AI-SDV 2020: IPscreener

•

0 likes•614 views

With IPscreener anyone is able to explore and understand the tech knowledge hidden in patents. By only using a plain text input the semantic AI presents a dashboard of the innovation landscape, identifying similar documents and pointing out relevant paragraphs. Use IPscreener for a smarter way to validating your ideas.

Internet

IPscreener
-‐
AI
assisted
exploring
&
reading

enhanced
searchers

MANUAL

The
searcher
reads
the
text
to
extract
keywords

&
classes,
then
creates
a
boolean
strategy
using

combinaBons
of
diﬀerent
operators
&
ﬁelds

AUTOMATIC

The
engine
creates
a
ﬁngerprint
reﬂecBng

the
content
of
a
text,
then
matched
using

trained
data
for
ﬁnding
similar
documents

AI
vs
manual,
what
is
the
diﬀerence?

The
elements
of
prior
art
searching

Right
Document(s)
Right
Passage(s)
Right
InterpretaBon(s)

Current
focus

of
search
tools

Client files
idea
Build
decision
support
processes

IP-
analysis
Finds match
- the idea/concept
lacks novelty
Finds similar
- the idea/concept
is partially known
Manual
search
Finds nothing
– the idea/concept
seems new
Large search
suggested
Minor search
suggested
Auto- classification of
application
No further search
suggested

Search

Analysis

Time

Search

Analysis

The
future
role
of
the
Searcher?

Today
Tomorrow

Strategy

Customer
metrics;
recall
on
porVolio

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

A61K
A61P
C07D
C07C
C07K

Recall
100
long
Recall
100
short

Recall
ra(o
of
number
X/Y
cita(ons
from
patent
oﬃce
report
found
by
IPscreener
within
top
100

hits.
The
scores
are
based
on
3907
patent
applica(ons
from
latest
10
years,
(tle/abstract
vs
full

text
input.

Input
format
Recall
10
Recall
25
Recall
50
Recall
100
Avg
length

Long
31,29%
39,39%
44,05%
48,88%
120293

Short
23,99%
30,59%
34,86%
39,72%
2007

Thanks
For
Listening!

QuesBons?

Thanks
for
listening!
QuesBons?

Torsten
Lindholm

Head
of
Customer
Success

Torsten@Ipscreener.com

Book
a
demo
@:

hhps://meeBngs.hubspot.com/torsten

What's hot

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

IC-SDV 2019: Minesoft

Dr. Haxel Consult

Biomax provides computational solutions for better decision making and knowledge management in the life science industry. Biomax helps customers generate value from proprietary and public resources by extracting the knowledge indispensable for efficient data exploration and interpretation. They focus on integrating information to enable a knowledge-based approach to develop innovative life science products. The company supports its customers with a platform that combines software products with knowledge resources, including oncology, nutrigenomics, plant research and functional genomics. With the launch of the NeuroXM Brain Science Suite in 2018 Biomax offers products tailored for the field of connectome research. The new Semantic Searching Platform AILANI provides a corporate-wide knowledge repository accessible for everyone, any time and from anywhere. Biomax’s worldwide customer community includes companies and research organizations that are successful in the areas of drug discovery, diagnostics, fine chemicals, food and plant production.

AI-SDV 2020: Biomax

Dr. Haxel Consult

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

AI-SDV 2021: Dolcera

Dr. Haxel Consult

Jochen Lennhof (Minesoft, Germany) Patent data is a critical source of information to stimulate innovation and for competitive intelligence. Patents are often the first and only source of disclosure of a new invention and hence, ignoring them will only delay innovation and give an incomplete competitive intelligence picture. Delivered from the perspective of a patent analyst, we will use case studies to describe the use of patent data to compile a competitive landscape, to stimulate innovation by learning from others and to help identify valuable IP in a portfolio. We will discuss the challenges in using patents for competitive intelligence and the recent innovative features and functionality in PatBase which can help, including: Using thesauri, semantic and non-patent literature searching to compile a comprehensive competitive landscape. The use of Analytics for customised, multidimensional analysis and to visually compare multiple datasets. Text-mining to automatically identify and highlight concepts within any full text patent. Citation analysis to identify key competitors, collaborators or potential infringers. This presentation will demonstrate how any user can benefit from the innovative features and functionality in PatBase to interrogate and visualize the competitive landscape for any technical area.

