Detecting Emerging Technologies in Artificial Intelligence Scientific Ecosystem Using an Indicator-based Model

Early identification of emergent topics is of eminent importance due to their potential impacts on society. There are many methods for detecting emerging terms and topics, all with advantages and drawbacks. However, there is no consensus about the attributes and indicators of emergence. In this study, we evaluate emerging topic detection in the field of artificial intelligence using a new method to evaluate emergence. We also introduce two new attributes of collaboration and technological impact which can help us use both paper and patent information simultaneously. Our results confirm that the proposed new method can successfully identify the emerging topics in the period of the study. Moreover, this new method can provide us with the score of each attribute and a final emergence score, which enable us to rank the emerging topics with their emergence scores and each attribute score

Innovation Novelty and Firm Value: Deep Learning based Text Understanding

Innovation is widely acknowledged as a key driver of firm performance, with patents serving as unique indicators of a company’s technological advancements. This study aims to investigate the impact of textual novelty within patents on firm performance, focusing specifically on biotechnology startups listed on the Nasdaq. Utilizing deep learning-based approaches, we construct measures for semantic originality in patent texts. Through panel vector autoregressive (VAR) analysis, our empirical findings demonstrate a positive correlation between textual novelty and abnormal stock returns. Further, impulse response function analysis indicates that the impact of textual novelty peaks approximately one week after patent issuance and gradually diminishes within a month. These insights offer valuable contributions to both the theoretical understanding and practical application of innovation management and strategic planning

Generating Information Relation Matrix Using Semantic Patent Mining for Technology Planning: A Case of Nano-Sensor

For the purposes of technology planning and research and development strategy development, we present a semi-automated method that extracts text information from patent data, uses natural language processing to extract the key technical information of the patent, and then visualizes this information in a matrix form. We tried to support qualitative analysis of patent contents by extracting functions, components, and contexts, which are the most important information about inventions. We validated the method by applying it to patent data related to nanosensors. The matrix can emphasize technical information that have not been exploited in patents, and thereby identify development opportunities.111Ysciescopu

Drivers of the decrease of patent similarities from 1976 to 2021

The citation network of patents citing prior art arises from the legal obligation of patent applicants to properly disclose their invention. One way to study the relationship between current patents and their antecedents is by analyzing the similarity between the textual elements of patents. Patent similarity indicators have been constantly decreasing since the mid-70s. The aim of this work is to investigate the drivers of this downward trend through a general additive model and contextually propose a computationally efficient way to derive the similarity scores across pairs of patent citations leveraging on state-of-the-art tools in Natural Language Processing. We found that by using this non-linear modelling technique we are able to distinguish between distinct, temporally varying drivers of the patent similarity levels that accounts for more variation in the data ($R^2\sim 18\%$) in comparison to the previous literature. Moreover, with such corrections in place, we conclude that the trend in similarity shows a different pattern than the one presented in previous studies

TeknoAssistant : a domain specific tech mining approach for technical problem-solving support

This paper presents TeknoAssistant, a domain-specific tech mining method for building a problem-solution conceptual network aimed at helping technicians from a particular field to find alternative tools and pathways to implement when confronted with a problem. We evaluate our approach using Natural Language Processing field, and propose a 2-g text mining process adapted for analyzing scientific publications. We rely on a combination of custom indicators with Stanford OpenIE SAO extractor to build a Bernoulli Naive Bayes classifier which is trained by using domain-specific vocabulary provided by the TeknoAssistant user. The 2-g contained in the abstracts of a scientific publication dataset are classified in either "problem", "solution" or "none" categories, and a problem-solution network is built, based on the co-occurrence of problems and solutions in the abstracts. We propose a combination of clustering technique, visualization and Social Network Analysis indicators for guiding a hypothetical user in a domain-specific problem solving process

Monitoring Newly Adopted Technologies Using Keyword Based Analysis of Cited Patents

This paper proposes a method that can reliably monitor the adoption of existing technology by term frequency-inverse document frequency (11-IDF) and K-means clustering using cited patents. 11-IDF and K-means clustering can extract patent information when the number of patents is sufficiently large. When the number of patents is too small for 11-IDF and K-means clustering to be reliable, the method considers patents that were cited by the originally set of patents. The mixed set of citing patents and cited patents is the new subject of analysis. As a case study, we have focused in agricultural tractor in which new technologies were adopted to achieve automated driving. TF-IDF and K-means clustering alone failed to monitor the adoption of new technology but the proposed method successfully monitored it. We anticipate that our method can ensure the reliability of patent monitoring even when the number of patents is small.11Ysciescopu

Requirement-oriented core technological components’ identification based on SAO analysis

© 2017, Akadémiai Kiadó, Budapest, Hungary. Technologies play an important role in the survival and development of enterprises. Understanding and monitoring the core technological components (e.g., technology process, operation method, function) of a technology is an important issue for researchers to develop R&D policy and manage product competitiveness. However, it is difficult to identify core technological components from a mass of terms, and we may experience some difficulties with describing complete technical details and understanding the terms-based results. This paper proposes a Subject-Action-Object (SAO)-based method, in which (1) a syntax-based approach is constructed to extract the SAO structures describing the function, relationship and operation in specified topics; (2) a systematic method is built to extract and screen technological components from SAOs; and (3) we propose a “relevance indicator” to calculate the relevance of the technological components to requirements, and finally identify core technological components based on this indicator. Based on the considerations for requirements and novelty, the core technological components identified have great market potential and can be useful in monitoring and forecasting new technologies. An empirical study of graphene is performed to demonstrate the proposed method. The resulting knowledge may hold interest for R&D management and corporate technology strategies in practice

Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification