvements across all metrics. We conclude by discussing the implications of the proposed framework for various design domains, along with its limitations and several directions for future research in this domain. 

Generative adversarial networks (GANs) have recently been proposed as a potentially disruptive approach to generative design due to their remarkable ability to generate visually appealing and realistic samples. Yet, we show that the current generator-discriminator architecture inherently limits the ability of GANs as a design concept generation (DCG) tool. Specifically, we conduct a DCG study on a large-scale dataset based on a GAN architecture to advance the understanding of the performance of these generative models in generating novel and diverse samples. Our findings, derived from a series of comprehensive and objective assessments, reveal that while the traditional GAN architecture can generate realistic samples, the generated and style-mixed samples closely resemble the training dataset, exhibiting significantly low creativity. We propose a new generic architecture for DCG with GANs (DCG-GAN) that enables GAN-based generative processes to be guided by geometric conditions and criteria such as novelty, diversity and desirability. We validate the performance of the DCG-GAN model through a rigorous quantitative assessment procedure and an extensive qualitative assessment involving 89 participants. We conclude by providing several future research directions and insights for the engineering design community to realize the untapped potential of GANs for DCG. 

Aspect-based sentiment analysis (ABSA) enables a systematic identification of user opinions on particular aspects, thus enhancing the idea creation process in the initial stages of product/service design. Attention-based large language models (LLMs) like BERT and T5 have proven powerful in ABSA tasks. Yet, several key limitations remain, both regarding the ABSA task and the capabilities of attention-based models. First, existing research mainly focuses on relatively simpler ABSA tasks such as aspect-based sentiment analysis, while the task of extracting aspect, opinion, and sentiment in a unified model remains largely unaddressed. Second, current ABSA tasks overlook implicit opinions and sentiments. Third, most attention-based LLMs like BERT use position encoding in a linear projected manner or through split-position relations in word distance schemes, which could lead to relation biases during the training process. This article addresses these gaps by (1) creating a new annotated dataset with five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI), (2) developing a unified model capable of extracting all five types of labels simultaneously in a generative manner, and (3) designing a new position encoding method in the attention-based model. The numerical experiments conducted on a manually labeled dataset scraped from three major e-Commerce retail stores for apparel and footwear products demonstrate the performance, scalability, and potential of the framework developed. The article concludes with recommendations for future research on automated need finding and sentiment analysis for user-centered design. 

Aspect-based sentiment analysis (ABSA) provides an opportunity to systematically generate user's opinions of specific aspects to enrich the idea creation process in the early stage of product/service design process. Yet, the current ABSA task has two major limitations. First, existing research mostly focusing on the subsets of ABSA task, e.g. aspect-sentiment extraction, extract aspect, opinion, and sentiment in a unified model is still an open problem. Second, the implicit opinion and sentiment are ignored in the current ABSA task. This article tackles these gaps by (1) creating a new annotated dataset comprised of five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI) and (2) developing a unified model which could extract all five types of labels simultaneously in a generative manner. Numerical experiments conducted on the manually labeled dataset originally scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, scalability, and potentials of the framework developed. Several directions are provided for future exploration in the area of automated aspect-based sentiment analysis for user-centered design. 

Generative Adversarial Networks (GANs) have shown stupendous power in generating realistic images to an extend that human eyes are not capable of recognizing them as synthesized. State-of-the-art GAN models are capable of generating realistic and high-quality images, which promise unprecedented opportunities for generating design concepts. Yet, the preliminary experiments reported in this paper shed light on a fundamental limitation of GANs for generative design: lack of novelty and diversity in generated samples. This article conducts a generative design study on a large-scale sneaker dataset based on StyleGAN, a state-of-the-art GAN architecture, to advance the understanding of the performance of these generative models in generating novel and diverse samples (i.e., sneaker images). The findings reveal that although StyleGAN can generate samples with quality and realism, the generated and style-mixed samples highly resemble the training dataset (i.e., existing sneakers). This article aims to provide future research directions and insights for the engineering design community to further realize the untapped potentials of GANs for generative design. 

Prior research has shown the importance of latent user needs for enabling innovation in early product development phases. The success of a product is largely dependent on to what extent the product satisfies customer needs, and latent user needs play a significant role in impacting the way the product or service unexpectedly delights the user. Complications arise because traditional need finding methods are not able to account for the nuances of latent user needs. A user's need is multidimensional while traditional methods are built on deductive reasoning. The traditional method isolates parts of the user's needs, only pointing to what is deducible within its search space. To address this, we introduce abduction as a way to broaden traditional need finding methods. From a logic based argument it is shown that abduction accounts for the dimensionality of user needs by integrating various traditional need finding theories using design knowledge to isolate the latent need. This theoretical development shows that latent need finding must go beyond a deductive focus, to developing methods that are able to conjecture with the deduced facts in order to abduce the latent user need. 

Abstract Eliciting informative user opinions from online reviews is a key success factor for innovative product design and development. The unstructured, noisy, and verbose nature of user reviews, however, often complicate large-scale need finding in a format useful for designers without losing important information. Recent advances in abstractive text summarization has created the opportunity to systematically generate opinion summaries from online reviews to inform the early stages of product design and development. However, two knowledge gaps hinder the applicability of opinion summarization methods in practice. First, there is a lack of formal mechanisms to guide the generative process with respect to different categories of product attributes and user sentiments. Second, the annotated training datasets needed for supervised training of abstractive summarization models are often difficult and costly to create. This article addresses these gaps by (1) devising an efficient computational framework for abstractive opinion summarization guided by specific product attributes and sentiment polarities, and (2) automatically generating a synthetic training dataset that captures various degrees of granularity and polarity. A hierarchical multi-instance attribute-sentiment inference mode is developed for assembling a high-quality synthetic dataset, which is utilized to fine-tune a pretrained language model for abstractive summary generation. Numerical experiments conducted on a large dataset scraped from three major e-Commerce retail store for apparel and footwear products indicate the performance, feasibility, and potentials of the developed framework. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered design. 

The early-stage product design and development (PDD) process fundamentally involves the processing, synthesis, and communication of a large amount of information to make a series of key decisions on design exploration and specification, concept generation and evaluation, and prototyping. Although most current PDD practices depend heavily on human intuition, advances in computing, communication, and human–computer interaction technologies can transform PDD processes by combining the creativity and ingenuity of human designers with the speed and precision of computers. Emerging technologies like artificial intelligence (AI), cloud computing, and extended reality (XR) stand to substantially change the way designers process information and make decisions in the early stages of PDD by enabling new methods such as natural language processing, generative modeling, cloud-based virtual collaboration, and immersive design and prototyping. These new technologies are unlikely to render the human designer obsolete, but rather do change the role that the human designer plays. Thus, it is essential to understand the designer's role as an individual, a team, and a group that forms an organization. The purpose of this special issue is to synthesize the state-of-the-art research on technologies and methods that augment the performance of designers in the front-end of PDD—from understanding user needs to conceptual design, prototyping, and development of systems architecture while also emphasizing the critical need to understand the designer and their role as well.