This document discusses 7 common errors that users may encounter when using PATSTAT patent data. It summarizes issues such as inventors/applicants not always being listed, person_id not uniquely identifying entities, incomplete coverage of CPC codes, missing data for PCT equivalents, missing abstracts and citations, and data transmission gaps from national offices to the EPO. The document provides examples and statistics to illustrate these potential problems when analyzing patent data from PATSTAT.
This is the Highly Detailed factory service repair manual for the1999 INFINITI Q45, this Service Manual has detailed illustrations as well as step by step instructions,It is 100 percents complete and intact. they are specifically written for the do-it-yourself-er as well as the experienced mechanic.1999 INFINITI Q45 Service Repair Workshop Manual provides step-by-step instructions based on the complete dis-assembly of the machine. It is this level of detail, along with hundreds of photos and illustrations, that guide the reader through each service and repair procedure. Complete download comes in pdf format which can work under all PC based windows operating system and Mac also, All pages are printable. Using this repair manual is an inexpensive way to keep your vehicle working properly.
Service Repair Manual Covers:
General Information
Maintenance
Engine Mechanical
Engine Lubrication & Cooling Systems
Engine Control System
Accelerator Control, Fuel & Exhaust Systems
Automatic Transmission
Propeller Shaft & Differential Carrier
Front Axle & Front Suspension
Rear Axle & Rear Suspension
Brake System
Steering System
Restraint System
Body & Trim
Heater & Air Conditioner
Electrical System
Alphabetical Index
File Format: PDF
Compatible: All Versions of Windows & Mac
Language: English
Requirements: Adobe PDF Reader
NO waiting, Buy from responsible seller and get INSTANT DOWNLOAD, Without wasting your hard-owned money on uncertainty or surprise! All pages are is great to have1999 INFINITI Q45 Service Repair Workshop Manual.
Looking for some other Service Repair Manual,please check:
https://www.aservicemanualpdf.com/
Thanks for visiting!
This is the Highly Detailed factory service repair manual for the1999 INFINITI Q45, this Service Manual has detailed illustrations as well as step by step instructions,It is 100 percents complete and intact. they are specifically written for the do-it-yourself-er as well as the experienced mechanic.1999 INFINITI Q45 Service Repair Workshop Manual provides step-by-step instructions based on the complete dis-assembly of the machine. It is this level of detail, along with hundreds of photos and illustrations, that guide the reader through each service and repair procedure. Complete download comes in pdf format which can work under all PC based windows operating system and Mac also, All pages are printable. Using this repair manual is an inexpensive way to keep your vehicle working properly.
Service Repair Manual Covers:
General Information
Maintenance
Engine Mechanical
Engine Lubrication & Cooling Systems
Engine Control System
Accelerator Control, Fuel & Exhaust Systems
Automatic Transmission
Propeller Shaft & Differential Carrier
Front Axle & Front Suspension
Rear Axle & Rear Suspension
Brake System
Steering System
Restraint System
Body & Trim
Heater & Air Conditioner
Electrical System
Alphabetical Index
File Format: PDF
Compatible: All Versions of Windows & Mac
Language: English
Requirements: Adobe PDF Reader
NO waiting, Buy from responsible seller and get INSTANT DOWNLOAD, Without wasting your hard-owned money on uncertainty or surprise! All pages are is great to have1999 INFINITI Q45 Service Repair Workshop Manual.
Looking for some other Service Repair Manual,please check:
https://www.aservicemanualpdf.com/
Thanks for visiting!
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).
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;
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
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
• How SAP Fiori paves the way for using AI in SAP apps
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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!
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
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.
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/
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:
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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!
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
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.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
2. In short
This presentation aims to show out 7
common errors user may incur in when
they use PATSTAT;
This is in ideal the continuation of
‘PATSTAT 7 deadly sins’ from 2013
Nevertheless there is only one sin user
have to avoid when using patent data:
… SLOTH ….
