II-SDV 2016 Minesoft

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This document provides information about new features and enhancements to patent search and analysis tools from Minesoft, including: 1. Citation Explorer module integrated with PatBase to allow better examination of forward and backward citations. 2. Legal status enhancements including new groups and indicator of patent family status. 3. Minesoft's new Textmine product allows chemical structure and keyword searching of chemicals extracted from patent texts. 4. Details provided on Textmine's search options, visualization of chemical instances in text, and linking capabilities.

II-SDV 18-19 April 2016, Nice
Olivier Huc

Specialists
in Patent
Information
Building
Intelligent
Patent
Information
Solutions
since 1996
What we do
Trusted
by IP experts
Worldwide
Corporations,
National Patent
Offices, Patent
Attorneys and
Patent Search
Firms worldwide
International
Customer
Support
Global client base
With Offices and
Support across
Europe North
America, and Asia

Patent Families
Analytics
Quality Control
Fast Search
Legal Status
Review
Alerts
• 31 Full Text Collections
• 52 Million Families
• 106 Issuing Authorities
• IPC, CPC US and JP classes
• Quality Controlled content
• Normalised data

Citation Enhancements
• New Module Citation Explorer, integrated to PatBase
• Allows for better examination of forward
and backwards citations
• Side by Side view
• Annotate and Flag Citations
• X, Y, citations categories displayable and searchable
for more concise search and review
• Monitor and track Forward citations with Citetracker
• Stay Alerted to new patents citing a particular set of documents
• New citation command available (CTBX, CTBY etc..)
5

Legal Status enhancements
7
• New Legal Status Groups Added
• LSAL “Appeal”
• LSRX “Reexamination”
• Integrate the Legal Status View into your
own internal applications with the PatBase API

• New status indicator in the family table
• Alive or Dead based on Legal Status
• Enable this option in your user settings
• Faster review of the whole Patent Family
• Faster review during FTO searches
• Can now also be exported
in excel, word, pdf etc..
New status indicator
8

• NEW Minesoft product
• Database of chemical entities extracted from full text of patents
• Structure and keyword searchable
• Coverage:
• >12m unique chemicals from >10m full text documents
• English text: WO, EP, US, GB, AU, IL and IN
• Non-Latin text: JP, CN, KR (extracted from original text not from MT)
• French and German text: FR, DE, EP
• US images from 2001 to date (~40% increase in recall)
• Updated daily
• Seamlessly linked to PatBase & PatBase Express
9

Flexible
search
options
Limit search
to claims or
by country
Search
chemical
terminology
Full
Structure
drawing
capability
10

Structure
of hits found
Instances of
chemical in
full text
Links to external
resources
Details of hits
found
11

Publications
disclosing
chemical of
interest tagged
Textmine view
of publication
and chemical
of interest
12

All the terms
are identified,
grouped,
categorised and
clickable
Textmine offers a unique FT visualisation

Upcoming PatBase Roadshows
London: 11th May
Basel: 19th May
Utrecht: 24th May
Paris: 14th June
Munich: 16th June
Madrid: 21st June
Barcelona: 23rd June
Dusseldorf: 24th June

Life science companies increasingly rely on text mining to gain important insights from vast amounts of published information. But researchers struggle to get access to full-text articles for text mining. When they do get the full text they must contend with multiple formats and inconsistent license terms – all of which inhibit text mining efforts. In this presentation, we will describe the value in mining full-text scientific literature and outline the issues researchers face in accessing and licensing this content for commercial purposes. We will provide a walkthrough of Copyright Clearance Center’s (CCC) RightFind™ XML for Mining solution and contrast this with other approaches to solving these time-consuming content and licensing challenges. CCC is the parent organization of RightsDirect.

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WIPO started work in the area of patent analytics in 2010 with a Development Agenda project on “Developing Tools for Access to Patent Information” which resulted in the production of a series of Patent Landscape Reports (WIPO’s patent landscape reports can be found here). These reports, prepared in cooperation with various UN Agencies, non-governmental organizations, research institutes and national IP Offices, analyze patent activity in various topics in the areas of public health, food and agriculture, environment and energy, and disabilities. The key findings are often summarized in an infographic. In 2013 WIPO started working also on awareness raising and capacity building in the area of patent analytics. Apart from various workshops organized on this topic, WIPO published in September 2015 the “Guidelines for Preparing Patent Landscape Reports”. The Guidelines describe the objectives and motivations for preparing Patent Landscape Reports (PLR) and other types of patent analysis, the tasks associated with patent analytics, as well as the stages in the preparation of PLRs, providing also some insights from WIPO’s experience in the area. Since 2015 WIPO is exploring open source tools for patent analytics purposes in the framework of the preparation of a Manual on Open Source Tools for Patent Analytics. Open source tools are typically used by other disciplines, usually business/data analysts, statisticians, IT professionals and scientists, rather than with regard to patent data. Nevertheless, in recent years they started emerging as an alternative and/or a complement to ready-to-use tools, providing flexibility and adaptability in different analysis types. In view of the necessary programming related to this type of tools, WIPO developed step-by-step instructions in the Manual with example datasets, and will provide capacity building activities with training on patent analytics for Technology and Innovation Technology Support Centers (TISCs) around the world (for more information on the TISC program please visit www.wipo.int/tisc) .