ICIC 2017: The Use of Patent Information for Innovation and Competitive Intel...

Dr. Haxel Consult

II-SDV 2014 Hybrid Intelligence – foresight for opportunities (Youri Aksenov ...

Dr. Haxel Consult

As access to the internet grows globally, the number of information sources are growing exponentially. In addition, innovations driven by technology platforms such as IoT, AI, MEMS, etc. are bringing new competitors into spaces that were never considered before. Whoever thought that an automotive OEM would feel threatened by Apple or Google. This period of expansion in both information sources and technologies presents a tremendous opportunity and challenge for global corporations. Information needs to be tracked on a regular and consistent basis from various (previously ignored) sources to identify new opportunities, innovations and threats. SciTech Patent Art will share its learnings from the establishment of one such well-thought through Competitive Technology Intelligence program for a global corporation. The presenter will discuss how scope was established, approach used, learnings/refinements to make the program more useful and at the end, summarize key elements of establishing and making such a program successful. Use of tools such as Artificial Intelligence, which is playing a critical role in program sustenance, will be reviewed in the context of establishing such a cost-effective global CTI program.

IC-SDV 2018: Srin Achanta (SciTech Patent Art) Global Competitive Technology ...

Dr. Haxel Consult

Patent Search - Before beginning and conducting search

TT Consultants

II-SDV 2012 Patent Prior-Art Searching with Latent Semantic Analysis

Dr. Haxel Consult

Cognitive Overview

IBM Watson

II-SDV 2012 Expert System Driven Insights into Patent Quality and Competitive...

Dr. Haxel Consult

This paper revisits the issues discussed in our 2013 and 2016 presentations "Challenges in Visualizing Pharmaceutical Business Information," where we analyzed some of the unique challenges in visualizing competitive intelligence information for the pharmaceutical industry. A key challenge for pharmaceutical companies is to evaluate the competitive landscapes for drug launches many years in the future, based on a combination of publicly available drug pipeline and clinical trials data and internal company knowledge. This information is often conveyed in hand-drawn PowerPoint slides, which are very time consuming to create and update as the competitive landscape changes. In this year's presentation, we'll discuss the development of a "dashboard" of visualizations (piano charts, trial completion timelines, maps, etc.) to facilitate the analysis and visualization of competitive drug landscapes.

IC-SDV 2018: Diane Webb (BizInt) Challenges in Visualizing Pharmaceutical Inf...

Dr. Haxel Consult

II-SDV 2014 The Digital Workplace – The death of desktop search? (Martin Whit...

Dr. Haxel Consult

Customer lifetime Value/Revenue(LTV/R) is the present value of the future profits/revenue from a customer. Estimating it, is important for businesses to optimise the marketing costs in acquiring and retaining the customers. Complex consumer behaviour and innumerable ways a consumer interacts with the business makes things challenging to estimate it. Years of ongoing research in this field has led to the development of various ML tools and techniques. We would like to take this opportunity to walkthrough some of these techniques and their applications in specific business contexts. Condé Nast is a global media company that produces some of the world’s leading print, digital, video and social brands. These include Vogue, GQ, The New Yorker, Vanity Fair, Wired and Architectural Digest (AD), Condé Nast Traveler and La Cucina Italiana, among others. Subscription revenue is one of the major revenue streams for the organization and we’d like to demonstrate the implementation of LTV/R model for the subscription revenue for one of the brands using survival models and along with that illustrate the following. Estimate the average lifetime (ALT) of a brand’s subscriber. Estimate the average lifetime of various segments within the brand and identify the most valuable/least valuable segments, so marketing teams could device appropriate targeting strategies. Finally attempt to estimate the lifetime at a subscriber level. Key insights & findings through the analysis. Demo of sample code Leveraging databricks delta files for our big data processing needs.