10
3. Inventors / applicants are not always listed (I)
A part of applications miss Inventors and/or
applicants data
SELECT
Sum(If(b.APPLN_ID IS NULL, 1, 0)) AS noperson,
Count(c.APPLN_ID) AS n_APPLN_ID
FROM
patstat.tls207_pers_appln b
RIGHT JOIN patstat.tls201_appln c ON b.APPLN_ID = c.APPLN_ID
WHERE
Appln_kind <>”D2”
10
n appln_id no person %
221.595.818 18.202.821 9%
Autumn 2016 data
4. Inventors / applicants are not always listed (II)
10
Limit to A,W applications, offices with >
10.000 applications
appln_auth appln_kind noperson n_APPLN_ID perc
LU A 57057 88522 64%
BE A 453348 784265 58%
NL A 382777 681266 56%
SE A 552912 1345982 41%
AT A 154041 751803 20%
CH A 341522 1839496 19%
FR A 793741 4501015 18%
DD A 99003 651159 15%
EA A 17272 118772 15%
GT A 2423 17932 14%
CA A 928350 7300631 13%
CS A 46955 381949 12%
GB A 579052 4924739 12%
DK A 68952 589986 12%
5. Person_id is not an entity id (I)
Person_id in patstat do not identifies an
entity but a distinct name – address-
country
Same entity more person_ids
Same person_id more entity
10
6. Person_id is not an entity id: top inventors
SELECT
a.PERSON_NAME, a.PERSON_ADDRESS, a.PERSON_CTRY_CODE,
Count(c.APPLN_ID) AS Count_APPLN_ID,
Min(c.EARLIEST_FILING_YEAR) AS Min_EARLIEST_FILING_YEAR,
Max(c.EARLIEST_FILING_YEAR) AS Max_EARLIEST_FILING_YEAR
FROM
patstat.tls207_pers_appln b
INNER JOIN patstat.tls206_person a ON a.PERSON_ID = b.person_id
INNER JOIN patstat.tls201_appln c ON b.APPLN_ID = c.APPLN_ID
WHERE b.invt_seq_nr > 0 and c.EARLIEST_FILING_YEAR < 9999
GROUP BY a.PERSON_NAME, a.PERSON_ADDRESS,
a.PERSON_CTRY_CODE
ORDER BY Count_APPLN_ID DESC
10
7. Person_id is not an entity id : top inventors (II)
person_name ctry_code person_id n_app minyear maxyear
THE INVENTOR HAS WAIVED THE RIGHT TO BE MENTIONED 19584860 38067 2002 2015
KVASENKOV OLEG IVANOVICH RU 34298480 29682 2003 2015
WANG WEI 15786453 23156 1985 2015
ZHANG WEI 14837632 21771 1985 2015
NAME NOT GIVEN 13592151 17722 1964 2002
LI WEI 13615436 17298 1985 2015
VERZICHT DES ERFINDERS AUF NENNUNG 21108740 17260 1964 1993
WANG JUN 18500497 15755 1985 2015
LIU WEI 18697297 15319 1985 2015
LI JUN 18510590 14854 1985 2015
WANG LEI 18754169 14710 1986 2015
ZHANG LEI 18557049 14244 1987 2015
ZHANG JUN 18719351 12815 1985 2015
WANG JIAN 13113349 11936 1986 2015
WANG YONG 12656416 11844 1985 2016
ZHANG JIAN 14914085 11837 1985 2015
CHEN WEI 14837625 11706 1985 2015
WANG HUI 18663499 11452 1987 2015
LIU YANG 13930482 11126 1985 2015
LIU JUN 18710534 10927 1985 2015
LI LI 13632985 9958 1985 2015
AKTIENGESELLSCHAFT I. G. FARBENINDUSTRIE DE 17443080 9958 1897 1942
WANG TAO 18331978 9856 1985 2015
ZHANG YONG 18712075 9795 1985 2015
ZHANG LI 18704857 9716 1985 2015
10
8. Person_id is not an entity id: network analysis
SELECT
a.person_id, Count(DISTINCT b.person_id) AS n_coinv,
