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An approach to human–machine collaboration in innovation
Abstract
If a solvable problem is currently unsolved, then something important to a solution is most likely being overlooked. From this simple observation we derive the obscure features hypothesis: every innovative solution is built upon at least one commonly overlooked or new (i.e., obscure) feature of the problem. By using a new definition of a feature as an effect of an interaction, we are able to accomplish five things. First, we are able to determine where features come from and how to search for new ones. Second, we are able to construct mathematical arguments that the set of features of an object is not computably enumerable. Third, we are able to characterize innovative problem solving as looking for a series of interactions that produce the desired effects (i.e., the goal). Fourth, we are able to construct a precise problem-solving grammar that is both human and machine friendly. Fifth, we are able to devise a visual and verbal problem-solving representation that both humans and computers can contribute to as they help counteract each other's problem-solving weaknesses. We show how computers can counter some of the known cognitive obstacles to innovation that humans have. We also briefly discuss ways in which humans can return the favor. We conclude that a promising process for innovative problem solving is a human–computer collaboration in which each partner assists the other in unearthing the obscure features of a problem.
... Mccaffrey [56] developed Analogy Finder, a DbA support system to identify adaptable semantic analogies from the patent database. Later, Mccaffrey and Spector [57] devised a visual and verbal problem-solving representation to support human-machine collaboration in innovative design. Luo et al. [58] developed InnoGPS a cloud-based tool that employs an empirically-built interactive network map of all patent technology classes to guide the search for design inspiration (from patent texts) and innovation opportunities in different domains. ...
... In these human studies, full patent documents were often directly provided to designers as design aids such as prior art solutions [11,24,26,25,27,29,30], or design stimuli [15,16,17,20,28,19] for engineering design. Besides, a group of qualitative studies adopted knowledge-based rules or strategies to boost computer-aided engineering design and developed rule-based expert systems [43,31,54,68,42,47,66,33,48,57,45,62,28,13,46,41,12,35]. These expert systems usually require designers to collaborate with algorithms to tackle specific problems. ...
Patent data have been utilized for engineering design research for long because it contains massive amount of design information. Recent advances in artificial intelligence and data science present unprecedented opportunities to mine, analyse and make sense of patent data to develop design theory and methodology. Herein, we survey the patent-for-design literature by their contributions to design theories, methods, tools, and strategies, as well as different forms of patent data and various methods. Our review sheds light on promising future research directions for the field.
... Among the different kinds of data sources for design stimuli, the patent database is probably the biggest digitized design repository. Growing work on design-byanalogy has explored the patent database and proposed various vectorization approaches for associating patent documents as design stimuli of different analogical distances to given design problems [12][13][14]. However, most of these prior works only focused on text analysis and ignored visual information. ...
... Song et al. [37] located patent stimuli in home, near, and far-fields with the patent technology network built on the patent bibliographic information. McCaffrey and Spector [12] developed a system named "Analogy Finder" to search adaptable stimuli in the patent database by rephrasing the input design problem into verbs and synonyms. Sarica et al. [41] used word embedding models to train vector representations of technical terms (which represent technical functions, components, structures, and working mechanisms) from the patent title and the abstract, to guide the retrieval of technical concepts according to their distances in the vector space. ...
The patent database is often used by designers to search for inspirational stimuli for innovative design opportunities because of the large size, extensive variety and the massive quantity of design information contained in patent documents. Growing work on design-by-analogy has adopted various vectorization approaches for associating design documents. However, they only focused on text analysis and ignored visual information. Research in engineering design and cognitive psychology has shown that visual stimuli may benefit design-by-analogy. In this study, we focus on visual design stimuli and automatically derive the vector space and the design feature vectors representing design images. The automatic vectorization approach uses a novel convolutional neural network architecture named Dual-VGG aiming to accomplish two tasks: visual material type prediction and international patent classification (IPC) section-label predictions. The derived feature vectors that embed both visual characteristics and technology-related knowledge can be potentially utilized to guide the retrieval and use of near-field and far-field design stimuli according to their vector distances. We report the accuracy of the training tasks and also use a case study to demonstrate the advantages of design image retrievals based on our model.
... The existence of confounders provides rationale for instances when a correlation does not always imply a causal relationship. A variety of approaches have been developed to generate causal ordering in literature (Finkbeiner et al., 2015;McCaffrey and Spector, 2018;Bhatt et al., 2021). The method proposed in this article contributes to the body of knowledge in causal ordering using oriented graphs and equations. ...
Automatically extracting knowledge from small datasets with a valid causal ordering is a challenge for current state-of-the-art methods in machine learning. Extracting other type of knowledge is important but challenging for multiple engineering fields where data are scarce and difficult to collect. This research aims to address this problem by presenting a machine learning-based modeling framework leveraging the knowledge available in fundamental units of the variables recorded from data samples, to develop parsimonious, explainable, and graph-based simulation models during the early design stages. The developed approach is exemplified using an engineering design case study of a spherical body moving in a fluid. For the system of interest, two types of intricated models are generated by (1) using an automated selection of variables from datasets and (2) combining the automated extraction with supplementary knowledge about functions and dimensional homogeneity associated with the variables of the system. The effect of design, data, model, and simulation specifications on model fidelity are investigated. The study discusses the interrelationships between fidelity levels, variables, functions, and the available knowledge. The research contributes to the development of a fidelity measurement theory by presenting the premises of a standardized, modeling approach for transforming data into measurable level of fidelities for the produced models. This research shows that structured model building with a focus on model fidelity can support early design reasoning and decision making using for example the dimensional analysis conceptual modeling (DACM) framework.
