World Conference of AI-Powered Innovation and Inventive Design

Addressing the intricate challenges of lavender harvesting, this study pioneers the development of an autonomous robot, integrating cutting-edge methodologies to enhance agricultural robotics. Traditional harvesting methods have faltered in efficiently collecting lavender, a crop demanding precise and gentle handling. By synthesizing the “Cognitive Breakthrough Assistant” (COBRA), generative AI, the Theory of Inventive Problem Solving (TRIZ), and the Complex Systems Design Technique (CSDT), our research seeks to dismantle psychological inertia, fostering a fertile ground for innovative solutions in robotic design. The methodology adopts COBRA to pinpoint and disrupt entrenched thinking patterns, utilizing its natural language processing to reassess and redefine problem-solving paradigms. The amalgamation of generative AI with TRIZ and CSDT propels forward the ideation and design phases, crafting a robot equipped with sophisticated navigation, delicate handling mechanisms, and adaptive algorithms tailored for the nuances of lavender fields. This interdisciplinary approach yields a prototype that excels in diverse harvesting scenarios and sets the stage for its optimization toward commercial viability, emphasizing scalability and sustainability. With rigorous field trials, the prototype's efficacy in real-world conditions promises a significant leap toward commercial production.

The aim of this article is to improve the performance of production systems by integrating Lean tools and functionalities from the design phase, thereby reducing the need to apply them during the use phase of future manufacturing systems. This implies a change of mindset; instead of reacting to problems as they arise and then implementing Lean to remedy them, companies should proactively integrate it from the design phase. However, trying to integrate several Lean tools and functionalities into the design of a production system to satisfy multiple performance criteria can generate contradictions. For this reason, we propose a new methodology: Lean Parameter Integration Matrix (LPIM), which can be used to resolve the different types of problems. LPIM contains a set of generalized Lean parameters extracted from the Lean tools most used in companies. It allows the simultaneous identification of technical and physical contradictions that may arise from the integration of multiple Lean tools and their functionalities, and provides guidance for their resolution, using the principles of TRIZ methodology for resolving technical and physical contradictions.

Enhancing robotic system efficiency through lean principles is essential in the dynamic landscape of industrial automation. This study introduces a comprehensive framework combining the “Visionary Pathway Design Tool” (VPDT), simulation-based analysis, and TRIZ methodologies to optimize lean concept integration in robotic systems across various production scenarios. The research addresses the complexities of designing intelligent industrial robotic systems under lean management, focusing on enhancing production process resilience and efficiency The approach integrates VPDT's AI-driven problem definition and Ideal Final Result (IFR) visioning capabilities with simulation models developed in tools like RoboDK. It leverages predictive modeling and scenario planning to envision ideal operational states and uses backcasting to map strategic pathways to achieve these goals. TRIZ principles are applied to resolve inherent contradictions in system design, ensuring innovative solutions aligned with lean management objectives. The simulation phase, using real-world industrial scenarios, and reinforcement learning assesses the impact of lean strategies on production process resilience.

The pressing need for sustainable and efficient energy solutions has driven the exploration of hybrid systems combining biomass, photovoltaic, and other renewable sources. This study introduces the Cognitive Innovator Xcelerator (CIX), an advanced AI framework designed to optimize the design and integration of these complex systems. CIX employs a multi-faceted approach, integrating Intelligent Problem Mapping and ASIT Thinking Modules to analyze and address challenges in hybrid energy system design. By leveraging AI-driven idea generation and solution refinement, CIX facilitates a systematic exploration of potential design configurations. The application of CIX led to the identification of novel, efficient configurations for hybrid energy systems, surpassing conventional designs in energy output and sustainability metrics. Physical testing of the optimized design confirmed theoretical predictions, demonstrating significant improvements in energy efficiency and system integration. Through intelligent problem mapping and creative solution generation, CIX proves to be a powerful tool in advancing the development of sustainable hybrid energy systems.

The relentless pursuit of performance and perfection in manufactured parts drives us to adopt new manufacturing technologies. In the realm of composite parts, additive manufacturing offers highly attractive alternatives in terms of manufacturing costs and possibilities. In this study, we propose to explore this manufacturing process using a Markforged 3D printer, renowned for its expertise in 3D printing composite materials utilizing two extruders. The primary material investigated for the parts is Onyx, which will be reinforced with four types of fibers: carbon, glass, High-Strength High-Temperature (HSHT) glass, and Kevlar fibers. The overall objective is to propose a method for analyzing the results and experiments conducted through a multicriteria optimization approach. The proposed method aims to push the limits of optimization by applying inventive design methods, aiming to overcome the Pareto frontier defined by the experiments. The approach followed in the article is based on TRIZ. Solution concepts have been proposed to enhance the relevance of 3D printing.

This study explores a methodology for the topological optimization of car modules by integrating TRIZ (Theory of Inventive Problem Solving) and machine learning techniques. Initially, TRIZ principles guide the qualitative optimization phase, establishing proper design directions aimed at weight reduction and durability enhancement. Following this, machine learning tools, including ARRK’s proprietary algorithms, are applied for precise parametric optimization, ensuring alignment with performance criteria. The findings demonstrate the efficacy of this integrated approach, significantly improving car module design by refining geometrical proportions and achieving dual objectives: weight reduction and enhanced strength. While the study highlights the potential of combining TRIZ and machine learning, it acknowledges limitations due to the use of freely available 3D models and the proprietary nature of certain algorithms. Nonetheless, this research provides a comprehensive framework for automotive engineers and designers, setting a new benchmark for incorporating qualitative insights into the quantitative optimization of complex systems.

The Main Parameter of Value (MPV) is a vital factor in TRIZ’ Trend of Engineering System Evolution (TESE), helping to understand what customers like in specific products. The Trend of S-Curve Evolution in TESE shows how the MPV changes over time, guiding decisions for innovating products. In the current industrial landscape, the surge in industrial revolution is reshaping consumer choices towards sustainable products. Manufacturers vie for market dominance, continually seeking insights into customers’ preferences regarding MPVs for design forecasts and sustainable innovations. However, existing MPV analyses are primarily manual, lacking a computational model for predicting future MPVs. This study addresses this gap by proposing an AI-driven computational model for identifying potential MPVs. Utilizing the Python programming, the methodology incorporates Natural Language Processing (NLP) for feature extraction and Machine Learning algorithms, including Naïve Bayes and Linear Regression Classifier to achieve accurate means of predicting and understanding MPVs. The anticipated outcome is an automated S-Curve analysis derived from AI-analyzed MPV data. This visual representation of evolving MPVs over time is expected to empower manufacturers with insights for strategic decision-making and innovation in sustainable product development, fostering a competitive edge in the market.

Suffering earthquake (EQ) disasters, Japan has developed intensive networks of seismographs and achieved probabilistic long-/medium-term forecasting of big EQs. However, the great disaster EQs of 1995 and 2011 were never predicted. After these two EQs, Seismology Society of Japan and the government decided to avoid investigating the short-term prediction of EQs, considering it impossible. Earthquake Prediction Society of Japan has been challenging this issue since 2014 and has found a light very recently, especially by observing electromagnetic precursor phenomena.

The present paper first clarifies the requirements for the observation of precursor phenomena to be useful for a future short-term prediction alert system. Using these criteria, various research presentations are reviewed and three new presentations are found promising. Especially, M. Tsutsui observed the variation of DC electric field deep underground every second with high S/N ratio. The electric field generated by piezoelectric effect is transmitted through the earth’s crust without being disturbed by noise on the ground. In the case of EQ (on 2021/5/1, M6.8, 750 km away), the signal showed a drastic (±) variation for 46 min. 1.5 h before EQ, a sharp spike at the time of EQ, and a drastic variation for 68 min. 8.5 h after EQ. The data are excellent, showing the correlation with the EQ and containing much information about the EQ rupture process. This method is at the stage of single observation site, overcoming various practical difficulties. The present paper proposes to initiate a collaborative research project within EPSJ with several research groups and observation sites. The project will certainly step up to prove the correlation with EQs, find the method to predict EQs with 3 factors (where, when, and how large), and establish a system for short-term EQ prediction alert. The present approach is based on the TRIZ philosophy and experimental science in general and will serve Japan and all over the world.

