IC-SDV 2019: Minesoft

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Minesoft, founded in 1996, develops patent information services and solutions, linking technical, legal, bibliographic and full text patent information for patent professionals and research specialists. Minesoft's products and services are used by leading corporations, national Patent Offices, patent attorneys and law firms globally. Minesoft develops fast, effective document delivery solutions, a range of competitive intelligence services including patent alerting and monitoring tools, a patent family & legal status portal delivering the latest data for over 40 countries, a searchable patent database of over 48 million records, patent archiving, advanced viewing tools and more. In May 2003, Minesoft and RWS announced their agreement to develop PatBase, a new searchable patent database designed by experts in the complex art of search and retrieval of patent information. The first public demonstrations of PatBase in November 2003 received critical acclaim from expert patent searchers for the intuitive and feature-rich interface. PatBase is a comprehensive new search resource for patent and R&D specialists worldwide, covering 100 countries. Organised into extended patent families, each invention is represented by one individual record, making it easy to see in which countries a filing for patent protection has been made. PatBase is an ideal resource for conducting daily patent & technology searches. Rapid delivery of competitive intelligence, integrated searchable full text, PDF delivery and legal status reports bring PatBase to the forefront of today's searchable patent information tools. PatBase is regularly updated with new data and new search features and is now established as a leading patent resource. PatBase Express, developed in partnership with RWS and released March 2006, provides an easy-to-use end-user corporate-wide solution. PatBase Express allows the most up-to-date scientific research and competitor information contained in patent documents to be accessed throughout an organisation. Minesoft products can be customised to individual corporate requirements. Find more on www.minesoft.com

Internet

Welcome
IC-SDV 2019
Nice, France
Mélanie Bridier

Minesoft - Specialists in Patent Information

New PatBase
Interface
• New streamlined and
intuitive design
• Extra buttons added to
command line for easier
navigation

New Export tool
• New streamlined and intuitive
design
• Save a template with preferred
format, fields to include and choose
how the data is sorted
• Time-saving and easy-to-use

New Citation
Analysis
• Quickly spot who the most
influential players are in a
technology area

Introducing PatDocs
• Family tree mapping
• Auto-populate IDS
forms
• Family legal reports
• Simple to use search
• Quantity search
• Powerful machine
translations
• Integrated PatentOrder

US PAIR Alerts
• Results sent by email
• Monitor status, file
history, transaction
history and
continuation events
• Direct link to file history
• Direct link to the Family
Tree
• Available now

New sketcher
• Query atom properties
e.g. ring bond count,
substitution count,
match multiple elements
• Query bonds e.g.
ring/chain topology,
match multiple bond
orders

New family report: Export by patent
family
• Export matching
structures grouped
by patent family
• Location of
structure in patent
indicated
• Links back to
PatBase family
with links to show
the structures in
their full-text
context

Store data in one secure location
An IPShare Project is an exportable collection of patents and non-patent
literature, there is no hard limit on the number of documents that can be
stored in a project.
Filter by
information shown
in the document
table, i.e. type of
document, title,
publication number
etc.
Quickly check the
assigned ranking,
labels or if there are
any unread chat
messages
All recent activity
on the project is
shown here
Import patent
numbers and NPL
Create new
highlight sets, add
new label
categories, access
the exporting
options, alter the
permission levels
on the project, or
search across the
project.

Encourage collaboration
Collaboration is encouraged through chat messages for each document
and the ability to highlight and annotate sections of a document. All
messages & annotations can be deleted once resolved.
Annotate sections
of the document
Multiple highlighting
sets can be added
to a project
Chat messages are
available for each
document
The action
history tracks
all changes
made to a
document

Thank you
Don’t miss Daniel Lowe’s presentation tomorrow morning:
Physical quantity search with the simplicity of keyword search
Visit the Minesoft stand for more information…

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Founded in 2004 by a group of business & technology entrepreneurs and inventors, Dolcera is a next generation patent and information analytics company. Our proprietary patent search strategy and domain expertise combined with our game changing AI/ML driven tools like PCS and E-Search enable us to offer top notch patent search and analytics services to our diverse clientele in key decision-making areas of IP strategy & creation, litigation, portfolio analysis, competitive intelligence, product development and licensing. Our AI- driven tools and services include: Dolcera PCS – A deep-learning driven superfast patent search engine that offers domain specific Semantic and taxonomy-oriented search capabilities for Patent portfolio analysis, Prior Art, Invalidation and licensing searches. Dolcera Machine learning for Auto-categorization – The transparent, customizable system solves the problem of human bias or comprehension of a patent when classifying them by leveraging proprietary Dolcera AI/ML. The system gives complete control to the user to deliver highly accurate categorized documents with a quick turn around. Dolcera Enterprise Search (E-Search) – Dolcera Enterprise Search is a machine learning enabled enterprise-wide search engine integrated with various value added features and smart charts. The application uses patent documents, scientific literature, product information or generally other technical documents inside a company. The system lets users have access to all the related files with minimal clicks to generate actionable insights. Dolcera ETSI Dashboard – A collection of SEP from various technologies and various specifications updated regularly + PCS (AI driven patent search tool) links the SEPs declared under the specific technical standards & visualize the information for. Our bespoke services include IP research, technical review, business research and newsletter alerts which help our clients make key strategic decisions in relation to: Choosing the right technology Filing & protecting the right IP Standard mapping and claim charting Product enhancement IP, technical & business landscapes Competitive intelligence Regulatory landscape The amalgamation of Dolcera’s AI driven tools, IP services & market research capabilities have time and again assisted our clients in managing their IP & non-IP assets systematically, while enhancing their overall decision-making process. For further information visit: www.dolcera.com Our Global presence: USA: California, Chicago Europe: Germany, UK Asia: India, China

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AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...

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: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...

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

Dr. Haxel Consult

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

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

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: New Insights from Trademarks with Natural Language Processing Al...

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: Extracting information from tables in documents Holger Keibel (K...

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: Scientific publishing in the age of data mining and artificial i...

Dr. Haxel Consult

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

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

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: Copyright Clearance Center

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: Lighthouse IP

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: New Product Introductions: CENTREDOC

Dr. Haxel Consult

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

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!

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

Dr. Haxel Consult

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