t6.PERSON_NAME, t6.PERSON_ADDRESS, t6.PERSON_CTRY_CODE
FROM
patstat.tls207_pers_appln a
INNER JOIN patstat.tls207_pers_appln b ON a.APPLN_ID = b.APPLN_ID
INNER JOIN patstat.tls206_person t6 ON t6.PERSON_ID = a.person_id
WHERE a.invt_seq_nr > 0 AND b.invt_seq_nr > 0
GROUP BY a.person_id, t6.PERSON_NAME, t6.PERSON_ADDRESS,
t6.PERSON_CTRY_CODE
ORDER BY person_id1 DESC
10
9. Person_id is not an entity id: network analysis
person_id n coinv name address
15786453 32384 WANG WEI
14837632 27602 ZHANG WEI
13615436 25550 LI WEI
18697297 21915 LIU WEI
18754169 21237 WANG LEI
18557049 20629 ZHANG LEI
18500497 20562 WANG JUN
18510590 19789 LI JUN
13113349 17270 WANG JIAN
13930482 16618 LIU YANG
18719351 16576 ZHANG JUN
14914085 16464 ZHANG JIAN
12656416 16208 WANG YONG
18663499 15686 WANG HUI
18704857 15224 ZHANG LI
14837625 15027 CHEN WEI
13632985 14882 LI LI
18331978 14780 WANG TAO
12656569 14656 LI YAN
18712075 14616 ZHANG YONG
10
Whang and Zhang Wei
have in common 120
Sipo patents; top 3
have 3 degrees of
distance networks of
about 900K inventors
person_id name 3 DoD
15786453 WANG WEI 943.562
14837632 ZHANG WEI 925.099
13615436 LI WEI 916.268
10. Person_id is not an entity id:
possible solution
At analisys level the couple person_id –
appln_id identifies for sure one entity
Starting at this level of disaggregation entities
should be disambiguated further with other
means
(FI appln 1
& 2 from same
applicant)
10
11. CPC codes coverage is incomplete (I)
The Cooperative Patent Classification
(CPC) was initiated as a joint partnership
between the USPTO and the EPO;
It has a more complete set of technologies
(fi green energy, nanotech);
It started in 2011, it does not apply to all
type of patents (ie Utility models) and it has
backward data to be rebuilt.
10
12. CPC codes coverage is incomplete (II)
Coverage of CPC allover patstat is far from
good and much smaller than IPC coverage
10
appln kind n app n with cpc cpc rate ipc rate
'A' 66.750.533 39.505.860 0.5918 0.8413
'U' 13.503.902 1.140.172 0.0844 0.9115
'W' 3.012.030 2.990.252 0.9928 0.9900
13. CPC coverage (type A)
10
APPLN
KIND
APPLN
AUTH
Count
APPLN_ID
count_app
with_cpc ratio
A AR 143884 103372 72%
A AT 587486 174977 30%
A AU 1374657 1114774 81%
A BE 646320 551552 85%
A BR 547104 374724 68%
A CA 3209303 1269659 40%
A CH 1048915 571085 54%
A CN 6343484 2155452 34%
A DE 4617268 3861583 84%
A DK 319177 119062 37%
A EP 3227647 3113078 96%
A ES 423071 202677 48%
A FI 251054 112028 45%
A FR 3098874 2387891 77%
A GB 3384892 2116655 63%
A GR 69272 24607 36%
A HK 133738 119890 90%
A HU 131491 73025 56%
A IE 91782 43044 47%
A IL 216193 122462 57%
A IN 106610 46024 43%
A IT 605707 326251 54%
A JP 13944907 4355789 31%
A KR 2831385 1425304 50%
A LU 68712 59814 87%
A MX 262534 236276 90%