This study introduces the Inventive Creation Score (SCI) Framework to address the gap in quantitatively evaluating the inventiveness of generative artificial intelligence (AI) systems. The SCI Framework combines dimensional analysis and expert validation to quantify key components of creativity—novelty, originality, functionality, and relevance—into a comprehensive evaluation model. Preliminary results indicate the framework effectively differentiates levels of inventiveness in AI-generated outputs, providing insights into optimizing AI for inventive tasks. Future research will refine the SCI Framework through broader empirical testing and feedback from diverse creative fields. The framework aims to guide developers in enhancing AI’s creative capabilities and assist practitioners in selecting appropriate AI tools. This research underscores AI’s potential as a collaborative partner in creativity, shifting perceptions from AI as an “automaton” to a creative collaborator. The SCI Framework offers a standardized method for evaluating AI’s inventiveness, supporting advancements in AI-driven innovation.

Since 2010s at business, digital transformation (DX) initiatives and digitalizing businesses have increased. The design of digital information systems is influenced by business and technological aspects and can have a considerable impact on the attractiveness of services. However, research on design science for DX or digitalizing business remains inadequate and not established. Meanwhile, the effectiveness of many methods established in the engineering field, including TRIZ that supports design and conceptual design, has been tested. The authors proposed “Creative and inventive Design Support System (CDSS)” in 2009 to support conceptual design in the engineering field based on TRIZ and suggested new methods “CDSSforDX” for applications in digital business and digital transformation (DX) in 2023. CDSSforDX is based on CDSS and has two additional processes, “strategic response” and “digital business strategy and DX strategy process,” as the strategy. In this paper, we present CDSSforDX methods and show the evaluation results and examples of using this methodology.

The purpose of this article is to present an AI technology based innovative approach, involved in a platform for digitizing processes in the Car Insurance Business field, which allows the end user (the broker of insurance or the insurer) to base his decisions on robust information to help him make a robust business forecast. Predictive analytics for insurance entails the use of special technology to sift through and analyze historical telematics data and consumer trends in effort to project future behavior. Obviously combining the AI based IT technologies with mathematical and statistical models, the integrated digitalized platform presented in this article involve also both Data Modeling and Deep Learning. Practically, the software platform presented in this article represents the backbone of any insurance brokerage business, because without such an application it is impossible to manage business processes that have hundreds or even thousands of sales agents. From structural point of view, this platform has a layered structure, the first layer being the basic brokerage application, this being extended with innovative predictive computational modules as upper layers. The technical implications mainly refers to the innovative way of involving in the digitalized system a massive amount of telematics data. The expected business implications consists in offering, by an innovative digitalized solution, the possibility for the final client (insurance broker or insurer) to receive information that will help him make a forecast of business.

The cosmetic industry is characterized by its dynamic nature, influenced by ever-changing consumer preferences and trends. In this context, accurate demand forecasting plays a pivotal role in optimizing the cosmetic supply chain. There is a lack of comprehensive research on the applicability and effectiveness of various demand forecasting techniques within the cosmetic supply chain, considering seasonality as a factor. This paper explores the existing literature on demand forecasting within the cosmetic industry, emphasizing the significance of predictive analytics and advanced forecasting models. Through a case study of real-world data on a cosmetic product, this research assesses the applicability and effectiveness of various forecasting algorithms using machine learning. This study provides a comprehensive understanding of the challenges faced by cosmetic supply chains in demand forecasting, identifies key factors influencing demand and their impact on forecasting accuracy, and evaluates the effectiveness of different forecasting techniques in the context of cosmetic products.

Segmenting clients according to common characteristics and determining the needs for each group is an important objective in the domain of car insurance. We perform segmentation through specific methods, comparing multiple clustering techniques, considering both classical and deep learning algorithms. Regarding the classical methods, clustering techniques appropriate for both numerical and categorical data were adopted, such as k-means clustering, X-means clustering, respectively k-prototype clustering. Regarding the deep-learning techniques, a stacked denoising autoencoder, followed by conventional clustering techniques, was experimented, the performance being compared with that achieved after the individual application of the classical techniques. After employing the clustering methods, the relevant attributes that separate among clusters were determined, the dependencies between the policy insurance type and other attributes being also analyzed through graphical representations and association rules. The experiments were performed considering the data extracted from a relational database specific for car insurance, containing 1000 instances for the main tables.