Modelling Customer Lifetime Revenue for Subscription Business

Databricks

What do PLI, MetOpera, ASCO, and PLOS have in common? Content management and content discovery needed major improvements. User were not getting the results they needed. The content production team including editorials, managing editorials – the whole team – could no longer cope with the volume and variety. Content quality was suffering. Brief discussions of each organization’s challenges set the stage for AI-based, human curated solutions. What worked, what didn’t, and the how and the why will be presented.

AI-SDV 2021 - Marjorie Hlava - Semantic Search and Content Management – Case...

Dr. Haxel Consult

II-SDV 2014 Search and Data Mining Open Source Platforms (Patrick Beaucamp - ...

Dr. Haxel Consult

BizInt Solutions, Inc. is a small software company based in Seattle. We develop, market and support the BizInt Smart Charts software products, which help information specialists create, customize and distribute tabular reports from the leading patent and drug pipeline databases. Our customers include pharmaceutical, biotech, chemical, petroleum, and device manufacturing companies in North America, Europe, and Japan.

IC-SDV 2018: BizInt

Dr. Haxel Consult

KnowMade is specialized in analysis of patents and scientific information. We provide patent search, IP landscape, patent valuation, IP due diligence, scientific literature landscape, identification of technologies available for transfer/licensing/sale, alerts and updates. Our service offer consists of custom studies, analysis reports, on-demand tracking and strategy consulting. Knowmade combines information search services, scientific expertise, powerful analytics and visualization tools, and proprietary methodologies for analyzing patents and scientific information. With a solid focus on Microelectronics, Nanotechnology and Biotechnology, KnowMade supports research laboratories, industrial companies and investors in their business development.

KnowMade company presentation

Knowmade

Cognitive Reservoir Analytics

IBM Watson

II-SDV 2012 Automatic Query Re-Ranking in a Patent Database by Local Frequenc...

Dr. Haxel Consult

PatSnap for RD

Timo Bruggemann

What's hot (20)

IC-SDV 2019: Minesoft

AI-SDV 2020: Biomax

AI-SDV 2021: Dolcera

ICIC 2017: The Use of Patent Information for Innovation and Competitive Intel...

II-SDV 2014 Hybrid Intelligence – foresight for opportunities (Youri Aksenov ...

IC-SDV 2018: Srin Achanta (SciTech Patent Art) Global Competitive Technology ...

Patent Search - Before beginning and conducting search

II-SDV 2012 Patent Prior-Art Searching with Latent Semantic Analysis

Cognitive Overview

II-SDV 2012 Expert System Driven Insights into Patent Quality and Competitive...

IC-SDV 2018: Diane Webb (BizInt) Challenges in Visualizing Pharmaceutical Inf...

II-SDV 2014 The Digital Workplace – The death of desktop search? (Martin Whit...

Modelling Customer Lifetime Revenue for Subscription Business

AI-SDV 2021 - Marjorie Hlava - Semantic Search and Content Management – Case...

II-SDV 2014 Search and Data Mining Open Source Platforms (Patrick Beaucamp - ...

IC-SDV 2018: BizInt

KnowMade company presentation

Cognitive Reservoir Analytics

II-SDV 2012 Automatic Query Re-Ranking in a Patent Database by Local Frequenc...

PatSnap for RD

Similar to AI-SDV 2020: IPscreener

New tools are indeed present promising opportunities. However, the use of AI also raises new concerns regarding the transparency of the expected usability and reliability. This presentation will elaborate around some the associated questions; the impact of the training data, the black box symptoms, verification of performance and privacy considerations when using AI. But of course also some insights to best practice of AI search tool and how to take control of the process for better efficiency.

AI-SDV 2021: Linus Wretblad - Best practice on new intelligent tools in IP ma...

Dr. Haxel Consult

The top questions we get about ChatGPT-powered enterprise scenarios are all about using the company's own data as the basis for the responses. In this session, we'll explore various options starting from simply injecting data into the prompt to the advanced architectures with multiple Cognitive Services chained together and fine-tuning models - all for you to choose the flexible, scalable, and cost-efficient solution that works the best for you.

Using the power of OpenAI with your own data: what's possible and how to start?

