Forecasting the Spreading of Technologies in Research Communities

Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution

A Study of Identifying Trends in Projector using F-Term Codes from Japanese Patent Applications

It is important for companies to understand technological trends when developing technologies and products. Based on Utterback and Abernathy's theory of the emergence of dominant design, we investigate a method of obtaining technological trends using patent information. In a previous study, we visualised the innovation state and obtained the emergence of dominant designs using FI and theme codes, which are original patent classification codes of Japanese patents, as well as F-terms. However, there has not been sufficient research conducted on how to obtain patent applications and how to select FIs, theme codes, and F-terms, which are the preconditions for the analysis. In this study, we discuss a procedure for obtaining patent applications, selecting FIs, theme codes and F-terms, visualising the innovation state, and predicting the emergence of dominant designs, and report the results

PERANCANGAN INOVASI DESAIN KEMASAN PRODUK PADA PRODUK SUSU FRESHTIME DENGAN METODE TECHNOLOGY FORECASTING (STUDI KASUS : KPSBU LEMBANG)

Inovasi merupakan faktor penentu dalam persaingan industri dan merupakan senjata yang tangguh menghadapi persaingan. Inovasi tidak dapat dihindari oleh perusahaan, jika ingin mencari kesuksesan dalam bisnisnya. Kesuksesan suatu perusahaan sangat bergantung kepada kemampuan perusahaan tersebut dalam mendapatkan, menggunakan pengetahuan, dan mengaplikasikannya menjadi sebuah produk baru karena Inovasi merupakan kegiatan inti untuk pengembangan dan produktifitas dari segala kegiatan ekonomi. Begitupun untuk KPSBU Lembang yang perlu melakukan inovasi untuk kemasan salah satu produknya yaitu kemasan produk susu freshtime, karena jika dilihat KPSBU Lembang ini belum melakukan inovasi dari aspek kemasan produk. Inovasi dilakukan supaya KPSBU Lembang dapat bertahan di tengah arus perkembangan zaman. Maka dari itu inovasi yang dilakukan dalam penelitian ini dibantu dengan menggunakan metode forecasting technology. Metode Technology Forecasting yang dilakukan ialah metode Technology Forecasting secara kualitatif dengan membuat Technology Roadmap untuk melihat trend teknologi yang memberikan informasi mengenai potensi pengembangan yang dapat dilakukan, dilanjutkan dengan membuat Morphology Chart mengenai fungsi, sub-fungsi, material, serta mengenai model atau desain dari kemasan tersebut yang mana Morphology Chart ini berfungsi untuk menghasilkan laternatif-alternatif desain, serta menggambarkan hasil dari dilakukannya forecasting. Selanjutnya melakukan perancangan akhir produk inovasi dari alternatif terpilih pada morphology chart untuk kemasan produk susu freshtime

Technology Forecasting of Unmanned Aerial Vehicle Technologies through Hierarchical S Curves

This study aims to propose a technology forecasting approach based on hierarchical S-curves. The proposed approach uses holistic forecasting by evaluating the S-curves of sub-technologies as well as the main technology under concern. A case study of unmanned aerial vehicle (UAV) technologies is conducted to demonstrate how the proposed approach works in practice. This is the first study that applies hierarchical S-curves to technology forecasting of unmanned aerial vehicle technologies in the literature. The future trend of the UAV technologies is analysed in detail through a hierarchical S-curve approach. Hierarchical S-curves are also utilised to investigate the sub-technologies of the UAV. In addition, the technology development life cycle of technology is assessed by using the three indexes namely, (1) the current technological maturity ratio (TMR), (2) estimating the number of potential patents that could be granted in the future (PPA), and (3) forecasting the expected remaining life (ERL). The results of this study indicate that the UAV technologies and their sub-technologies are at the growth stage in the technology life cycle, and most of the developments in UAV technology will have been completed by 2048. Hence, these technologies can be considered emerging technologies

An assessment of technology forecasting: Revisiting earlier analyses on dye-sensitized solar cells (DSSCs)

