Recently released from EPO, EP Register data contains information on all the steps of a patent application.
This seminar aims to show at a glance how Register can be used in combination with other datasets or by themselves in order to enrich patent studies or get new non trivial indicators.
The presentation will be centered on examples in the subfield of wind energy.
This work shows a methodology used to match PATSTAT inventor names to a full list of researchers working in Italian universities. The goal is to have higher recall, leaving institutions/researchers to validate the data.
Focus will not be on results (evaluation still in progress) but on data processing, selection and match algorithm, highlighting some difficulties and relative workarounds.
Recently released from EPO, EP Register data contains information on all the steps of a patent application.
This seminar aims to show at a glance how Register can be used in combination with other datasets or by themselves in order to enrich patent studies or get new non trivial indicators.
The presentation will be centered on examples in the subfield of wind energy.
This work shows a methodology used to match PATSTAT inventor names to a full list of researchers working in Italian universities. The goal is to have higher recall, leaving institutions/researchers to validate the data.
Focus will not be on results (evaluation still in progress) but on data processing, selection and match algorithm, highlighting some difficulties and relative workarounds.
This presentation explains concepts of Patents and Market Exclusivity. This presentation is compiled from publicly available material on the world wide web.
Match of PATSTAT data (2019 spring) and PatentsView (jan 2019) is discussed here, with focus on how this match can help to enrich PATSTAT data with information not contained in USPTO patents (and the other way round).
Patents applicants: how to create the full time seriesGianluca Tarasconi
Patents change applicants data within time;
Main reason for change are ownership change, name/address change, M&A …
Applicant’s names contained in TLS206 is the ‘last available’ data;
PATSTAT Global + EP Register make available several sources to build a chain of names and a timeline for patents contained;
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
This presentation explains concepts of Patents and Market Exclusivity. This presentation is compiled from publicly available material on the world wide web.
Match of PATSTAT data (2019 spring) and PatentsView (jan 2019) is discussed here, with focus on how this match can help to enrich PATSTAT data with information not contained in USPTO patents (and the other way round).
Patents applicants: how to create the full time seriesGianluca Tarasconi
Patents change applicants data within time;
Main reason for change are ownership change, name/address change, M&A …
Applicant’s names contained in TLS206 is the ‘last available’ data;
PATSTAT Global + EP Register make available several sources to build a chain of names and a timeline for patents contained;
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
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One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
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SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdf
Trackin patent applicant changes with a temporal database
1. Tracking Applicants
history and structure
using a temporal
database
Gianluca Tarasconi – Kites Univ. Bocconi / OST
Blog: http://rawpatentdata.blogspot.com
2. OST Common identifier (I)
This presentation will deal with some preliminary results originated in
OST common identifier project.
The Observatoire des Sciences et des Techniques (OST) designs and
produces R&D indicators.
By providing both indicators and expertise OST serves the actors of
research system.
To carry out its missions, OST maintains a database on international
research, constructed from multiple sources (mainly CORDIS,
PATSTAT and ISI-WOS)
Common Identifier project aims to create a bridge among all
kind of entities existing across the 3 main datasets.
3. OST Common identifier (II)
Data categories existing across patent, scientific
publications and Framework programs data:
PATSTAT CORDIS WOS
inventors/appl. participants
Geographic data affiliations addresses
addresses addresses
inventors,
Individuals contacts authors
applicants
Companies applicants participants affiliations
Sci /tech
IPC thematic priorities subject cathegories
taxonomies
4. OST Common identifier (III)
Pilot for project feasibility is building a data
structure that may embed data for legal
persons, starting with PATSTAT
applicants (and soon WOS affiliations)
Patstat ISI WoS
Applicants C.I. data
affiliations
structure
5. Data constrains (I)
1) DEFINE ATOMIC ENTITIES AND
NON AMBIGUOS JOINS
• Data involved may contain the same legal entity at different
level (ie for ABB: exists ABB holding, IP depts, divisions,
JVs…)
• C.I. dataset should use a entity size allowing unique data
match across different sets.
• C.I. dataset should also make possible a hierarchic structure
of entities allowing join at different level to main datasets.
6. Data constrains (II)
THE REGENTS OF THE UNIVERSITY OF CALIFORNIA,
001 BERKELEY
FROM 002 UNIVERSITY OF CALIFORNIA, BERKELEY OFFICE OF
TECHNOLOGY LICENSING
MATHEMATICAL SCIENCE PUBLISHERS DEPARTMENT
003 OF MATHEMATICS UNIVERSITY OF CALIFORNIA,
BERKELEY
004 UNIVERSITY OF CALIFORNIA AT BERKELEY
TO 005 UNIV OF CALIFORNIA BERKELEY
001 father to 002
001 father to 003
002 brother to 003
Using a mix of datamining tecniques to disambiguate applicants
and to rebuild relationships among them (keywords lists);
Choose of atomic entity relies also on sector allocation developed
starting from KUL algorithm with further developments.
