The Patent-Based Idea Generation task asks systems to turn real patents into product ideas viable within three years. We propose MK2, a prompt-centric pipeline: Gemini 2.5 drafts and iteratively edits a prompt, grafting useful fragments from weaker outputs; GPT-4.1 then uses this prompt to create one idea per patent, and an Elo loop judged by Qwen3-8B selects the best prompt-all without extra training data. Across three domains, two evaluator types, and six criteria, MK2 topped the automatic leaderboard and won 25 of 36 tests. Only the materials-chemistry track lagged, indicating the need for deeper domain grounding; yet, the results show that lightweight prompt engineering has already delivered competitive, commercially relevant ideation from patents.

How are the merits of innovative ideas communicated in science? Here we conduct semantic analyses of grant application success with a focus on scientific promotional language, which has been growing in frequency in many contexts and purportedly may convey an innovative idea's originality and significance. Our analysis attempts to surmount limitations of prior studies by examining the full text of tens of thousands of both funded and unfunded grants from three leading public and private funding agencies: the NIH, the NSF, and the Novo Nordisk Foundation, one of the world's largest private science foundations. We find a robust association between promotional language and the support and adoption of innovative ideas by funders and other scientists. First, the percentage of promotional language in a grant proposal is associated with up to a doubling of the grant's probability of being funded. Second, a grant's promotional language reflects its intrinsic level of innovativeness. Third, the percentage of promotional language predicts the expected citation and productivity impact of publications that are supported by funded grants. Lastly, a computer-assisted experiment that manipulates the promotional language in our data demonstrates how promotional language can communicate the merit of ideas through cognitive activation. With the incidence of promotional language in science steeply rising, and the pivotal role of grants in converting promising and aspirational ideas into solutions, our analysis provides empirical evidence that promotional language is associated with effectively communicating the merits of innovative scientific ideas.

Four years ago, Gartner predicted that by 2022, 85% of AI projects would fail to deliver tangible outcomes. But eventually, according to the IBM Global AI Adoption Index, around 66% of tech companies either execute or plan to apply AI today. This means the market is still growing, and there is no other way to stay competitive but to adopt artificial intelligence. The prosperity of AI products, in turn, makes it easier for new applications to grow by producing technical resources. However, publicly available assets don't make it any easier to adopt.

The paper presents a possible solution to the problem of algorithmization for quantifying inno-vativeness indicators of technical products, inventions and technologies. The concepts of technological nov-elty, relevance and implementability as components of product innovation criterion are introduced. Authors propose a model and algorithm to calculate every of these indicators of innovativeness under conditions of incompleteness and inaccuracy, and sometimes inconsistency of the initial information. The paper describes the developed specialized software that is a promising methodological tool for using interval estimations in accordance with the theory of evidence. These estimations are used in the analysis of complex multicomponent systems, aggregations of large volumes of fuzzy and incomplete data of various structures. Composition and structure of a multi-agent expert system are presented. The purpose of such system is to process groups of measurement results and to estimate indicators values of objects innovativeness. The paper defines active elements of the system, their functionality, roles, interaction order, input and output inter-faces, as well as the general software functioning algorithm. It describes implementation of software modules and gives an example of solving a specific problem to determine the level of technical products innovation.

Automatic evaluation of text for its innovative quality has been necessitated by the growing trend to organize open innovation contests by different organizations. Such online/offline contests are known to fuel major business benefits to many industries. However, open contests result in a huge number of documents of which only a few may contain potentially interesting and relevant ideas. Usually these entries are manually reviewed and scored by multiple experts. But manual evaluation process not only require a lot of time and effort but are also prone to erroneous judgments due to inter-annotator disagreements. To counter this issue, in this paper, we have proposed a new approach towards detecting novelty or innovativeness of textual ideas from a given collection of ideas. The proposed approach uses information theoretic measures and term relevance to domain to compute document level innovativeness score. We have evaluated the performance of the proposed approach with a real world collection of innovative ideas which were manually scored by experts. We have compared the performance of our proposed model with some of the commonly used baseline approaches that rely on distributional semantics and geometric distances. The result shows that the proposed method outperform the existing baseline models.