... The concept of the adjacent possible has now disseminated into a wide range of research fields, including economy (Kauffman 2019), innovation studies (Hochberg et al. 2017;McCaffrey and Spector 2018), technological evolution (Tria et al. 2014), cultural evolution (Gravino et al. 2016), evolution of language (Ferrer-i-Cancho 2016), biosemiotics (Favareau 2015), artistic emergence (Hoelscher 2014), design studies (Ruttonsha 2017), recommender systems (Gravino et al. 2019), learning processes (Jörg 2017), conversation analysis (Favareau 2015), and psychotherapy (Cambray 2019). Some of these studies have already been mentioned. ...
Adjacent, i.e., nearby, possibilities constantly emerge in a multitude of settings for a multitude of entities. When these possibilities are explored, yet new possibilities emerge. The concept of the “adjacent possible” was introduced by Stuart Kauffman (1996; 2000) in evolutionary biology and complex adaptive systems to explain how biological evolution can be seen as exploration and actualization of what is adjacent possible, i.e., available at hand. The concept has now disseminated into a wide range of research fields including economy, innovation studies, technological evolution, cultural evolution, learning processes, recommender systems, and design studies. The “adjacent possible” can be defined as “the set of possibilities available to individuals, communities, institutions, organisms, productive processes, etc., at a given point in time during their evolution” (Loreto 2015, p. 9). The concept of the “adjacent possible” is useful for understanding how new possibilities emerge, and how they are constrained, discovered, explored, actualized, developed, reconfigured, designed, and so on, in an interplay between what is actual and what is possible for specific entities in specific settings. In this entry, the history and the state of art of research on the “adjacent possible” is briefly outlined. Discussion focuses on four essential aspects of “adjacent possibles”: (1) topologies of “adjacent possibles,” (2) types of “adjacent possibles,” (3) serendipity as actualization of “adjacent possibles,” and (4) designing for “adjacent possibles.”
... A human-machine teaming framework that guides AI development teams to create broadly adopted ethical AI systems that are usable, secure, and trustworthy is presented in [123]. In addition to this, major players, such as IBM [124], DeepMind [125], Google [126], and other academic institutions recently initiated a research effort to enhance human-machine collaboration [127][128][129]. ...
This paper presents the findings of detailed and comprehensive technical literature aimed at identifying the current and future research challenges of tactical autonomy. It discusses in great detail the current state-of-the-art powerful artificial intelligence (AI), machine learning (ML), and robot technologies, and their potential for developing safe and robust autonomous systems in the context of future military and defense applications. Additionally, we discuss some of the technical and operational critical challenges that arise when attempting to practically build fully autonomous systems for advanced military and defense applications. Our paper provides the state-of-the-art advanced AI methods available for tactical autonomy. To the best of our knowledge, this is the first work that addresses the important current trends, strategies, critical challenges, tactical complexities, and future research directions of tactical autonomy. We believe this work will greatly interest researchers and scientists from academia and the industry working in the field of robotics and the autonomous systems community. We hope this work encourages researchers across multiple disciplines of AI to explore the broader tactical autonomy domain. We also hope that our work serves as an essential step toward designing advanced AI and ML models with practical implications for real-world military and defense settings.
... Academic papers and patents usually represent original research outcomes or totally new inventions, which contain rich scientific and technological knowledge. Several attempts (Fu et al. 2013;He et al. 2019;McCaffrey and Spector 2018;Munoz and Tucker 2016;Sarica et al. 2020;Shi et al. 2017) have been made to apply the academic paper and patents to a design creativity task. However, one of the major limitations is that patents and scientific literature are restricted to only technological and scientific knowledge (Ernst 2003;Furukawa et al. 2015;Li et al. 2019;Shibata et al. 2008), while the nature of design tasks is of high diversity and complexity, with broad coverage of disciplines. ...
Data-driven design is a process to reuse data sources and provide valuable information to provoke creative ideas in the stages of design. However, existing semantic networks for design creativity are built on data sources restricted to technological and scientific information. Existing studies build the edges of a semantic network on statistical or semantic relationships, which are less likely to make full use of the benefits from both types of relationships and discover implicit knowledge for design creativity. Therefore, to overcome the gaps, we constructed WikiLink, a semantic network based on Wikipedia, which is an integrated source of general knowledge and specific knowledge, with broad coverage of disciplines. The weight in WikiLink fuses both the statistic and semantic weights between concepts instead of simply one type of weight, and four algorithms are developed for inspiring new ideas. Evaluation experiments are undertaken, and the results show that the network is characterised by high coverage of terms, relationships and disciplines, which demonstrates and supports the network’s effectiveness and usefulness. A demonstration and case study results indicate that WikiLink can serve as an idea generation tool for creativity in conceptual design. The source code of WikiLink and the backend data are provided open-source for more users to explore and develop.
... The vast amount of data are of high diversity, and can be reused as incentives and stimuli for new knowledge. Reusing existing knowledge to speed up the idea generation has already been used in the domain of design (Shi et al., 2017, Sarica et al., 2020, McCaffrey and Spector, 2018, Fu et al., 2013. A new concept -knowledge graph -is introduced to represent the knowledge in data in a new format. ...
This paper builds a patent-based knowledge graph, patent-KG, to represent the knowledge facts in patents for engineering design. The arising patent-KG approach proposes a new unsupervised mechanism to extract knowledge facts in a patent, by searching the attention graph in language models. The extracted entities are compared with other benchmarks in the criteria of recall rate. The result reaches the highest 0.8 recall rate in the standard list of mechanical engineering related technical terms, which means the highest coverage of engineering words.
... Modern day's complex challenges may be addressed by combining the strengths of humans and machines [17,39,55,83]. While attempts are made to mimic and replicate human thinking and actions by machines, there are either ethical or procedural reasons to keep humans in the loop or humans are simply more capable of performing the requested actions [52,79]. ...