Flexible molds, with their excellent adaptability and reconfigurability, can meet the production needs of curved products with varying models, shapes, and sizes flexibly. In the design process of flexible molds, accurately determining the position and quantity of surface formation points is crucial. Therefore, this study innovatively introduces the construction of a Flexible Mold Surface Formation Neural Network model based on point cloud and neural network technologies, specifically for the extraction and determination of surface formation points in the design of flexible molds. Based on the TRIZ theory, we utilized the tools of local idealization and technical contradiction resolution to analyze the function and architecture of the Flexible Mold Surface Formation Network. And we analyzed the characteristics of the point cloud that are pertinent to flexible molds. Next, for the processing of the raw point cloud data, we introduced a lower edge point screening module based on the Support Vector Machine (SVM) and a filtering module. We then incorporated the PointNet++ network and made improvements to it, including the addition of a surface formation feature information integration module and the enhancement of the point cloud sampling method, thereby establishing a comprehensive neural network model dedicated to the surface formation of flexible molds. Finally, experimental data indicate that the method we have developed offers high precision and effectiveness, providing a practical theoretical foundation for the critical step of determining surface formation points in the design of flexible molds.

The study explores the cost implications of different product designs: modular, adaptive, and uniform, focusing on ideality metrics across three products with the same primary function. Through a systematic innovation toolkit that utilizes TRIZ and AI in the concept generation phase, this research conducts a comparative analysis of workshop cranes, assessing material use, assembly time, and reconfiguration time. Considering further input parameters, namely cost factors specific to the workshop and job distributions representing the overall market, allows us to determine the design of the highest ideality for the constellation. Each design has its domain of being the most ideal solution. The results offer insights into the practical applications of AI tools in modular product design and provide a systematic framework for optimizing cost-effectiveness and sustainability in product development. Moreover, exploring ideality and resource consumption being different among the designs contributes to developing more sustainable and adaptable manufacturing practices.

This study introduces an interdisciplinary prediction framework as part of a novel approach that integrates the Inventive Design Method (IDM), Topic Modeling, and Generative AI to foster innovation across academic fields. Identifying interdisciplinary connections is essential for solving complex, multi-domain problems. Our research uses a supervised machine learning classifier to identify interdisciplinary documents within the Semantic Scholar corpus, extracting latent insights. The Text Convolutional Neural Network model performed best, achieving an F1 score of 0.80. We find that approximately 25% of human knowledge is interdisciplinary. This framework helps create comprehensive knowledge maps across multiple domains, promoting innovation through effective cross-domain knowledge transfer.

A typical TRIZ project aimed to develop or improve a system consists of the problem analysis, solution generation and solution substantiation phases. The last phase may vary in approach and scope, ranging from expert assessment to physical prototypes and digital twins. Prototyping activities are often iterative, and the results achieved with one prototype are typically used to develop another. The order of modifications may significantly affect the total cost, time, and effort of prototyping, so adequately managing this process seems to be a vital challenge.

This paper proposes a systematic approach to prototyping navigation using TRIZ tools, such as Function Analysis and Value Analysis. If a new solution is to fail eventually during the substantiation, we would prefer it to fail fast and minimize the efforts required to obtain this result. Prototyping a successful solution, on the contrary, may benefit from a systematic approach by postponing the most significant investments to the latest stages when the design seems sufficiently reliable. The paper introduces the notions of prototyping attractiveness, uncertainty zone, and uncertainty time and provides simple guidelines for navigating the prototyping process, which are illustrated by a real-world example.

Cause-effect models used in TRIZ to identify key disadvantages of the analyzed systems employ AND and (explicit or implicit) OR operators to indicate how the causes trigger the effects. These fully defined logical operators imply disadvantage elimination strategies, i.e., removing any of the AND-connected causes vs. removing all OR-connected causes. On the contrary, the partially defined logical operators incur uncertainty about the trigger conditions due to the output values being undefined for some input combinations. This paper proposes a method of handling such operators to support decisions regarding disadvantage elimination, which may interest TRIZ practitioners and researchers.

The paper starts with recalling the basics of cause-effect analysis and introducing the notations for describing Boolean functions. The requirements and sample statistics concerning disadvantage descriptions are discussed in the second section, while the third section introduces partially defined operators. The following two sections present the proposed categorization of logical functions and the method for systematic selection of contributing causes to remove. The summary and ideas for further research are given in the last section of the paper.

With the planet facing escalating degradation due to mass production and increasing carbon emissions and waste, the buildings sector is at the core of innovation to build or optimize existing buildings, particularly in terms of adopting the most appropriate technologies, considering user behavior, and reducing carbon emissions. This research emphasizes the significance of a comprehensive and inventive approach in addressing the challenges of contemporary building sector practices. Using the Inventive De-sign Methodology (IDM), we propose a decision-support method for an evaluation tool for simulating and calculating the life-cycle costs, energy efficiency, and environmental impact of buildings. This tool is named NUKOSI RE. TRIZ is combined with the Design of Experiments Analysis to identify the most critical parameters of NUKOSI RE and avoid compromises between them. By better understanding the relationships (contradic-tions) among various parameters and recognizing the limitations of current models, we can help decision-makers pave the way for more effective and sustainable construction measures in the future.

In the contemporary landscape of technological advancement, artificial intelligence (AI) holds immense potential to address complex issues. This study aims at exploring AI’s applicability in the initial stages of inventive design processes, specifically focusing on the initial situation analysis and resolution phases. It emphasizes leveraging information retrieval techniques, for the efficient design of innovative lattice structures, a noteworthy industrial challenge encompassing mass production at low cost. The approach entails formulating a TRIZ based contradiction query in natural language and implementing an intricate information retrieval pipeline using a meticulously analyzing vast patent databases, as they epitomize a significant knowledge pool in the realm of technological innovations. This research underscores the nuanced challenges associated with optimizing retrieval pipelines to ensure reliable and contextually relevant data extraction. Ultimately culminating in the development of novel lattice structures, our findings accentuate new horizons in industrial production.

Achieving carbon neutrality by 2050 requires unprecedented technological, economic, and sociological changes. With time as a scarce resource, it is crucial to base decisions on relevant facts and information to avoid misdirection. This study aims to help decision makers quickly find relevant information related to companies and organizations in the renewable energy sector. Over the course of this PhD program, we will propose several text-mining methods applied to the renewable energy sector in order to detect technological breakthroughs and new, innovative companies. These techniques include specialized Named Entity Recognition (NER) models, news summarization, and trend analysis of scientific articles. Further steps in this project will contain a TRIZ-based analysis of scientific articles in order to attribute a multi-factor score on the innovative potential of novel technologies.

The continuing survival of Society depends on the existence of Truth. Innovation depends on Truth. The mismatch between AI technology, our Stone-Age brains and our Dark-Age institutions jeopardises the ability to identify Truth. It has become possible for small numbers of ‘bad actors’ to corrupt truth and create highly non-linear harm. At the same time, Truth is also dynamic. Facts have a half-life. Metaphorical truths, in the form of the ‘meaning of life’ work of artists, are also constantly evolving. The purpose of the paper is to examine how Society defines and uses dynamic Truth, and from there to conceptualise and demonstrate AI system algorithms capable of establishing context-appropriate, harm-eliminating, actionable ‘truth’. The primary purpose of such AI solutions will be to increase the success rate of innovation attempts. The secondary, albeit bigger, purpose is to try to mitigate the enormous risks that a failure to determine truth in a timely manner poses to Society at large.

Recently, LLMs pervaded the world of text understanding. Their ability to generate text based on instructions comes with the price of having an extremely large number of parameters and, consequently extremely large hardware demands or inference times. We consider that certain classification tasks, such as the detection of fixed lists of events from text can be achieved with acceptable performance even with smaller language models. The longstanding issue of collecting and annotating text is significantly eased by the presence of LLMs. Therefore, we employ a prompting technique to create a synthetic dataset of tweets annotated with events from the speaker’s life and train a small language model on it.

