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

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

Discovering shifts in competitive strategies in probiotics, accelerated with TechMining

[EN] Profiling the technological strategy of different competitors is a key element for the companies in a given industry, as well to technology planners and R&D strategists. The analysis of the patent portfolio of a company as well as its evolution in the time line is of interest for technology analysts and decision makers. However, the need for the participation of experts in the field of a company as well as patent specialists, slows down the process. Bibliometrics and text mining techniques contribute to the interpretation of specialists. The present paper tries to offer a step by step procedure to analyze the technology strategy of several companies through the analysis of their portfolio claims, combined with the use of TechMining with the help of a text mining tool. The procedure, complemented with a semantic TRIZ analysis provides key insights in disclosing the technological analysis of some competitors in the field of probiotics for livestock health. 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Extract conceptual graphs from plain texts in patent claims. Engineering Applications of Artificial Intelligence, 25, 874–887.Yoon, J., Park, H., & Kim, K. (2013). Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-bassed content analysis. Scientometrics, 94, 313–331

Identifying patent conflicts: TRIZ-Led patent mapping

This paper presents a novel method of patent mapping for visualising conflicts between patent claims that incorporates the Theory of Inventive Problem Solving (TRIZ). The method uses TRIZ engineering parameters as the criteria for evaluating dissimilarities between patent claims, producing a visualisation based on Multi-Dimensional Scaling (MDS) that can be compared with legal judgments. The advantages of the method are that it (a) reduces evaluation complexity by transforming claim-to-claim comparisons into claim-to-criteria comparisons, and (b) provides a means of comparing judgment standards between different legal authorities in mechanical engineering terms. Reliability and validity of the method are tested through focus groups using a case study on aircraft seats. The scope of the method is limited to the field of mechanical inventions

A literature review on the state-of-the-art on intellectual property analytics

In this paper, we contribute in solving the problem of incomplete adoption of IPA within firms, by producing a state of the art literature review on IPA methods and techniques. The paper’s aim is to summarise the existing work in the field, especially when it comes to the application of machine learning, artificial neural networks and artificial intelligence in the intellectual property domain

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

Patent\u27s New Salience

The vast majority of patents do not matter. They are almost never enforced or licensed and, in consequence, are almost always ignored. This is a well-accepted feature of the patent system and has a tremendous impact on patent policy. In particular, while there are many aspects of patent law that are potentially troubling—including grants of unmerited patents, high transaction costs in obtaining necessary patent licenses, and patents’ potential to block innovation and hinder economic growth—these problems may be insignificant in practice because patents are under-enforced and routinely infringed without consequence. This Article argues that technological developments are greatly increasing the salience of patents by making patents easier and cheaper to find and enforce. These developments—including private platforms’ adjudication systems and AI-driven patent analytics—profoundly impact how the patent system functions and upend the system’s present dependence on under-enforcement and ignorance. Where most patents could previously be safely disregarded, formerly forgotten patents now matter. This Article makes four contributions to the literature. First, this Article explores the technology that is rendering patents newly salient and explains how this alters basic assumptions underlying the patent system. Second, this Article demonstrates that although new technology is increasing the number of patents that can be reviewed and enforced, this transformation sometimes decreases the depth of patent analysis. Because it is difficult to draw conclusions about patent scope or validity without in-depth analysis, this omission means that technological review of patents may give patents unmerited influence. Third, this Article shows a sharp divergence between public policy goals and private use of patents. For several decades, the courts and Congress have been reforming patent policy to decrease the impact of patents to alleviate concerns that patent owners hinder innovation by others. This Article demonstrates, in clear contrast to this goal, an increase in patent salience that is due exclusively to the use of private platforms and technologies. Further, the use of private platforms to find, analyze, and enforce patents creates the risk that choices made by companies and software developers will displace substantive patent law. Finally, this Article suggests policy reform, including ways to improve technology and patents and adjusted approaches to patent doctrine and theory