Identifying target for technology mergers and acquisitions using patent information and semantic analysis

Technology plays an increasingly important role in today’s enterprise competition. Technology mergers and acquisitions (Tech M&A), as an effective way to acquire the external technology resources rapidly, have attracted attention from researchers for their potential realization of value through synergy. A big challenge is how to identify appropriate targets to support the effective technology integration. In this study, we developed a model of target selection of Tech M&A from the perspective of technology relatedness and R&D capability. We present results for the Tech M&A case in China’s cloud computing industr

Deriving Technology Intelligence from Patents: Preposition-based Semantic Analysis

Patents are one of the most reliable sources of technology intelligence, and the true value of patent analysis stems from its capability of describing the content of technology based on the relationships between keywords. To date a number of techniques for analyzing the information contained in patent documents that focus on the relationships between keywords have been suggested. However, a drawback of the existing keyword approaches is that they cannot yet determine the types of relationships between the keywords. This study proposes a novel approach based on preposition semantic analysis network which overcomes the limitations of the existing keywords-based network analysis and demonstrates its potential through an application. A preposition is a word that defines the relationship between two neighboring words, and, in the case of patents, prepositions aid in revealing the relationships between keywords related to technologies. To demonstrate the approach, patents regarding an electric vehicle were employed. 13 prepositions were identified which could be used to define 5 relationships between neighboring technological terms: “inclusion (utilization),” “objective (purpose),” “effect,” “process,” and “likeness.” The proposed approach is expected to improve the usability of keyword-based patent analyses and support more elaborate studies on patent documents

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

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

Patent data driven innovation logic

Innovation research is conventionally conducted with creativity techniques such as TRIZ, Mind Mapping, Brainstorming, etc. (Dewulf, Baillie 1998). Patent research is typically used to research novelty or prior art, and legal studies. This thesis is at the intersection of creativity techniques, and patent data analysis. It describes how to utilise patent data for distilling Innovation Logic and conducting innovation research. Using the patent research tool PatentInspiration (© AULIVE Software NV), the 4 different stages of the Innovation Logic approach have been subjected to text analysis in patent literature. The specific text patterns were identified and documented on several case studies, with one case study across the whole thesis: the toothbrush. The opportunities and limitations of Patent Data Driven Innovation Research have been documented and discussed. This methodology has been demonstrated within a proposed structural approach to problem solving, technology marketing and innovation research. Furthermore, the potential of artificial idea generation and artificial creativity was examined and debated for the purpose of computer aided creativity. This thesis examines and confirms three claims: CLAIM 1: PROPERTIES AND FUNCTIONS CAN BE ADJECTIVES AND VERBS IN PATENT LITERATURE CLAIM 2: PATENT DATA ANALYSIS AUGMENTS THE FULL INNOVATION LOGIC PROCESS CLAIM 3: ARTIFICIAL INNOVATION METHODS CAN BE FUELED BY PATENT DATA Patent data can be text mined, acting as a global brain consisting of over 100 million invention documents. It is possible to use this existing data to reverse engineer thinking methodologies, allowing scientists and engineers to solve new problems, invent new products or processes, or find new markets for existing technologies. Patent Data Driven Innovation Logic will demonstrate a systematic innovation approach that combines the force of contemporary data mining methods on patent literature, with a structured innovation research methodology.Open Acces

An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation

© 2017, Akadémiai Kiadó, Budapest, Hungary. How to evaluate the value of a patent in technological innovation quantitatively and systematically challenges bibliometrics. Traditional indicator systems and weighting approaches mostly lead to “moderation” results; that is, patents ranked to a top list can have only good-looking values on all indicators rather than distinctive performances in certain individual indicators. Orienting patents authorized by the United States Patent and Trademark Office (USPTO), this paper constructs an entropy-based indicator system to measure their potential in technological innovation. Shannon’s entropy is introduced to quantitatively weight indicators and a collaborative filtering technique is used to iteratively remove negative patents. What remains is a small set of positive patents with potential in technological innovation as the output. A case study with 28,509 USPTO-authorized patents with Chinese assignees, covering the period from 1976 to 2014, demonstrates the feasibility and reliability of this method

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