... The work of college teachers is a kind of complex mental work that differs from that of primary and middle school teachers (Li, 2019;Kholbutaevna, 2021). Colleges must undertake complex and onerous teaching tasks, as well as more difficult and creative academic tasks (Mccaffrey and Spector, 2018;Park et al., 2021). At present, a series of reforms in the overall education system and management system has been conducted in China's higher education system to meet the needs of modern development, the internationalization of higher education, the concept of information-based learning and foreign advanced educational technology (Qian et al., 2018;Rogoza et al., 2018). ...
The purpose was to analyze the psychological changes of teaching staff in the academic management of local colleges, and briefly explore the role of teaching staff in the development of the social economy and colleges. In the environment of artificial intelligence and human–computer interaction (HCI), first, the relevant theories of teaching staffs’ psychological status and the characteristics of teaching staff in college academic management were analyzed and expounded. Next, the way of the questionnaire was selected to analyze the psychology of teaching staff in college academic management at different ages, professional titles, academic qualifications, disciplines, and teaching years. The results showed that the mental health level of college teachers was lower than the current national adult standard; the mental health level of female teachers in colleges was higher than that of male teachers; the p value of mental health of college teachers with different ages, professional titles, education, disciplines, and teaching years was greater than 0.05, indicating that there was no significant difference; the p -value of professional academic and mental health was less than 0.01, indicating that there was a significant correlation, that was, teachers’ professional academic exerted a significant impact on teachers’ mental health. In short, under the background of artificial intelligence and HCI’s rapid development, higher education was moving forward with high quality, and more attention should be paid to the psychological changes of college teaching staff.
... The knowledge base used is a hand-built semantic network based on the SBF (Structure-Behaviour-Function) modelling framework, which contains a limited amount of domain-specific knowledge regarding biological and engineering systems. Analogy Finder, developed by McCaffrey and Spector (2017), could retrieve adaptable analogues from the US patent database for solving problems. The US patent database contains useful technical knowledge from patents but was not in the form of semantic networks. ...
There have been growing uses of semantic networks in the past decade, such as leveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research. Therefore, the paper provides a survey of the research that has employed semantic networks in the engineering design research community. The survey reveals that engineering design researchers have primarily relied on WordNet, ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources to develop methods or tools for engineering design. Meanwhile, there are emerging efforts to mine large scale technical publication and patent databases to construct engineering-contextualized semantic network databases, e.g., B-Link and TechNet, to support NLP in engineering design. On this basis, we recommend future research directions for the construction and applications of engineering-related semantic networks in engineering design research and practice.
... The knowledge contained is mainly practical knowledge, which has limited the Retriever in constructing profoundly new ontologies for supporting idea generation. Analogy Finder (McCaffrey & Spector, 2017) provides users with adaptable analogous ideas for solving technical problems by conducting searches using the US patent database. However, the tool requires the users to have substantial expertise and knowledge to adapt the ideas retrieved from the US patent database employed for solving problems. ...
Purpose – to explore a crowdsourcing data-driven approach to construct crowdknowledge databases for innovation through supporting creative idea generation. In the approach, social media will be used as platforms to crowdsource knowledge for producing the databases.
Findings. Creativity is an essential element of innovation, but producing creative ideas is often challenging in design. Many computational tools have become available recently to support designers in producing creative ideas that are new to individuals. As a standard feature, most of the tools rely on the databases employed, such as ConceptNet and the US Patent Database. This study highlighted that the limitations of these databases have constrained the capabilities of the tools and, thereby, new computational databases supporting the generation of new ideas to a crowd or even history are needed. Crowdsourcing outsources tasks conventionally performed in-house to a crowd and uses external knowledge to solve problems and democratize innovation. Social media is often employed in crowdsourcing for a crowd to create and share knowledge.
Originality/value/scientific novelty of the research. This paper proposes a novel approach employing social media to crowdsource knowledge from a crowd for constructing crowd knowledge databases.
Practical importance of the research. The crowd knowledge database is expected to be used by the current computational tools to support designers producing highly creative ideas that are new to the crowd, in new product design, and ultimately to innovation.
Research limitations/Future research. In this study to provide insights and potential directions for future research are discussed that challenges of employing described approach.
Paper type – theoretical.
... The concept of the adjacent possible has now disseminated into a wide range of research fields, including economy (Kauffman 2019), innovation studies (Hochberg et al. 2017;McCaffrey and Spector 2018), technological evolution (Tria et al. 2014), cultural evolution (Gravino et al. 2016), evolution of language (Ferrer-i-Cancho 2016), biosemiotics (Favareau 2015), artistic emergence (Hoelscher 2014), design studies (Ruttonsha 2017), recommender systems (Gravino et al. 2019), learning processes (Jörg 2017), conversation analysis (Favareau 2015), and psychotherapy (Cambray 2019). Some of these studies have already been mentioned. ...
Adjacent, i.e., nearby, possibilities constantly emerge in a multitude of settings for a multitude of entities. When these possibilities are explored, yet new possibilities emerge. The concept of the “adjacent possible” was introduced by Stuart Kauffman (1996; 2000) in evolutionary biology and complex adaptive systems to explain how biological evolution can be seen as exploration and actualization of what is adjacent possible, i.e., available at hand. The concept has now disseminated into a wide range of research fields including economy, innovation studies, technological evolution, cultural evolution, learning processes, recommender systems, and design studies.
The “adjacent possible” can be defined as “the set of possibilities available to individuals, communities, institutions, organisms, productive processes, etc., at a given point in time during their evolution” (Loreto 2015, p. 9). The concept of the “adjacent possible” is useful for understanding how new possibilities emerge, and how they are constrained, discovered, explored, actualized, developed, reconfigured, designed, and so on, in an interplay between what is actual and what is possible for specific entities in specific settings.
