Capturing Values at the Boundaries

Novelty is not a sufficient condition for innovation. For new ideas and products to succeed, they must be integrated into the collective understanding and existing infrastructure, illustrating how the past determines the future. Here, we develop a comprehensive framework to understand how the structure of accumulated past successes curves the adjacent possible trajectory of future innovations. We observe that certain technological building blocks, upon frequent combination, coalesce into noticeable clusters manifested as well-defined domains within the exploration landscape. These clusters compress the space around them, thus bending the trajectory of exploration towards them as if exerting a gravitational pull on new ideas and actions. Our methodology quantifies this effect, mapping out the curvatures within the adjacent possible space of actions and identifying significant curvatures that define the boundaries of consensus domains. These domains, serving as knowledge repertoire, guide inventors towards proven solutions and past successes, explaining why the most commercially successful inventions often emerge at the fringes of established domains. Through a case study of Edison's patents, we demonstrate his well-known design strategy of leveraging institutionalized domains, manifested as high curvature in this space. In contrast, Tesla's inventions are predominantly located in low-curvature areas. Our further analysis reveals that innovations in areas of high curvature are indeed more likely to capture market values, supporting our observations. Our framework provides insights into how new ideas interact with and evolve alongside established structures in institutional frameworks and collective understanding, illustrating the complex dialogue between innovation and convention.Comment: 59 pages, 5 main figures, 13 supplementary figure

Automatic Patent Clustering using SOM and Bibliographic Coupling

Patents are usually organized in classes generated by the offices responsible for patents protection, to create a useful format to the information retrieval process. The complexity of patent taxonomies is a challenge for the automation of patent classification. Beside this, the high numbers of subgroups makes the classification in deeper levels more difficult. This work proposes a method to cluster patents using Self Organizing Maps (SOM) networks and bibliographic coupling. To validate the proposed method, an empirical experiment used a patent database from a specific classification system. The obtained results show that patents clusters were successfully identified by SOM through their cited references, and that SOM results were similar to k-Means algorithm results to perform this task. This study can contribute to the development of the knowledge organization systems by evaluating the use of citation analysis in the automatic clustering of patents in a constrained knowledge domain, at the subgroup level of current patent classification systems

Mapping the Metaverse – Knowledge Generation Structures in a Nascent Ecosystem

The so called Metaverse is among the most popular emerging themes in contemporary business and academic discourses related to digital transformation. Beside the hype and buzz around the topic, recent research has established important progress related to the technical foundations and use-case scenarios. However, the preconditions to manifest these promising potentials, that is the underlying socio-technical ecosystem, is, to date, not well-understood. In particular, we still do not know much about the key building blocks of technical knowledge and who creates this knowledge base necessary for the Metaverse. Therefore, in this study, employing cluster-analysis to a complex dataset involving 2297 Metaverse-patents, we reveal key areas of technological knowledge as well as important actor groups shaping the development of the knowledge base. Building upon these inductively derived knowledge generation structures, we discuss important implications for managers and academics and propose an Information Systems research agenda to further explore the emerging Metaverse ecosystem

Patent Database: Their Importance in Prior Art Documentation and Patent Search

In knowledge based economies the nation’s economic status depends on the production, distribution and use of knowledge and information. The recent trend in the economic growth of nations is mainly determined by innovative technological knowhow of the individuals. Intellectual property has gained attention in this era of knowledge. The vast amount of data generated through the application of intellectual assets is managed with the help of various in- silico tools. In recent days, the patent databases have gained importance due to the detailed information available on the granted patent and other details, such as, legal status of the patent applications, which are not available through any other literature search. This review paper attempts to describe different types of patent databases available, their unique features, strengths, weakness and their major purpose. This paper details the information on how to access a patent database, the relevance of patent information obtained from these databases in prior art search, patent analysis, and the drawbacks present in these patent databases

Long-run dynamics of the U.S. patent classification system

Almost by definition, radical innovations create a need to revise existing classification systems. In this paper, we argue that classification system changes and patent reclassification are common and reveal interesting information about technological evolution. To support our argument, we present three sets of findings regarding classification volatility in the U.S. patent classification system. First, we study the evolution of the number of distinct classes. Reconstructed time series based on the current classification scheme are very different from historical data. This suggests that using the current classification to analyze the past produces a distorted view of the evolution of the system. Second, we study the relative sizes of classes. The size distribution is exponential so classes are of quite different sizes, but the largest classes are not necessarily the oldest. To explain this pattern with a simple stochastic growth model, we introduce the assumption that classes have a regular chance to be split. Third, we study reclassification. The share of patents that are in a different class now than they were at birth can be quite high. Reclassification mostly occurs across classes belonging to the same 1-digit NBER category, but not always. We also document that reclassified patents tend to be more cited than non-reclassified ones, even after controlling for grant year and class of origin

A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance

Measuring similarity between patents is an essential step to ensure novelty of innovation. However, a large number of methods of measuring the similarity between patents still rely on manual classification of patents by experts. Another body of research has proposed automated methods; nevertheless, most of it solely focuses on the semantic similarity of patents. In order to tackle these limitations, we propose a hybrid method for automatically measuring the similarity between patents, considering both semantic and technological similarities. We measure the semantic similarity based on patent texts using BERT, calculate the technological similarity with IPC codes using Jaccard similarity, and perform hybridization by assigning weights to the two similarity methods. Our evaluation result demonstrates that the proposed method outperforms the baseline that considers the semantic similarity only

Networks and navigation in the knowledge economy: Studies on the structural conditions and consequences of path-dependent and relational action

In the wake of a relational turn, economic geographers have begun to scrutinize the relationships and interactions between people and organizations as a driving force behind economic processes at both global and local scales. Through a focus on contingent contextuality and path dependence, relational economic geography and network thinking have provided the necessary conceptual toolbox for untangling the structural effects and drivers of these relationships and their spatial embeddedness. However, despite the conceptual richness of the relational approach, empirical studies have often fallen short of capturing its core tenets: First, there is a prevalence to focus on places, infrastructures, and similarities as aggregate proxies for actors and their socio-economic relationships as the unit of geographical network analysis; While often convenient, this approach misses out on the capacity of networks to represent spatially embedded social contexts as enablers or constraints of economic action. Second, while path dependence is at the heart of evolutionary approaches towards economic geography, few studies actually trace how path-dependent and interrelated innovation shapes the long-term emergence of fields. Relational processes are especially salient when outcomes are opaque, decisions are interdependent, and when formal rules and roles are weak or absent. In this thesis, I ask how actors navigate such contexts and investigate the structural conditions and consequences of their navigation efforts. In my pursuit of this question, I draw on literatures from sociology, economics, and organization studies and build on novel methods of network analysis capable of empirically capturing contextuality and path dependence to investigate relational processes at three levels of economic activity: The thesis first looks towards a localized and informal trade platform to demonstrate how consumers rely on their former transactions to navigate exchange uncertainty and how such an exchange system can become liable to personal lock-in. It then moves on to show how the geographically and organizationally diversified search for innovation opportunities structures the transfer of knowledge across a globalized and partially informal corporate scouting community. Finally, the thesis shows how the linkage of distinct knowledge domains drives the long-term emergence of heterogeneous technological fields. In its endeavor to trace these processes, the thesis contributes a set of distinct relational research designs that demonstrate how advances in methods and data can be employed to empirically exploit the conceptual richness of relational economic geography