Maxim Salnikov

DCXS best selfcare-solutions DynamicFAQ

LilianBernardin

In this video from HPC Advisory Council Spain Conference, Dan Olds from OrionX presents insights from their Q2-Q3 2017 Survey on Artificial Intelligence/Machine Learning/Deep Learning – one of the industry’s most comprehensive AI/ML/DL surveys to date with more than 144 data points. Watch the video: https://insidehpc.com/2017/10/industry-analysis-ai-deep-learning-voice-market/ Learn more: http://orionx.net/ Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

OrionX AI & Machine Learning Customer Survey

inside-BigData.com

What exactly is a data product, and how to build one in a data driven manner? In this session I will dive into those questions. This will be done, based on recent experience in a project where a particular search technology was replaced by a data drive search pipeline. First some context will be sketched by laying out the starting point of this project. How, we moved from a once-a-day update of the index, to real-time updates in an architecture based on ElasticSearch, kafka, microservices, command query responsibility separation, and real-time monitoring using kafka-streams. Next, various parts of the new pipeline will be highlighted while discussing what kind of data was measured and how it steered the engineering efforts. Various lessons learned, such as in which order to do things when building a data product and how to deal with the relation between engineering and data science will be discussed. Finally, we will have a look on how machine learning techniques such as learning-to-rank, and entity recognition, where added to the mix. After this session you will have a better understanding of the pitfalls when building a data product and some concrete anchors to drive the engineering efforts of your own team.

Data driven search

Bertjan Broeksema

Speaker: Barnam Bora, Head of AI/ML, APAC, AWS Customer Speaker: Guangda Li, Co-founder & CTO, ViSenze Note: This is part 2 of the deck. WS offers different paths for building and deploying scalable ML solutions. This session provides an insight to how AWS customers are building intelligent systems powered by AI and ML. Learn how these services, in conjunction with the large number of complementary AWS technologies, provide a great platform for our customers to build their own AI and ML powered solutions and drive business value. Towards the latter part of this session, hear how customers are deploying their ML on AWS and can now leverage Marketplace to monetise their models.

AWS Summit Singapore 2019 | Building Business Outcomes with Machine Learning ...

Amazon Web Services

Microsoft 365 Copilot is a revolutionary productivity assistant that leverages large language models (LLMs) and your organisational data to help you create, communicate, and collaborate more effectively across Microsoft 365 apps. Copilot can assist you with various tasks, such as drafting emails, making presentations, processing data, and finding insights. But to make the most of this game-changing technology, you must master three key aspects: adoption, data security, and governance. In this two-part session, a Microsoft 365 expert, Nikki will guide you through these aspects and show you how to use Copilot to boost your productivity, creativity, and confidence. In the first part of the session, Nikki will focus on adoption. She will explain that Copilot is not just a tool—it’s a mindset. It’s about embracing AI as a partner, not a threat. It’s about creating a culture of AI literacy in your organisation, where everyone can use Copilot’s features and understand its limits. It’s about using Copilot to enhance your skills and outcomes, not to replace them. Key takeaways: – What Microsoft 365 Copilot is, and how it works – How to build your business case for Microsoft 365 Copilot – How to adopt Microsoft 365 Copilot in your organisation and overcome common barriers and challenges

Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...

Nikki Chapple

Enterprise Video Search ROI

RAMP Holdings, Inc.

The talk "Strategies for using alternative queries to mitigate zero results and their application to online marketplaces" took center stage at Haystack EU 2023. The presenters: Jean Silva @wallapop, ex @trivago Working with various programming languages since 2008 and fall in love with the search world in 2014. Jean has worked in different industries such as travel, e-commerce, and classified listings. René Kriegler @OpenSourceConnections Works in search since ~2008 Jean and René shed light on the challenges associated with zero results queries, emphasizing the critical importance of avoiding empty search result lists to enhance user experience and prevent negative business impacts. This issue is particularly pertinent in user-generated content platforms like Wallapop, where the diversity of items and user backgrounds complicates search outcomes. Addressing these challenges, they discussed recent advancements in vector search and the availability of powerful language models for semantic search. While these developments offer potential solutions by ensuring some results are always returned, they also pose usability and computational challenges, especially in platforms with short-lived content. Building upon strategies presented at Haystack US 2019, Jean and René explored approaches to providing users with alternative queries, such as query relaxation and term substitution, leveraging Large Language Models. Additionally, they shared insights from experiments involving simpler query relaxation techniques based on query term statistics. These strategies not only improve user experience but also offer versatile alternatives to vector search, applicable beyond classified ads platforms. For those interested in delving deeper into these discussions, the conference talk can be accessed via the following link: https://haystackconf.com/eu2023/talk-9/ Jean Silva Search Engineer @Wallapop.