A MY 50974 40612 80%
A NL 595393 528493 89%
A NO 222376 171392 77%
A NZ 141064 110223 78%
A PL 246209 79640 32%
A RU 658280 199365 30%
A SE 858651 330375 38%
A SG 102679 90508 88%
A SU 1363419 100573 7%
A TW 737206 497644 68%
A UA 55255 18206 33%
A US 12700957 11612249 91%
A ZA 293611 191492 65%
after Y2K
80%
71%
80%
94%
79%
25%
68%
32%
93%
6%
95%
88%
22%
98%
43%
80%
86%
51%
32%
77%
43%
58%
32%
54%
93%
94%
74%
90%
83%
81%
32%
32%
17%
90%
53%
66%
31%
98%
67%
SELECT a.APPLN_KIND, a.APPLN_AUTH,
Count(distinct a.APPLN_ID) AS Count_APPLN_ID, count(distinct
b.appln_id) count_app_with_cpc, count(distinct
b.appln_id)/Count(distinct a.APPLN_ID) as ratio
FROM
patstat.tls201_appln a LEFT JOIN patstat.tls224_appln_cpc b
ON a.APPLN_ID = b.appln_id
WHERE a.APPLN_KIND in ('A','W', 'U')
GROUP BY a.APPLN_KIND, a.APPLN_AUTH
Situation is not homegenueus
After Y2K things improve a bit
14. CPC coverage type U , W
APPLN
KIND
APPLN
AUTH
Count
APPLN_ID
count_app
with_cpc ratio after Y2K
U BR 103233 5179 5% 10%
U CN 5894022 251879 4% 4%
U DE 1406011 618249 44% 43%
U ES 327087 32007 10% 15%
U IT 139608 12912 9% 14%
U JP 4289887 113890 3% 7%
U KR 506761 44226 9% 16%
U RU 166613 5567 3% 4%
U TW 407155 30996 8% 7%
U UA 103880 2037 2% 2%
W CN 160005 155010 97% 97%
W DE 65673 65433 100% 100%
W EP 462944 461118 100% 100%
W FR 82356 81494 99% 98%
W GB 114614 114257 100% 100%
W IB 134635 133070 99% 99%
W JP 503441 497961 99% 99%
W KR 119158 118141 99% 99%
W SE 53444 53264 100% 100%
W US 1002525 1000291 100% 100%
10
Count for offices with > 50K
patents
Pct data coverage is almost full
Utility models not really possible to
use.
15. Missing data for PCT equivalent
EP data where originated from regional
phase of a PCT patent can be partial
At least Abstract and Citations could be
missign and have to be extracted from PCT
equivalent (column INTERNAT_APPLN_ID
in tls201)
10
APPLN_ID APPLN_AUTH APPLN_NR APPLN_KIND IPR_TYPE INTERNAT_APPLN_ID int_phase reg_phase nat_phase GRANTED
347305EP 99931561 A PI 30241523Y Y N 1
16. Missing abstracts
APPLN_KIND Count_APPLN_ID Abstracts ratio
A (ep) 3227647 1849737 57%
W (pct) 3012030 2992978 99%
10
select
a.APPLN_KIND,
Count(a.APPLN_ID) AS Count_APPLN_ID,
Count(b.APPLN_ID) AS Abstracts,
Count(b.APPLN_ID) / Count(a.APPLN_ID) AS ratio
FROM
patstat.tls201_appln a
LEFT JOIN patstat.tls203_appln_abstr b
ON a.APPLN_ID = b.APPLN_ID
WHERE
(a.APPLN_AUTH = 'EP' AND a.appln_kind = 'A') or
a.appln_kind = 'W‘ group by a.APPLN_KIND
About 40% of abstracts for EPO
Should be extracted from PCT
equivalent
17. Missing citations
Euro -PCT applications:
Citations of the WO publications are not repeated in
the later EP publication. Instead a NPL citation with
the text “See also references of WO xxxxxxx ” is
included.
There are more citations in an Euro-PCT than is
obvious.