In this entry, the history and the state of art of research on the “adjacent possible” is briefly outlined. Discussion focuses on four essential aspects of “adjacent possibles”: (1) topologies of “adjacent possibles,” (2) types of “adjacent possibles,” (3) serendipity as actualization of “adjacent possibles,” and (4) designing for “adjacent possibles.”
... The knowledge contained is mainly common-sense knowledge, which has limited the Retriever in constructing highly novel ontologies for supporting idea generation. Analogy Finder (McCaffrey & Spector, 2017) provide users with adaptable analogous ideas for solving technical problems by conducting searches using the US patent database. However, the tool requires the users to have strong expertise and knowledge to adapt the ideas retrieved from the US patent database employed for solving problems. ...
Creativity is an essential element of innovation, but producing creative ideas is often challenging in design. Many computational tools have been developed recently to support designers in producing creative ideas that are new to individuals. As a common feature, most of the tools rely on the databases employed, such as ConceptNet and the US Patent Database. However, the limitations of these databases have constrained the capabilities of the tools. Thereby, new computational databases for supporting the generation of ideas that are new to a crowd or even history are needed. Crowdsourcing outsources tasks conventionally performed in-house to a crowd and uses external knowledge to solve problems and democratize innovation. Social media is often employed in crowdsourcing for a crowd to create and share knowledge. A novel approach employing social media to crowdsource knowledge from a crowd for constructing crowd knowledge databases is proposed in this paper. The crowd knowledge database is expected to be used by the current computational tools to support designer producing highly creative ideas, which are new to the crowd, in new product design, and ultimately leading to innovation. Challenges of employing this approach are discussed to provide insights and potential directions for future research.
... The knowledge contained is mainly common-sense knowledge, which has limited the Retriever in constructing highly novel ontologies for supporting idea generation. Analogy Finder (McCaffrey & Spector, 2017) provide users with adaptable analogous ideas for solving technical problems by conducting searches using the US patent database. However, the tool requires the users to have strong expertise and knowledge to adapt the ideas retrieved from the US patent database employed for solving problems. ...
This paper investigates the influence of innovation management tools on product innovation in 84 Peruvian companies that received public funding to carry out innovation projects. The empirical exploratory study is based on a comprehensive questionnaire for collecting data and is analysed using a binary Probit method. The results indicate that although the use of tools is scarce in Peruvian companies, innovation management tools influence product innovation. Furthermore, all the evidence shows that innovation management is important, and therefore the innovation process must be structured and systematized.
... A more recent effort, PQE [58], tries to model user curiosity and creativity, and tries to come up with design alternatives the user might not have thought of, with the goal of helping the user think out of the box. Another recent work is [59], where a process for innovative problem solving is proposed based on the human and computer assisting each other in the identification and characterization of the obscure features of a problem. Finally, ESA's Design Engineering Assistant (DEA) [60] introduces a VA for concurrent engineering processes that helps engineers by providing data aggregation and synthesis capabilities of past unstructured documentation in an automated manner. ...
This article describes Daphne, a virtual assistant for designing Earth observation distributed spacecraft missions. It is, to the best of our knowledge, the first virtual assistant for such application. The article provides a thorough description of Daphne, including its question answering system and the main features we have implemented to help system engineers design distributed spacecraft missions. In addition, the article describes a study performed at NASA's Jet Propulsion Laboratory (JPL) to assess the usefulness of Daphne in this use case. The study was conducted with
N
= 9 subjects from JPL, who were asked to work on a mission design task with two versions of Daphne, one that was fully featured implementing the cognitive assistance functions, and one that only had the features one would find in a traditional design space exploration tool. After the task, they filled out a standard user experience survey, completed a test to assess how much they learned about the task, and were asked a number of questions in a semi-structured exit interview. Results of the study suggest that Daphne can help improve performance during system design tasks compared to traditional tools, while keeping the system usable. However, the study also raises some concerns with respect to a potential reduction in human learning due to the use of the cognitive assistant. The article ends with a list of suggestions for future development of virtual assistants for space mission design.
... In comparison with other computational analogy tools, such as the WordTree method (Linsey et al., 2012) andAnalogy Finder (McCaffrey andSpector, 2017), the Retriever can provide image-based stimuli or outcomes in addition to text-based outcomes. Image-based ideas or entities in mood board styles could support the comprehension of text-based outcomes, as well as improve creativity and enhance design communication. ...
Analogy is a core cognition process used to produce inferences as well as new ideas using previous knowledge and experience. Ontology is a formal representation of a set of domain concepts and their relationships. The use of analogy and ontology in design activities to support design creativity have previously been explored. This paper explores an approach to construct ontologies with sufficient richness and coverage to support reasoning over real-world datasets for prompting creative idea generation. This approach has been implemented into a computational tool for assisting designers in generating creative ideas during the early stages of design. The tool, called “the Retriever”, has been developed based on ontology by embracing the aspects of analogical reasoning. A case study has indicated that the tool can be effective and useful for idea generation. The results have indicated that the tool, in its current formulation, can significantly improve the fluency and flexibility of idea generation and the usefulness of ideas, as well as slightly increase the originality of ideas, for the case study concerned.
... Design in architecture and related fields has always required balancing aesthetic, technical, functional, economical, and other concerns, with different priorities taking precedence depending on the building and its context. Increasingly, computers are able to help analyze how a design is performing, navigate the relationships between different design goals, or even overcome human cognitive obstacles (McCaffrey & Spector, 2017). Despite these advances in how design priorities are pursued, fundamental aspects of design remain synthetic, creative, and human. ...