7. Data constrains (III)
• 2) TIMESERIES
• 2a) DATASET ASINCHRONIES
• Data may enter the database with different time
frame depending from the dataset.
• (IE PATSTAT is a snapshot at moment of data
creation, WOS is an incremental update; so name
changes/M&A could make same entity different in 2
datasets; note also geographic entities change with
time: counties, countries…)
• C.I. tables must have a time-related dimension.
8. Data constrains (IV)
• 2b) DATA TRANFORMATIONS
• Data change within time.
(IE companies may merge, split [most critical case], change
name, change ownership…)
• C.I. tables must have a dimension allowing to follow
transformation of entities.
9. Data constrains (V)
Person_id Patstat ed.
C.I. TABLE CNRS PATSTAT 2010000 201210
CNRS PATSTAT 302452 201304
10012 CNRS
10013 CNRS Bordeaux
10014 Lab 3 CNRS Brodeaux
datasource
10012 2010000 201210
10012 302452 201304
Sarajevo chg from YU to BS in 1992
In other words, we must
have validity dates and NO: Sarajevo YU BS
data source data
YES: Sarajevo YU 1800 1991
Sarajevo BS 1992 9999
10. How to deal with:
• PROPERTIES / EVENTS BASED DATASTRUCTURE
• Data structure proposed should be a TEMPORAL DATABASE(1), allowing to store PROPERTIES of our
ENTITIES (ie applicants) by date, datasource and EVENTS that can change such properties
• PROPERTY NAME (ie ownership, affiliation…)
• PROPERTYVALUE (ie new owner, new affiliation)
• DATEFROM (these are
• DATETO validity date from analist point of view)
• DATASOURCE (ie patstat ediction: that is validity date from DB point of view)
• Events occur to change values of any property and may, by default let other be inherited by the entity
from that moment onward (FI: Acquisition of C from B, that is owned by A, makes C inherit
ownership from C)
• Main event that may occur are: BIRTH, MERGE, DEMERGE, SPINOFF, ACQUISITION BY, DEATH.
• Along with properties must also be defined how properties are inherited among entities (IE CNRS
Bordeaux inherits from CNRS ownership)
• (1) See Richard T. Snodgrass. "TSQL2 Temporal Query Language". www.cs.arizona.edu. Computer Science Department of
the University of Arizona
11. Example (I):
• NOVARTIS
• Novartis pharma is originated by merge of CIBA (1884)
GEIGY (1758) and Sandoz (1876)
• Until 1970 they are 3 separate entities
PCODE PNAME
1 CIBA
2 GEIGHY
3 SANDOZ
4 CIBA SUB 1..N
5 GEIGHY SUB 1…N
6 SANDOZ SUB 1…N
PCODE PROPNAME PROPVALUE STATUSCODE2 STATUSTEXT STATUSPERC DATEFROM DATETO CHGREASON
1 OWNERSHIP FULLOWN 1 100 1884 9999
2 OWNERSHIP FULLOWN 2 100 1758 9999
3 OWNERSHIP FULLOWN 3 100 1876 9999
4 OWNERSHIP FULLOWN 1 100 1884 9999
5 OWNERSHIP FULLOWN 2 100 1758 9999
6 OWNERSHIP FULLOWN 3 100 1876 9999
13. Populating the datasets:
• Building realtionships and changes cannot rely only on
datamining
• A lot of free resources are available
• SEC: http://www.sec.gov/edgar/searchedgar/companysearch.html
• NASDAQ: http://www.nasdaq.com/screening/companies-by-name.aspx?letter=A
• FULL WW UNIV LIST:
http://www.unibo.it/Portale/Guida/Universita+nel+mondo/Unimondo.htm
• INDIAN NAME CHANGES: http://www.smctradeonline.com/change-of-
name.aspx
• SELECTION OF OTHER DBS:
http://www.anderson.ucla.edu/x14519.xml#company
• Another great resource for name change is TLS221
listing more than 500.000 events with name change
of applicant (net names about 85.000 after
cleaning)
14. Next steps:
• Development is in progress with the goal of classifing 85% of
applications for EPO USPTO INPI PCT by June 2013;
• WoS and CORDIS data should be integrated soon after then
phase 2 should start aiming to classify 95% of applications
• Comments, ideas but also proposals of
collaboration are welcome