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Dieter Küry (Novartis Pharma, Switzerland) Using analytical methods are more and more replacing database searching in a knowledge manager's daily activities. In this presentation various facets of publication analysis will be presented and discussed. These new methods were applied for the analysis of publications in scientific journals and visuals were created to deduct publications strategies. On the technical side, the overall analysis process requires diverse tools for reference managing, text analysis and visualization. The impact on skills of the knowledge manager who moves from the expert for query languages to the expert for creation and maintaining of thesauri is also shown. Main benefit of the analytical methods compared to traditional database searching is the manifold use of results, which are easily adaptable to new requirements.

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AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...

Dr. Haxel Consult

In the patent domain, all types of issues, from very specific search requirements to the linguistic characteristics of the text domain, are accentuated. Consequently, to develop patent text mining tools for scientists and patent experts, we need to understand their daily work tasks, as well as the linguistic character of the text genre (i.e., patentese). Patent text is a mixture of legal and domain-specific terms. In processing technical English texts, a multi-word unit method is often deployed as a word-formation strategy to expand the working vocabulary, i.e., introducing a new concept without the invention of an entirely new word. This productive word formation is a well-known challenge for traditional natural language processing tools utilizing supervised machine learning algorithms due to limited domain-specific training data. Deep learning technologies have been introduced to overcome the reduction in performance of traditional NLP tools. In the Artificial Researcher technologies, we have integrated explicit and implicit linguistic knowledge into the deep learning algorithms, essential for domain-specific text mining tools. In this talk, we will present a step-by-step process of how we have developed the mentioned text mining tools. For the final outline, we will also demonstrate how these tools can be integrated in a cross-genre passage retrieval system, based on a technology from 2016 that still holds the state-of-the-art within the patent text mining research community in 2022.

AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...

Dr. Haxel Consult

In 2013 we witnessed an evolutionary change in the NLP field evolved thanks to the introduction of space embeddings that, with the use of deep learning architectures, achieved human-level performances in many NLP tasks. With the introduction of the Attention mechanism in 2017 the results were further improved and, as result, embeddings are quickly becoming the de facto standards in solving many NLP problems. In this presentation, you will learn how generate and use space embedding for search purposes and provide comparison metrics to more traditional relevance-based search engines. Moreover, I will provide some initial results on a paper currently under review that provides an insight on hyperparameter tuning during the generation of embeddings.

AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...

Dr. Haxel Consult

AI-SDV 2022: Machine learning based patent categorization: A success story in...

Dr. Haxel Consult

AI-SDV 2022: Machine learning based patent categorization: A success story in...

Dr. Haxel Consult

AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...

Dr. Haxel Consult

It is relatively easy for a human to read a document and quickly figure out which concepts are important. However, this task is a difficult challenge for a machine. During the past few decades, there have been two main approaches for concept identification: Natural Language Processing and Machine Learning. During the early part of this century, Machine Learning made great strides as new techniques came into wider use (SVM’s, Topic Modeling, etc..). Sensing the competition, Natural Language Processing responded with deployment of new emerging techniques (sematic networks, finite state automata, etc..). Neither approach has completely solved the WHAT problem. Advances in Artificial Intelligence have the potential to significantly improve the situation. Where AI is making the most impact is as an enhancement to make Machine Learning and Natural Language Processing work better and, more importantly, work together. This presentation looks at some of this history and what might happen in the future when we blend the interpretation of language with pattern prediction.

AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...

Dr. Haxel Consult

Trademarks serve as key leading indicators for innovation and economic growth. As the vanguards of new and expanding enterprises, trademarks can be used to study entrepreneurship and shifting market demands in response to varying economic factors. This responsiveness has been seen as recently as the COVID-19 pandemic, where trademark research revealed key insights about business reaction to the global upheaval. At CIPO, we have been delving more deeply than ever before into trademark analysis by leveraging cutting-edge natural language processing (NLP) tools to derive actionable business intelligence from trademark data. In this presentation, we present a survey of NLP in use at CIPO and the insights we have learned applying them. These insights include COVID-19 responses, line-of-business trends based on firm characteristics, and more. We also discuss ongoing and future trademark research projects at CIPO. These projects include emerging technology detection methods and high-resolution trademark classification systems. We conclude that artificial intelligence-enhanced tools like NLP are key components of future exploitation of trademark data for business and economic intelligence.

AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...

Dr. Haxel Consult

In our customer projects involving automated document processing, we often encounter document types providing crucial data in the form of tables. While established text analytics algorithms are usually optimized to operate on running text, they tend to produce rather poor results on tables as they do not capture the non-sequential relations inside them (e.g. interpret the content of a table cell relative to its column title, interpret line breaks inside a cell differently from line breaks between cells or rows). While there are elaborate information extraction products in the market for a few highly specific types of tabular documents, there is no general approach out there. The main cause for this is the fact that table structures can be encoded by a heterogenous range of layout means (e.g. column boundaries can be signaled by lines vs. aligned text vs. white space). In this talk, we will illustrate several solutions that we have developed for a range of challenges occurring in this context, both for scanned and digitally generated documents.

AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...

Dr. Haxel Consult

Most scientific journals request, that the complete set of research data is published simultaneously with the peer-reviewed paper. The publication of the research data usually is carried out as so-called "Supplementary Material", attached to the original paper, or on a "Research Data Repository". Both forms have in common, that the data is published usually unstructured and not in an uniform machine processable format. This makes its further use in electronic tools for AI or data mining unnecessarily difficult or even impossible. A concept is presented, in which the data is digitally recorded, following the principle of FAIR data, as part of the publication process. This digital capture makes the data available to the scientific community for easy use in data mining and AI tools. The data in the repository contains links to the publication to document its origin. The concept is applicable for preprints, peer-review papers, diploma and doctoral theses and is particularly suitable for open access publications. Moreover, the presentation highlights correspondent activities, which were released in scientific publications recently.

AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...

Dr. Haxel Consult

AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...

Dr. Haxel Consult

How do you find video when you only have sparse data? While you can wander the stacks (if you can still find open stacks) for inspiration, video either physical or digital, is difficult to discover. Wandering the virtual stacks is, well, virtually impossible. Discovery platforms on the whole have not replicated the inspirational experience of wandering the stacks. More companies are using archivable video for internal communication of the various research projects, product developments, test results, and more that are being considered, in progress, or completed. Showing how an experiment was conducted can convey considerably more information that is very difficult to communicate via text. How do you find a company video that might be helpful for your project? A case study is presented of the problems and the solutions that were implemented by a large, multinational chemical company. A suite of content discovery technologies was used including a video to text to tagging system connected to their documents database and automatically indexed using several chemical as well as conceptual systems (rule-based, NLP, inference engine). To build the system and support the manuscript and video submission there is a metadata extraction program which pulls and inserts the metadata into the submission forms so the author can move quickly through that process.

AI-SDV 2022: Copyright Clearance Center

Dr. Haxel Consult

Copyright Clearance Center A pioneer in voluntary collective licensing, CCC (Copyright Clearance Center) helps organizations integrate, access, and share information through licensing, content, software, and professional services. With expertise in copyright and information management, CCC and its subsidiary RightsDirect collaborate with stakeholders to design and deliver innovative information solutions that power decision-making by helping people integrate and navigate data sources and content assets. CCC recently acquired the assets and technology of Deep SEARCH 9 (DS9), a knowledge management platform that leverages machine learning to help customers perform semantic search, tag content, and discover new insights.

AI-SDV 2022: Lighthouse IP

Dr. Haxel Consult

Lighthouse IP is the world’s leading provider of intellectual property content. The core business of Lighthouse IP is sourcing and creating content from the world’s most challenging authorities. Specialized in IP data, Lighthouse IP provides over 160 countries coverage for patents, over 200 authorities for trademarks and over 90 authorities for designs. Lighthouse IP data is available via several partners. The company is headquartered in Schiphol-Rijk in the Netherlands and has offices in the United States, China, Thailand, Vietnam, Egypt, Indonesia and Belarus. Globally a team of 150 experts works on the creation of this unique data collection.

AI-SDV 2022: New Product Introductions: CENTREDOC

Dr. Haxel Consult

CENTREDOC was created in 1964 as the technical information center of the swiss watchmaking industry. Building on a strong team of engineers, CENTREDOC now offers a complete range of services and solutions for the monitoring of strategic, technological and competitive information. CENTREDOC is also a leader in the research of patent, technical and business intelligence, and offers consulting expertise in the implementation of monitoring solutions.

AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...

Dr. Haxel Consult

AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...

Dr. Haxel Consult

What if there was a platform where literature, conference abstracts, patents, clinical trials, news, grants and other sources were fully integrated? What if the data would be harmonized, enriched with standardized concepts and ready for analysis? After building our patent analytics platform we didn’t stop dreaming and built our big data analytics platform by semantically integrating text-rich, scientific sources. In my presentation I will talk about what we built and why we built it. And, of course, I will also address the challenges and hurdles along the way. Was it worth it and what comes next? Let’s talk about it!

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