To be useful for architects and related designers searching for creative, expressive forms, performance-based digital tools must generate a diverse range of design solutions. This gives the designer flexibility to choose from a number of high-performing designs based on aesthetic preferences or other priorities. However, there is no single established method for measuring diversity in the context of computational design, especially in the field of architecture. This paper explores different metrics for quantifying diversity in parametric design, which is an increasingly common digital approach to early-stage exploration, and tests how human users perceive these diversity measurements. It first provides a review of existing methodologies for measuring diversity and describes how they can be adapted for parametrically formulated design spaces. This paper then tests how these different metrics align with human perception of design diversity through an online visual survey. Finally, it offers a quantitative comparison between the different methods and a discussion of their attributes and potential applications. In general, the comparison indicates that at the level of diversity difference that becomes visually meaningful to humans, the measurable difference between metrics is small. This paper informs future researchers, developers, and designers about the measurement of diversity in parametric design, and can stimulate further studies into the perception of diversity within sets of design options, as well as new design methodologies that combine architectural novelty and performance.
This chapter examines the implications of extending the concept of industry 5.0 from single organizations to supply networks. It analyzes the first and principal component of the supply network 5.0 framework: the human-automation-machine collaboration. The aim is to adequately address the most prominent elements of the supply network 5.0 framework and provide real-world evidence of the economic and organizational benefits these instruments deliver to organizations. It defines several possible models, each one with its benefits and challenges. It considers people management in a supply network 5.0, their characteristics, and the kind of selection, training, and support they need.KeywordsHuman automationHuman automation machine collaborationPeople managementInter-collaboration cognitionTemporal collaborationEducation 5.0Augmentation theory
Robotic construction is a powerful means of addressing labor shortages, low productivity, and low sustainability in the construction industry. Even though construction robots have attracted attention in research and practice, in a market condition where the technology and industry scale of the construction industry is yet unable to meet the scale of full automation, human–robot interaction (HRI) is a more adaptable working model. It is crucial to change the level of automation to the level of cooperation. This paper proposes an HRI construction method that aims to provide a new idea for existing robotic construction research, which combines the advantages of manual construction and automated robotic construction. This construction method allows an inexperienced layman to quickly complete the construction of complex timber structures with the assistance of a robot. Furthermore, this automated construction method's advantages, limitations, and potential pitfalls and the environmental, economic, and social sustainability aspects of design, production, and construction are also considered, providing a technical reference for the sustainable development of China's construction industry.
Patent data have been utilized for engineering design research for long because of its large and expanding size, wide variety, and massive amount of design information contained in patents. Recent advances in artificial intelligence and data science present unprecedented opportunities to develop data-driven design methods and tools and to enable advanced understanding of design science based on the patent database. Herein, we survey the patent-for-design literature categorized by their contributions to design research and practice, including design theories, methods, tools, and strategies, as well as the forms of patent data and the data science methods in respective studies. Based on systematic review and analysis, our review sheds light on promising future research directions for the field.
ABSTRACT
The government is committed to improving the health system by providing universal coverage to all
South Africans as articulated in national health policies. The biggest threat facing the health sector
today is the shortage of well-trained healthcare workers and the increasing demand for healthcare
services. A quantitative study was used to examine the role of task-shifting as response to human
resource crisis facing the Ngwelezana Tertiary Hospital in KwaZulu-Natal. Data was analysed
using descriptive statistics, chi-square tests of association and the Cramer’s V test. The results
show that task-shifting was adopted to address staff shortages, delays in serving patients, long
waiting periods for patients, increased risks of error and patient mortality. However, task-shifting
presented its own challenges such as legal and professional risks and staff morale issues. The
paper concludes that task shifting should be used as a relief measure for reducing the impact of
staff shortages in hospitals.
Patent data have been utilized for engineering design research for long because it contains massive amount of design information. Recent advances in artificial intelligence and data science present unprecedented opportunities to mine, analyse and make sense of patent data to develop design theory and methodology. Herein, we survey the patent-for-design literature by their contributions to design theories, methods, tools, and strategies, as well as different forms of patent data and various methods. Our review sheds light on promising future research directions for the field.
Procuring and Disposing Entities (PDEs) in state departments are challenged with missing records, inadequate training, poor enforcement mechanisms, limited funding and poor reports. Taking into account the huge government investment into public contracts, research to understand factors enabling contract compliance is imperative. This study explored the enabling factors for contract compliance in PDEs in Uganda. A qualitative study of twenty-nine telephone interviews and focus group discussions via Zoom were conducted. Thematic and content analysis were used to identify the fourteen major themes. Findings suggest capacity building, funding, reporting, evaluation, contract management, records management, institutional structure, enforcement, legal and regulatory framework, communication, openness, compliance checks, ethical orientation and stakeholder involvement as enablers for a suitable contract compliance environment in state departments in Uganda. The proposed conceptual framework serves as a valuable resource for researchers and practitioners especially those involved in public procurement.
In the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design.
There have been growing uses of semantic networks in the past decade, such as leveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research. Therefore, the paper provides a survey of the research that has employed semantic networks in the engineering design research community. The survey reveals that engineering design researchers have primarily relied on WordNet, ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources to develop methods or tools for engineering design. Meanwhile, there are emerging efforts to mine large scale technical publication and patent databases to construct engineering-contextualized semantic network databases, e.g., B-Link and TechNet, to support NLP in engineering design. On this basis, we recommend future research directions for the construction and applications of engineering-related semantic networks in engineering design research and practice.