The issue addressed in this paper refers to the improvement of methods and tools for systematic innovation in the field of marketing management. The problem of Customer Experience (CE) and Customer Understanding in the area of new technologies in marketing of Big Data, Machine Learning, and Artificial Intelligence in considered. The main objective of this paper is to reflect the actual application of TRIZ in the problem-solving process related to new categories of marketing problems. The systematic review of the literature is conducted. The database of the publications, using the search terms related to Customer Experience and TRIZ and including sources of data such as Web of Science, is formulated. The bibliometric and content analysis of the selected subsets of most relevant publications is conducted. As the results, a set of problems raised in publications in which TRIZ is used in the area of the so-called customer driven quality management was identified. In the analyzed set of publications, TRIZ is integrated with tools supporting quality management, i.e., Failure Mode and Effect Analysis (FMEA), Quality Function Deployment (OFD), SERVQUAL, Kono Model, as well as Kansei engineering (KE) or Six Sigma and Lean Management concepts. The results of the research indicate that by integrating marketing elements included in quality management, it may be possible to solve current AI-based marketing management problems using specific TRIZ methods and tools.

When public services are provided to organizations and citizens in the most efficient way possible, favorable conditions are created that lead to increased trust in government and the strengthening of e-government. Investing in and using the capacity to integrate the IT systems that underpin these services is key. In this article, we discuss the notion of shared processes in organizations through the prism of information technology and interoperability. The term ‘process integration’ refers to the process of redesigning a service by evaluating and optimizing the workflow, pre- and post-service, of each element of the service. In terms of information technology solutions, data is frequently modeled from a single perspective; it is therefore, not possible to exchange and reuse data across multiple applications and processes. Adopting an interoperability framework is necessary to achieve this. This framework will include both theoretical and practical proposals that address all levels of system interconnection, including legal, organizational, semantic, and technical systems. Interoperability refers to the ability of organizations to work together, to exchange information and knowledge, and to use information technologies in the context of the work processes they support when working together. Relevant models for achieving information exchange between information systems in the local and area service provision sector are examined, with a particular focus on the influence, coordination, and contribution of an organization managing local and area road infrastructure.

The integration of Artificial Intelligence (AI) into the comprehensive project management field is revolutionizing the landscape of nowadays organizations. This paper explores the transformative role of AI in reshaping the responsibilities and functions of project managers. With AI’s capabilities to automate tasks, enhance data analysis, and improve risk assessment, there is a significant shift in traditional project management practices that potentially offers limitless opportunities for organizational enhancement.

This study specifically examines how AI is impacting project management across various dimensions including planning, execution, monitoring, and control. Utilizing both qualitative and quantitative data collected from a questionnaire distributed among professionals in different fields, the research analyzes the specific areas of project management that benefit most from AI technologies. The analysis aims to understand the practical impact of these technologies on project results and the evolution of the project manager role. The findings are expected to indicate a shift towards a more oversight-driven role where project managers are increasingly reliant on AI tools to handle operational tasks while they strategize and navigate complex project landscapes. This shift is reshaping the skill set required for successful project management, emphasizing the need for strong analytical skills and a deep understanding of AI capabilities and limitations. The empirical evidence gathered through this research will underscore the significant advantages that AI integration brings to project management across various industries. These include increased efficiency, improved accuracy in project tracking and forecasting, and enhanced risk management—all contributing to more successful project outcomes. However, the transition also necessitates a reevaluation of the project manager’s role, shifting from traditional task management to more strategic and analytical functions. This transformation, driven by AI, prepares practitioners for more dynamic and effective project management practices, ultimately leading to greater project success and organizational efficiency.

This article offers a methodological contribution for the standardization of engineering and systemic innovation with a view to supporting design teams in the development of eco-innovative products. We propose a unified methodology that will allow users to self-guide to create eco-innovative product concepts through the application of C-K theory combined with the TRIZ method while integrating the principles of the ASIT method. Firstly, understanding the links connecting the different sources of knowledge linked to the birth of eco-innovative concepts makes it possible to design one or more generic solutions. A simplified and adapted application of TRIZ will lead us to an eco-innovative solution. Thus, to refine the results, an AI algorithm will generate new solutions from the data and conditions recorded within the artificial intelligence.