In 2016 NPL citations that had the value “none” or
“see also references...” have been removed from the
data but related citations have not been replenished…
10
18. Example: EP1103560 equivalent to WO0006594
From citations table we would agree it has only 2
NPL (and one of them is "SEE ALSO
REFERENCES OF WO0006594 ”)
Missing PCT citations (II)
APPLN_ID
PUBLN_AUTH
+ NR PUBLN_ID
NPL_CITN
SEQ _NR NPL_PUBLN_ID NPL_BIBLIO
347305 EP1103560 511640 1 950236893
No further relevant documents
disclosed
347305 EP1103560 511640 3 950236894
See also references of WO
0006594A1
19. Missing PCT citations (III)
As a matter of fact, seeking in espacenet the
corresponding WO we find:
http://worldwide.espacenet.com/publicationDetails/citedDocuments?CC=WO&NR=0006594A1&KC=A1&FT=D&
ND=4&date=20000210&DB=EPODOC&locale=en_EP
20. Data transmission gaps from national offices to
EPO (I)
PATSTAT covers about 100 patent
authorities, but with inequal coverage and
pubblication lags.
Good coverage and short lags for EU
countries; less good and regular for
national patent authorities outside EU
(except big players ie US JP…)
10
21. Data transmission gaps from national offices to
EPO (II)
Data coverage for Docdb available at:
https://www.epo.org/searching-for-patents/helpful-
resources/data/tables/weekly.html
Nevertheless file is difficult to use
10
EDATE CC KC YEAR NB_DOC MIN_PN MAX_PN FIRST_DATE LAST_DATE LAST_ADDED LAST_EXCH
02/09/2017AM A2 2001 1 949 949 10/06/2001 10/06/2001 08/10/2015 15/10/2015
02/09/2017AM A2 2004 1 1402 1402 17/03/2004 17/03/2004 15/02/2011 24/02/2011
02/09/2017AM A2 2006 1 1813 1813 15/09/2006 15/09/2006 05/01/2017 13/04/2017
02/09/2017AM U 2009 1 170 170 26/10/2009 26/10/2009 01/08/2012 09/08/2012
02/09/2017AM U 2010 1 194 194 26/04/2010 26/04/2010 17/08/2017 24/08/2017
gaps
1011
912
0
182
Add a column GAPS for
same office, type of
publication
22. Data transmission gaps from national offices to
EPO (III)
10
A B U A B U
AT 496 2596 IS 912 1653
AU 2256 IT 4344
BA 803 JO 6193
BG 758 429 JP 376 0 451
BR 3183 0 3241 KR 476 874
BY 2678 KZ 2826 6488
CA 0 2394 LT 640 764
CH 66 0 LV 3087 867
CL 1533 MC 3085
CN 189 6370 6370 MD 860 3472
CR 1668 3062 MX 1114 1343
CY 614 MY 2575 0
DD 217 NL 3135 353
DE 0 196 NZ 1013
DK 1032 962 OA 2224
DO 3348 2057 PH 543
EC 1049 2513 PT 613 842 2083
EE 1360 RO 0 1009
EG 1291 RS 322 500 500
ES 336 RU 424
FI 63 402 SE 3100 4025
FR 0 SG 1335
GB 267 267 SI 1314 1287
GC 3084 SV 2487
GE 1554 1672 2344 TH 5597
GR 1136 586 6225 TJ 1470 2101 2030
GT 590 5556 TR 48 0 1225
HK 5061 TW 224 975 1642
HN 421 5510 UA 1449 1816
HU 1130 4996 839 US 70 336
ID 177 1127 UY 251 566
IL 1146 UZ 1641 1673
IN 1035 2153 YU 1229 1465 715
ZA 471
We see some countries for
some type of patents;
Orange / red : very
problematic cases; anyway
one application alone could
interrupt a gaps giving
misguiding results…
23. Data transmission gaps from national offices to
EPO (IV)
02/09/2017AU A A 2005 3 1475702 3432402 17/03/2005 25/08/2005 17/09/2005 02/03/2017 420