Strategies for using alternative queries to mitigate zero results

Jean Silva

In this session, we'll explore different scenarios where the features of Generative AI can provide added value to an IT solution. We'll also learn how to begin developing your own application powered by AI. Using Azure OpenAI service as an illustration, we'll examine the various APIs it offers, review the best practices of Prompt Engineering, explore different ways to incorporate your own data into the process, and take a glance at several tools and resources that make the developer experience more seamless.

Building Generative AI-infused apps: what's possible and how to start

Maxim Salnikov

"How to Build a Good Practice Software Project Portfolio" as presented by Hennie Huijgens at the 36th International Conference on Software Engineering, may 2014 in Hyderabad, India. Research on a repository of more than 350 software projects revealed 7 success factors and 9 failure factors for software projects. Find out how you can learn from this and build more productive and effective software project portfolios for your company too.

How to build a good practice software project portfolio webversion

Hennie Huijgens

The widespread adoption of test automation has created many challenges — for everything from development lifecycle integration to scripting strategy. One pitfall of automation is that teams often rush to automate everything they can. This is the automation firehose. However, just because a scenario CAN be automated does not mean it SHOULD be automated. For scenarios that should be automated, teams must adopt implementation plans to ensure tests are reliable and deriving value. Join this webinar led by Perfecto’s Chief Evangelist, Eran Kinsbruner, along with Thomas Haver, Manager of Automation & Delivery. In this session, the audience will: -Understand which test scenarios to automate. -Learn how to maximize the benefits of automation. -Receive a checklist to determine automation feasibility and ROI.

The Automation Firehose: Be Strategic & Tactical With Your Mobile & Web Testing

Perfecto by Perforce

Request for Proposal (RFP) management - Ask the right questions and choose wi...

Harold van Heeringen

Business success relies heavily on taking the right action, at the right time, all the time. And actions are dictated by data. But the batch-oriented, collect-store-contemplate model employed by Big Data Analytics technologies is incomplete because it does not make use of live data in real time. Without live, real-time data insights gathered are not up-to-date, and cannot accurately inform applications and services that would benefit from continuous, real-time context for time-sensitive decisions. To thrive, businesses need to be able to use both live and historical data in their applications and services, continuously, concurrently, and correctly and the only technology currently capable of handling it is streaming analytics. Streaming analytics computes data right now, when it can be analyzed and put to good use to make applications of all kinds contextual and smarter. This webinar held in collaboration with Forrester, Inc., showcased how streaming analytics applications can be built in minutes, to: - Aggregate, enrich, and analyze a high throughput of data from multiple, disparate live data sources and in any format to identify patterns, detect opportunities, automate actions, and dynamically adapt - Easily ingest streaming data from multiple disparate sources to multiple sources, within and between cloud and on-premises environments - Analyze and act on data as it arrives, without needing to store, eliminating unnecessary security risks and storage costs - Enable real-time analytics with existing business intelligence and data assets.

Streaming analytics webinar | 9.13.16 | Guest: Mike Gualtieri from Forrester

Cubic Corporation

Scope master introduction presentation feb 2020 w vid

Colin Hammond

A confluence of events is accelerating the growth of AI in the Enterprise - (i) The COVID pandemic is accelerating the digital transformation of enterprises, (ii) increased digital sales & digital interaction is fueling interest in operationalizing AI to drive revenue and cost efficiencies and (iii) Enterprise databases and enterprise apps are infusing AI to transparently augment predictive capabilities for clients. Enterprise Power Systems are pillars of the global economy hosting our trinity of operating systems

AI at Scale in Enterprises

Ganesan Narayanasamy

einstein-cheatsheet.pdf

experio1

Make Design A First Class Citizen To Ensure Analytics Success

Siteworx LLC

Blog writing by sumeet narang

Sumeet Narang

Blog writing by Sumeet Narang

Sumeet Narang

Similar to AI-SDV 2020: IPscreener (20)

AI-SDV 2021: Linus Wretblad - Best practice on new intelligent tools in IP ma...