Employers need engineers capable of leveraging CFD simulations to make intelligent design decisions, but undergraduate computational fluid dynamics (CFD) courses are not adequately preparing students for this type of work. CFD courses commonly familiarize students with topics, such as method derivation, domain creation, boundary conditions, mesh convergence, turbulence models, numerical convergence, and error analysis. This approach is an effective way to teach novices how CFD software works and how to prepare CFD analyses. However, it neglects development of higher level CFD skills and intuition important to engineering analysis and design, deferring this task to future study and training. This paper introduces the “Machine Learning Driven Interpretation of Fluid Dynamics Simulations to Develop Student Intuition” (MIFoS) software, a program designed to help CFD novices develop the high‐level skills and intuition that employers need in their engineers. A data‐driven approach was used to create the MIFoS software, which allows the submission of arbitrary geometries, automates an external flow simulation, and returns expert‐level graphical interpretation of simulation data. MIFoS's automated CFD simulation and feedback space allows novices to experiment with expert‐level suggestions on their own designs, enabling the skill and intuition development typically gained through years of study, practice, and expert guidance.
Creativity is the generation of an idea or artifact judged to be novel and high-quality by a knowledgeable social group, and is often said to be the pinnacle of intelligence. Several computational creativity systems of various designs are now being demonstrated and deployed. These myriad design possibilities raise the natural question: are there fundamental limits to creativity? Here we define a mathematical abstraction to capture key aspects of combinatorial creativity and study fundamental trade-offs between novelty and quality. The functional form of this fundamental limit resembles the capacity-cost relationship in information theory, especially when measuring novelty using Bayesian surprise—the relative entropy between the empirical distribution of an inspiration set and that set updated with the new idea or artifact. As such, we show how information geometry techniques provide insight into the limits of creativity and find that the maturity of the creative domain directly parameterizes the fundamental limit. This result is extended to the case when there is a diverse audience for creativity and when the quality function is not known but must be estimated from samples.
This paper reports on a series of experiments which were conducted to test the hypothesis that design fixation, defined as a blind adherence to a set of ideas or concepts limiting the output of conceptual design, is a measurable barrier in the conceptual design process. The results of the experiments clearly demonstrate the existence of design fixation. The paper related issues such as the nature of the phenomenon, some experimental issues which arise in such investigations, and directions for future research.
A new cognitive theory of innovation, the Obscure Features Hypothesis (OFH), is applied to the area of engineering design innovation. The OFH states that all innovative solutions are built upon at least one overlooked (i.e., obscure) feature of the problem at hand. In this paper, we first highlight the types of features that exist and the cognitive obstacles that hinder people's ability to notice the obscure members of various feature types. We then detail five innovation techniques we have developed to help designers search the obscure realms of the space of features. Each of these techniques counteracts a specific cognitive obstacle to innovation: design fixation, functional fixedness, narrow verb associations, assumption blindness, and analogy blindness. We compare our approach with other approaches to innovation in psychology (the representation change view and the distant association view) and engineering (theory of inventive problem solving and C–K theory). Finally, we show how the innovation techniques can be implemented in software to assist users in the design process.
The Obscure Features Hypothesis (OFH) for innovation states that a two-step process undergirds almost all innovative solutions: (1) notice an infrequently observed or new (i.e., obscure) feature of the problem and (2) construct an interaction involving the obscure feature that produces the desired effects to solve the problem. The OFH leads to a systematic derivation of innovation-enhancing techniques by engaging in two tasks. First, we developed a 32-category system of the types of features possessable by a physical object or material. This Feature Type Taxonomy (FTT) provides a panoramic view of the space of features and assists in searches for the obscure ones. Second, we are articulating the many cognitive reasons that obscure features are overlooked and are developing countering techniques for each known reason. We present the implications and techniques of the OFH, as well as indicate how software can assist innovators in the effective use of these innovation-enhancing techniques.
A new cognitive theory of innovation, the Obscure Features Hypothesis (OFH), states that almost all inno-vative solutions result from two steps: (1) noticing a rarely noticed or never-before noticed (i.e., obscure) feature of the problem's elements, and (2) building a solution based on that obscure feature (McCaffrey 2011). Structural properties of the human semantic network make it possible to locate useful obscure fea-tures with a high probability. Innovation Assistant (IA) software interactively guides human users to the most likely obscure features for solving the problem at hand.
One of the most famous measurements in the history of 20th-century astronomy was made over the course of several months in 1919. Teams of observers from the Greenwich and Cambridge observatories in the UK traveled to Brazil and western Africa to observe a total solar eclipse that took place on 29 May 1919. Their aim was to establish whether the paths of light rays were deflected in passing through the strong gravitational field of the Sun. Their observations were subsequently presented as establishing the soundness of general relativity; that is, the observations were more consistent with the predictions of the new gravitational theory developed by Albert Einstein than with the traditional Newtonian theory.
Insight problem solving is characterized by impasses, states of mind in which the thinker does not know what to do next. The authors hypothesized that impasses are broken by changing the problem representation, and 2 hypothetical mechanisms for representational change are described: the relaxation of constraints on the solution and the decomposition of perceptual chunks. These 2 mechanisms generate specific predictions about the relative difficulty of individual problems and about differential transfer effects. The predictions were tested in 4 experiments using matchstick arithmetic problems. The results were consistent with the predictions. Representational change is a more powerful explanation for insight than alternative hypotheses, if the hypothesized change processes are specified in detail. Overcoming impasses in insight is a special case of the general need to override the imperatives of past experience in the face of novel conditions. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This paper extends the function–behaviour–structure (FBS) framework, which proposed eight fundamental processes involved in designing. That framework did not explicitly account for the dynamic character of the context in which designing takes place, described by the notion of situatedness. This paper describes this concept as a recursive interrelationship between different environments, which, together with a model of constructive memory, provides the foundation of a situated FBS framework. The eight fundamental processes are then reconstructed within this new framework to represent designing in a dynamic world.
