Patents contain rich technical knowledge that can inspire innovative product ideas, yet accessing and interpreting this information remains a challenge. This work explores the use of Large Language Models (LLMs) and autonomous agents to mine and generate product concepts from a given patent. In this work, we design Agent Ideate, a framework for automatically generating product-based business ideas from patents. We experimented with open-source LLMs and agent-based architectures across three domains: Computer Science, Natural Language Processing, and Material Chemistry. Evaluation results show that the agentic approach consistently outperformed standalone LLMs in terms of idea quality, relevance, and novelty. These findings suggest that combining LLMs with agentic workflows can significantly enhance the innovation pipeline by unlocking the untapped potential of business idea generation from patent data.

This paper evaluates No-Code AutoML as a solution for challenges in AI product prototyping, characterized by unpredictability and inaccessibility to non-experts, and proposes a conceptual framework. This complexity of AI products hinders seamless execution and interdisciplinary collaboration crucial for human-centered AI products. Relevant to industry and innovation, it affects strategic decision-making and investment risk mitigation. Current approaches provide limited insights into the potential and feasibility of AI product ideas. Employing Design Science Research, the study identifies challenges and integrates no-code AutoML as a solution by presenting a framework for AI product prototyping with No-code AutoML. A case study confirms its potential in supporting non-experts, offering a structured approach to AI product development. The framework facilitates accessible and interpretable prototyping, benefiting academia, managers, and decision-makers. Strategic integration of no-code AutoML enhances efficiency, empowers non-experts, and informs early-stage decisions, albeit with acknowledged limitations.

Unlike routine tasks where consistency is prized, in creativity and innovation the goal is to create a diverse set of ideas. This paper delves into the burgeoning interest in employing Artificial Intelligence (AI) to enhance the productivity and quality of the idea generation process. While previous studies have found that the average quality of AI ideas is quite high, prior research also has pointed to the inability of AI-based brainstorming to create sufficient dispersion of ideas, which limits novelty and the quality of the overall best idea. Our research investigates methods to increase the dispersion in AI-generated ideas. Using GPT-4, we explore the effect of different prompting methods on Cosine Similarity, the number of unique ideas, and the speed with which the idea space gets exhausted. We do this in the domain of developing a new product development for college students, priced under $50. In this context, we find that (1) pools of ideas generated by GPT-4 with various plausible prompts are less diverse than ideas generated by groups of human subjects (2) the diversity of AI generated ideas can be substantially improved using prompt engineering (3) Chain-of-Thought (CoT) prompting leads to the highest diversity of ideas of all prompts we evaluated and was able to come close to what is achieved by groups of human subjects. It also was capable of generating the highest number of unique ideas of any prompt we studied.

Lena Anderson isn't a soccer fan, but she does spend a lot of time ferrying her kids between soccer practices and competitive games. "I may not pull out a foam finger and painted face, but soccer does have a place in my life," says the soccer mom--who also happens to be completely made up. Anderson is a fictional personality played by artificial intelligence software like that powering ChatGPT. Anderson doesn't let her imaginary status get in the way of her opinions, though, and comes complete with a detailed backstory. In a wide-ranging conversation with a human interlocutor, the bot says that it has a 7-year-old son who is a fan of the New England Revolution and loves going to home games at Gillette Stadium in Massachusetts.

Tastewise, an AI-powered market intelligence platform for the food and beverage industry, has launched its latest generative AI solution called TasteGPT. This new product is designed to provide speedy and contextual insights into infinite product ideas and help brands make decisions that are right for them. TasteGPT leverages Tastewise's proprietary AI and the largest dataset available on food consumption to provide speed, productivity, and increased new product success. It can shorten months of research and answer business-critical questions such as which product ideas are the best fit for Gen Z consumers, where to launch a new beverage product, and what the focus of the next marketing campaign should be. Traditional market research methods such as surveys, focus groups, and syndicated industry reports typically report data around 13 months late, missing significant behavioral shifts and making them unreliable in the current fast-moving consumer world.

Marc is yet another techie who has made a successful transition to being an entrepreneur. In this interview, he discusses his journey, as well as a lot of the nuances of positioning his venture, Starmind, for success. Sramana Mitra: Let's start by introducing our audience to yourself as well as to Starmind. Marc Vontobel: I'm the Co-Founder and CEO of Starmind. I have a background in Computer Science specializing in AI. That's also the origin of Starmind. I met my co-founder at the artificial intelligence laboratory in Zurich. We were supposed to work on humanoid robots, and we were totally lost. The initial idea of Starmind came up to build an eBay for knowledge. Instead of learning all the different disciplines ourselves, we envisioned having a platform where we can ask any question and put a price tag on it. That's how Starmind started. The problem was it was a one-sided market. No one really wanted to pay a colleague for their knowledge. It took us some time to

In this podcast series of episodes we are going to explain how to create a robotics startup step by step. We are going to learn how to select your co-founders, your team, how to look for investors, how to test your ideas, how to get customers, how to reach your market, how to build your product… Starting from zero, how to build a successful robotics startup. I'm Ricardo Tellez, CEO and co-founder of The Construct startup, a robotics startup at which we deliver the best learning experience to become a ROS Developer, that is, to learn how to program robots with ROS. Our company is already 5 years long, we are a team of 10 people working around the world. We have more than 100.000

Web-scale repositories of products, patents and scientific papers offer an opportunity for creating automated systems that scour millions of ideas and assist users in discovering inspirations and solutions. Yet the common representation of ideas is in the form of raw textual descriptions, lacking important structure that is required for supporting creative innovation. Prior work has pointed to the importance of functional structure -- capturing the mechanisms and purposes of inventions -- for allowing users to discover structural connections across ideas and creatively adapt existing technologies. However, the use of functional representations was either coarse and limited in expressivity, or dependent on curated knowledge bases with poor coverage and significant manual effort from users. To help bridge this gap and unlock the potential of large-scale idea mining, we propose a novel computational representation that automatically breaks up products into fine-grained functional facets. We train a model to extract these facets from a challenging real-world corpus of invention descriptions, and represent each product as a set of facet embeddings. We design similarity metrics that support granular matching between functional facets across ideas, and use them to build a novel functional search capability that enables expressive queries for mechanisms and purposes. We construct a graph capturing hierarchical relations between purposes and mechanisms across an entire corpus of products, and use the graph to help problem-solvers explore the design space around a focal problem and view related problem perspectives. In empirical user studies, our approach leads to a significant boost in search accuracy and in the quality of creative inspirations, outperforming strong baselines and state-of-art representations of product texts by 50-60%.

With the rapidly changing face of technology, AI has indeed reshaped the digital world. AI has created a positive impact on diverse sectors like finance, healthcare, retail, and more. But before we delve into how AI and ML are improving UX, let's have a look at what exactly does UX mean. User Experience (UX) encompasses all the aspects of the end user's interaction with the company, its services, products, and overall customer journey. The most crucial requirement for a great UX is meeting the exact customer needs and understanding their behavioral patterns.