ICIC 2013 New Product Introductions Minesoft

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This document provides a product review of Minesoft Ltd., which is a company specializing in patent information solutions. It summarizes Minesoft's services including patent family data from over 100 authorities updated daily, full text collections from over 23 sources, and added value services such as legal status tracking and analytics. The document also highlights new features and data sources for Minesoft's PatBase solution including expanded sequence searching, a Chinese interface, and integrated FDA data.

Empleo Tecnología

Product Review
ICIC, 14th October 2013
Mie Rasmussen
Minesoft Ltd.

Specialists in Patent Information
• Building Intelligent Patent Information Solutions since 1996
Trusted by IP experts Worldwide
• Corporations, National Patent Offices, Patent Attorneys and
Patent Search Firms worldwide
International Customer Support

• Global client base, including USA, Germany, Japan, India and
Israel

Patent
Families

Full Text

Added
Value

•
•
•
•

101 Authorities
Updated Daily
National Data Feeds
Legal Status Searching

• 23 Full Text Collections
• Non Latin Collections JP, CN,
KR, & PCT
• Abstracts from over 50
Authorities
• Searchable Full Text
Translations
• PDF Copies

• Class Revisions (IC, EP, US & JP)
• Citations grouped by families
• Manual Corrections
• Special Indexing
• Normalisation

Data overview
• Total data volume exceeds 35Tb
• 48 million families and 101 publishing bodies
• 40 million full-texts including over 20 million non-Latin into
English machine translations
• 54 million clipped images and 45 million complete sets of
drawings
• PatBase Express interface in seven languages (GB, DE, ES,
FR, JP, CN, PT)
• PatBase interface in three languages (EN, JP, CN)

Legal Status TimeLine
• Review legal status in a single glance
• Includes breakdown for EP national phase
entries
• Based on INPADOC PRS codes
• Included in PatBase Reports & Exports
• Easily linked within existing applications

New Legal Status Search Functionality
• Based on EPO INPADOC PRS data
• Legal status groups to easily combine
multiple PRS codes across different
authorities
• Combine with date Searching on Legal
Events
• Search all granted publications with a
single command

PatBase Analytics
• New PatBase Analytics Module
• Fast analysis on up to 100,000 PatBase
families
• Analyse Dates, Countries, Assignees,
Inventors, Classes and Agents
• Improved text clustering
• Single click analytic reports on IPC’s and
CPC’s

New - Sequence Search Form
• Fast biological sequence searching
• US sequence data covered from 2005
• Proteins,nucleic
acids, and
subsequences

• Search name or source

Additional Updates
• New Chinese Interface
• US, EP, and PCT Full Text translated into
Chinese
• Fast Left Hand Truncation
• FDA Orange Book Data integrated into
PatBase records
• Russian Full Text Data with Machine
Translations now searchable from 1994

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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.

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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