A recent analysis of real-world problems that led to historic inventions and insight problems that are used in psychology experiments suggests that during innovative problem solving, individuals discover at least one infrequently noticed or new (i.e., obscure) feature of the problem that can be used to reach a solution. This observation suggests that research uncovering aspects of the human semantic, perceptual, and motor systems that inhibit the noticing of obscure features would enable researchers to identify effective techniques to overcome those obstacles. As a critical step in this research program, this study showed that the generic-parts technique can help people unearth the types of obscure features that can be used to overcome functional fixedness, which is a classic inhibitor to problem solving. Subjects trained on this technique solved on average 67% more problems than a control group did. By devising techniques that facilitate the noticing of obscure features in order to overcome impediments to problem solving (e.g., design fixation), researchers can systematically create a tool kit of innovation-enhancing techniques.
(abridged) Predictions of the Concordance Cosmological Model (CCM) of the
structures in the environment of large spiral galaxies are compared with
observed properties of Local Group galaxies. Five new most probably
irreconcilable problems are uncovered. However, the Local Group properties
provide hints that may lead to a solution of the above problems The DoS and
bulge--satellite correlation suggest that dissipational events forming bulges
are related to the processes forming phase-space correlated satellite
populations. Such events are well known to occur since in galaxy encounters
energy and angular momentum are expelled in the form of tidal tails, which can
fragment to form populations of tidal-dwarf galaxies (TDGs) and associated star
clusters. If Local Group satellite galaxies are to be interpreted as TDGs then
the sub-structure predictions of CCM are internally in conflict. All findings
thus suggest that the CCM does not account for the Local Group observations and
that therefore existing as well as new viable alternatives have to be further
explored. These are discussed and natural solutions for the above problems
emerge.
This article commences with an elaboration of models of design as a process. It then introduces and describes a knowledge representation schema for design called design prototypes. This schema supports the initiation and continuation of the act of designing. Design prototypes are shown to provide a suitable framework to distinguish routine, innovative and creative design
According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individuals existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction.
Synchronized oscillators are ubiquitous in nature, and synchronization plays a key part in various classical and quantum phenomena. Several experiments have shown that in thin superconducting films, disorder enforces the droplet-like electronic texture--superconducting islands immersed into a normal matrix--and that tuning disorder drives the system from superconducting to insulating behaviour. In the vicinity of the transition, a distinct state forms: a Cooper-pair insulator, with thermally activated conductivity. It results from synchronization of the phase of the superconducting order parameter at the islands across the whole system. Here we show that at a certain finite temperature, a Cooper--air insulator undergoes a transition to a superinsulating state with infinite resistance. We present experimental evidence of this transition in titanium nitride films and show that the superinsulating state is dual to the superconducting state: it is destroyed by a sufficiently strong critical magnetic field, and breaks down at some critical voltage that is analogous to the critical current in superconductors.
Inventors rely a lot on happenstance. Can computers help us make our own luck? Paul Marks investigates.
The am program was constructed by Lenat in 1975 as an early experiment in getting machines to learn by discovery. In the preceding article in this issue of the AI Journal, Ritchie and Hanna focus on that work as they raise several fundamental questions about the methodology of artificial intelligence research. Part of this paper is a response to the specific points they make. It is seen that the difficulties they cite fall into four categories, the most serious of which are omitted heuristics, and the most common of which are miscommunications. Their considerations, and our post-am work on machines that learn, have clarified why am succeeded in the first place, and why it was so difficult to use the same paradigm to discover new heuristics. Those recent insights spawn questions about “where the meaning really resides” in the concepts discovered by am. This in turn leads to an appreciation of the crucial and unique role of representation in theory formation, specifically the benefits of having syntax mirror semantics. Some criticism of the paradigm of this work arises due to the ad hoc nature of many pieces of the work; at the end of this article we examine how this very adhocracy may be a potential source of power in itself.
To study productive thinking where it is most conspicuous in great achievements is certainly a temptation, and without a doubt, important information about the genesis of productive thought could be found in biographical material. A problem arises when a living creature has a goal but does not know how this goal is to be reached. Whenever one cannot go from the given situation to the desired situation simply by action, then there has to be recourse to thinking. The subjects ( S s), who were mostly students of universities or of colleges, were given various thinking problems, with the request that they think aloud. This instruction, "Think aloud", is not identical with the instruction to introspect which has been common in experiments on thought-processes. While the introspecter makes himself as thinking the object of his attention, the subject who is thinking aloud remains immediately directed to the problem, so to speak allowing his activity to become verbal. It is the shift of function of the components of a complex mathematical pattern—a shift which must so often occur if a certain structure is to be recognized in a given pattern—it is this restructuration, more precisely: this transformation of function within a system, which causes more or less difficulty for thinking, as one individual or another tries to find a mathematical proof.
We use our Modified Newtonian Dynamics (MOND) cosmological particle-mesh
N-body code to investigate the feasibility of structure formation in a
framework involving MOND and light sterile neutrinos in the mass range 11 - 300
eV: always assuming that \Omega_{\nu_s}=0.225 for H_o=72 \kms Mpc^{-1}. We run
a suite of simulations with variants on the expansion history, cosmological
variation of the MOND acceleration constant, different normalisations of the
power spectrum of the initial perturbations and interpolating functions. Using
various box sizes, but typically with ones of length 256 Mpc/h, we compare our
simulated halo mass functions with observed cluster mass functions and show
that (i) the sterile neutrino mass must be larger than 30 eV to account for the
low mass (M_{200}<10^{14.6} solar masses) clusters of galaxies in MOND and (ii)
regardless of sterile neutrino mass or any of the variations we mentioned
above, it is not possible to form the correct number of high mass
(M_{200}>10^{15.1} solar masses) clusters of galaxies: there is always a
considerable over production. This means that the ansatz of considering the
weak-field limit of MOND together with a component of light sterile neutrinos
to form structure from z ~ 200 fails. If MOND is the correct description of
weak-field gravitational dynamics, it could mean that subtle effects of the
additional fields in covariant theories of MOND render the ansatz inaccurate,
or that the gravity generated by light sterile neutrinos (or by similar hot
dark matter particles) is different from that generated by the baryons.