Using the power of OpenAI with your own data: what's possible and how to start?

DCXS best selfcare-solutions DynamicFAQ

OrionX AI & Machine Learning Customer Survey

Data driven search

AWS Summit Singapore 2019 | Building Business Outcomes with Machine Learning ...

Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...

Enterprise Video Search ROI

Strategies for using alternative queries to mitigate zero results

Building Generative AI-infused apps: what's possible and how to start

How to build a good practice software project portfolio webversion

The Automation Firehose: Be Strategic & Tactical With Your Mobile & Web Testing

Request for Proposal (RFP) management - Ask the right questions and choose wi...

Streaming analytics webinar | 9.13.16 | Guest: Mike Gualtieri from Forrester

Scope master introduction presentation feb 2020 w vid

AI at Scale in Enterprises

einstein-cheatsheet.pdf

Make Design A First Class Citizen To Ensure Analytics Success

Blog writing by sumeet narang

Blog writing by Sumeet Narang

More from Dr. Haxel Consult

AI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management

Dr. Haxel Consult

Knowledge Graphs are an increasingly relevant approach to store detailed knowledge in many domains. Recent advances in NLP allow to enrich Knowledge Graphs through automated analysis of large volumes of literature, reducing a lot the efforts in traditional manual information capturing. In our presentation we report the approach taken in a project with partner Fraunhofer SCAI in the life sciences where a knowledge graph organising detailed facts about psychiatric diseases has been computed. Information of cause-effect relations between proteins, genes, drugs and diseases has been encoded in the BEL (Biological Expression Language) and imported into a Graph database to approach an indication-wide Knowledge Graph for the selected therapeutic area. Ultimately, updating the graph will amount to just rerunning the analysis on the newly published literature.

AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...

Dr. Haxel Consult

In 2019 the UK was the first major economy to embrace a legal obligation to achieve net zero carbon emissions by 2050. More broadly, the 2021 UK Innovation Strategy sets out the UK government’s vision to make the UK a global hub for innovation by 2035 with a target of increasing public and private sector R&D expenditure to 2.4% of GDP to support the UK being a science superpower with a world-class research and innovation system. IP rights create an incentive for R&D which ultimately leads to innovation. Analysis and insights from IP data can therefore help provide a better understanding of how the IP system is being used and where and what innovation is taking place. Research and analysis of IP data is a key input to the ongoing work of the UKIPO’s Green Tech Working Group which seeks to: further the UK’s status as a global leader by making the UK’s IP environment the best for innovating green technology; develop and deliver IP policies to support government’s ambition on climate change and green technologies; and to help innovators best protect and commercialise their green tech innovations both at home and internationally. The UKIPO has been developing a broad portfolio of ‘green’ IP analytics research. A series of patent analytics reports have been published looking at green technologies, and analysis of how the UK’s Green Channel scheme for accelerated processing of green patent applications has been conducted. Patents have been used to identify technological comparative advantage within different green technologies at a country level, and new insights uncovered by mapping green technology patents to the UN Sustainable Development Goals (SDGs). Trade mark data provides a timeliness and closeness to market factor that patent data does not, and complementary trade mark analysis of UK ‘green’ trade marks, identified using a machine learning algorithm, provides a commercialisation angle to our research.

AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...

Dr. Haxel Consult

Word embeddings, deep learning, transformer models and other pre-trained neural language models (sometimes recently referred to as "foundational models") have fundamentally changed the way state-of-the-art systems for natural language processing and information access are built today. The "Data-to-Value" process methodology (Leidner 2013; Leidner 2022a,b) has been devised to embody best practices for the construction of natural language engineering solutions; it can assist practitioners and has also been used to transfer industrial insights into the university classroom. This talk recaps how the methodology supports engineers in building systems more consistently and then outlines the changes in the methodology to adapt it to the deep learning age. The cost and energy implications will also be discussed.

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

More from Dr. Haxel Consult (20)