© 2018 Elsevier Inc. The increasingly uncertain dynamics of technological change pose special challenges to traditional technology forecasting tools, which facilitates future-oriented technology analysis (FTA) tools to support the policy processes in the fields of science, technology & innovation (ST&I) and the management of technology (MOT), rather than merely forecasting incremental advances via analyses of continuous trends. Dye-sensitized solar cells are a promising third-generation photovoltaic technology that can add functionality and lower costs to enhance the value proposition of solar power generation in the early years of the 21st century. Through a series of technological forecasting studies analyzing the R&D patterns and trends in Dye-sensitized solar cells technology over the past several years, we have come to realize that validating previous forecasts is useful for improving ST&I policy processes. Yet, rarely do we revisit forecasts or projections to ascertain how well they fared. Moreover, few studies pay much attention to assessing FTA techniques. In this paper, we compare recent technology activities with previous forecasts to reveal the influencing factors that led to differences between past predictions and actual performance. Beyond our main aim of checking accuracy, in this paper we also wish to gain some sense of how valid those studies were and whether they proved useful to others in some ways

Patents in the computer-aided diagnosis industry

Computer aided diagnosis is a relatively new field, through the use of new techniques algorithms and technologies, it can help technicians perform a better and faster analysis, reduce or even substitute part of their workload. Patents are windows into a company's technological assets, as well as into the state of a certain technology field. In this thesis we analyzed patents that are mainly related to the automated analysis of human retinopathies. Using patent search engines we explored the various patent databases, using keywords related to the area and the international patent classification to refine the search and eliminate unrelated results, proceeding then to a thorough analysis of the dataset. By analyzing the structured and unstructured text, contained in the obtained patents, different observations where made: major players in the field,patent timelines, main technologies involved and the direction of the technology evolution

Opportunity Identification for New Product Planning: Ontological Semantic Patent Classification

Intelligence tools have been developed and applied widely in many different areas in engineering, business and management. Many commercialized tools for business intelligence are available in the market. However, no practically useful tools for technology intelligence are available at this time, and very little academic research in technology intelligence methods has been conducted to date. Patent databases are the most important data source for technology intelligence tools, but patents inherently contain unstructured data. Consequently, extracting text data from patent databases, converting that data to meaningful information and generating useful knowledge from this information become complex tasks. These tasks are currently being performed very ineffectively, inefficiently and unreliably by human experts. This deficiency is particularly vexing in product planning, where awareness of market needs and technological capabilities is critical for identifying opportunities for new products and services. Total nescience of the text of patents, as well as inadequate, unreliable and untimely knowledge derived from these patents, may consequently result in missed opportunities that could lead to severe competitive disadvantage and potentially catastrophic loss of revenue. The research performed in this dissertation tries to correct the abovementioned deficiency with an approach called patent mining. The research is conducted at Finex, an iron casting company that produces traditional kitchen skillets. To \u27mine\u27 pertinent patents, experts in new product development at Finex modeled one ontology for the required product features and another for the attributes of requisite metallurgical enabling technologies from which new product opportunities for skillets are identified by applying natural language processing, information retrieval, and machine learning (classification) to the text of patents in the USPTO database. Three main scenarios are examined in my research. Regular classification (RC) relies on keywords that are extracted directly from a group of USPTO patents. Ontological classification (OC) relies on keywords that result from an ontology developed by Finex experts, which is evaluated and improved by a panel of external experts. Ontological semantic classification (OSC) uses these ontological keywords and their synonyms, which are extracted from the WordNet database. For each scenario, I evaluate the performance of three classifiers: k-Nearest Neighbor (k-NN), random forest, and Support Vector Machine (SVM). My research shows that OSC is the best scenario and SVM is the best classifier for identifying product planning opportunities, because this combination yields the highest score in metrics that are generally used to measure classification performance in machine learning (e.g., ROC-AUC and F-score). My method also significantly outperforms current practice, because I demonstrate in an experiment that neither the experts at Finex nor the panel of external experts are able to search for and judge relevant patents with any degree of effectiveness, efficiency or reliability. This dissertation provides the rudiments of a theoretical foundation for patent mining, which has yielded a machine learning method that is deployed successfully in a new product planning setting (Finex). Further development of this method could make a significant contribution to management practice by identifying opportunities for new product development that have been missed by the approaches that have been deployed to date