The observational evidence for dark matter is examined, reviewing the
results of recent investigations. A historical introduction to the
problem is presented, and consideration is given to single galaxies (the
Galaxy; other spiral galaxies, elliptical galaxies, and dwarf galaxies);
galaxies in binaries and small groups; and rich clusters, superclusters,
and global aspects. Also discussed are observational constraints on the
nature of dark matter, including easily excludable objects, the
nucleosynthesis problem for baryons, brown and white dwarfs and black
holes, nonbaryonic dark matter, and detection problems.
In the last few years, “Design Thinking” has gained popularity – it is now seen as an exciting new paradigm for dealing with problems in sectors as far a field as IT, Business, Education and Medicine. This potential success challenges the design research community to provide unambiguous answers to two key questions: “What is the core of Design Thinking?” and “What could it bring to practitioners and organisations in other fields?”. We sketch a partial answer by considering the fundamental reasoning pattern behind design, and then looking at the core design practices of framing and frame creation. The paper ends with an exploration of the way in which these core design practices can be adopted for organisational problem solving and innovation.Highlights► Analysing design reasoning in terms of abduction. ► Presenting a framework for the description of design practices. ► Investigating the creation of new frames as a key design practice. ► Describing the different levels on which design (framing) practices can impact organisations.
In engineering design, all products and artifacts have some intended reason behind their existence: the product or artifact
function. Functional modeling provides an abstract, yet direct, method for understanding and representing an overall product
or artifact function. Functional modeling also strategically guides design activities such as problem decomposition, physical
modeling, product architecting, concept generation, and team organization. A formal function representation is needed to support
functional modeling, and a standardized set of function-related terminology leads to repeatable and meaningful results from
such a representation. We refer to this representation as a functional basis; in this paper, we seek to reconcile and integrate two independent research efforts into a significantly evolved functional
basis. These efforts include research from the National Institute of Standards and Technology and two US universities, and
their industrial partners. The overall approach for integrating the functional representations and the final results are presented.
This approach also provides a mechanism for evaluating whether future revisions are needed to the functional basis and, if
so, how to proceed. The integration process is discussed relative to differences, similarities, insights into the representations,
and product validation. Based on the results, a more versatile and comprehensive design vocabulary emerges. This vocabulary
will greatly enhance and expand the frontiers of research in design repositories, product architecture, design synthesis,
and general product modeling.
Research in feature-based design is reviewed. Feature-based design is regarded as a key factor towards CAD/CAPP integration from a process planning point of view. From a design point of view, feature-based design offers possibilities for supporting the design process better than current CAD systems do. The evolution of feature definitions is briefly discussed. Features and their role in the design process and as representatives of design-objects and design-object knowledge are discussed. The main research issues related to feature-based design are outlined. These are: feature representation, features and tolerances, feature validation, multiple viewpoints towards features, features and standardization, and features and languages. An overview of some academic feature-based design systems is provided. Future research issues in feature-based design are outlined. The conclusion is that feature-based design is still in its infancy, and that more research is needed for a better support of the design process and better integration with manufacturing, although major advances have already been made.
Analysis of over 1,000 innovative inventions reveals that during the innovative process at least one rarely-noticed or new (i.e., obscure) feature is unearthed and built upon to create the solution (i.e., the Obscure Features Hypothesis for innovation: OFH) [6, 7]. Embedding the insights from this analysis into the structure of semantic networks creates AhaNets, which help optimize the search for the needed key obscure feature. Techniques to overcome cognitive aversions to noticing the obscure (i.e., fixation effects) further enhance innovation by improving the search process. Once implemented in software, AhaNets and counter-fixation techniques create an innovation-enhancing human-machine interaction.
Much artificial intelligence research is based on the construction of large impressive-looking programs, the theoretical content of which may not always be clearly stated. This is unproductive from the point of view of building a stable base for further research. We illustrate this problem by referring to Lenat's AM program, in which the techniques employed are somewhat obscure in spite of the impressive performance.
This book provides the foundation for understanding the theory and pracitce of compilers.
Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet ¹ provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].
On 10 July 1908, in his laboratory at Leiden University, the great Dutch physicist Heike Kamerlingh Onnes (1853-1926) experienced the most glorious moment of his career. That was the day he first liquefied helium and thus opened an entirely new chapter in low-temperature physics. (See the article in PHYSICS TODAY, March 2008, page 36.)
Traducción de: Über die spezielle und die allgemeine Relativitätstheorie
The intent of this paper is the presentation of an associative interpretation of the process of creative thinking. The explanation is not directed to any specific field of application such as art or science but attempts to delineate processes that underlie all creative thought. The discussion will take the following form, (a) First, we will define creative thinking in associative terms and indicate three ways in which creative solutions may be achieved—serendipity, similarity, and mediation, (b) This definition will allow us to deduce those individual difference variables which will facilitate creative performance, (c) Consideration of the definition of the creative process has suggested an operational statement of the definition in the form of a test. The test will be briefly described along with some preliminary research results. (d) The paper will conclude with a discussion of predictions regarding the influence of certain experimentally manipulable variables upon the creative process. Creative individuals and the processes by which they manifest their creativity have excited a good deal of
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An updated historical profile of the Higgs boson
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Exoplanets: Detection, Formation, Properties, Habitability
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Cahoots: a software platform for enhancing innovation and facilitating situation transfer
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