02/09/2017AU A A 2010 1 6326480 6326480 29/04/2010 29/04/2010 12/05/2010 20/05/2010 1708
10
02/09/2017IN B 2010 10 237550 264673 01/01/2010 17/12/2010 01/04/2016 17/08/2017 77
02/09/2017IN B 2011 6 239400 247731 07/01/2011 13/05/2011 16/02/2016 08/12/2016 21
02/09/2017IN B 2012 1 253973 253973 14/09/2012 14/09/2012 31/03/2016 07/04/2016 490
Australia: we have a problem
India: we have a problem bigger than expected
Authorities should be examined case by case, also using some count
by year, benchmarked with previous
24. Two possible errors:
different transmission timeframe (decay of patent count in BR starts before GB);
Partial data transmission: counts are different than official data from patent office
Data transmission gaps from national
offices to EPO (V)
BR GB IN
1990 10851 30055 2209
1991 10122 29991 2002
1992 9103 30089 1958
1993 10272 29901 2032
1994 10992 29560 2529
1995 13557 29909 2554
1996 15580 30448 1679
1997 18589 31219 1383
1998 19032 32828 1026
1999 21019 35222 750
2000 20725 36996 690
2001 20626 36884 705
2002 19265 36318 757
2003 20909 35452 1049
2004 22816 33794 1113
2005 23973 31066 1691
2006 23472 30495 1973
2007 16078 30848 2215
2008 10088 28816 2541
2009 8843 27103 2507
2010 5028 25363 2988
2011 539 24010 872
2012 7 7955 28
25. Citations double counts (I)
Citations in Patstat are stored as publication to
publication, by origin.
Simple citation counts on TLS212 can lead on
misguiding results.
Appln_id to appln_id citations help to clarify
10
Select sum(Count_CITED_PAT_PUBLN_ID) n_pub_cited, sum(count_distinct_appln_cited) n_distinct_appln_cited
(SELECT
t11.APPLN_ID, Count(t12.CITED_PAT_PUBLN_ID) AS Count_CITED_PAT_PUBLN_ID,
Count(DISTINCT t11b.APPLN_ID) AS count_distinct_appln_cited
FROM
patstat.tls212_citation t12
INNER JOIN patstat.tls211_pat_publn t11 ON t11.PAT_PUBLN_ID = t12.PAT_PUBLN_ID
INNER JOIN patstat.tls211_pat_publn t11b ON t12.CITED_PAT_PUBLN_ID = t11b.PAT_PUBLN_ID
WHERE t12.CITED_PAT_PUBLN_ID > 0
GROUP BY t11.APPLN_ID
26. APPLN_ID PAT_PUBLN_ID CITED_PAT_PUBLN_ID CITED_APPLN_ID
1 293253293 306927614 16980819
1 293253293 301830017 17000979
1 293253293 298485954 13388690
1 387522680 306927614 16980819
1 387522680 301830017 17000979
1 387522680 298485954 13388690
Citations double counts (II)
Case 1: appln_id 1 has 2
publications showing exactly
same citations
3 291964096 300128315 49123163
3 291964096 295303503 13538355
3 387535649 300128315 49123163
3 387535649 296195755 53888801
3 387535649 306928379 52488529
Case 2: appln_id 1 has 2
publications showing 1 common
and 2 different citations
27. Citations double counts (III)
Case 3: same citation shows
with different origin
Case 4: same citation 4 times
same publication citing, same
origin [data error, could be
sistematic with multiple priority
from some offices]
APPLN_ID PAT_PUBLN_ID CITED_PAT_PUBLN_ID CITED_APPLN_ID
23 289129312 305684503 16736817
23 289129312 293787435 15702748
23 289129312 308462347 48996652APP
23 289129312 293787435 15702748
23 289129312 305684503 16736817
23 289129312 308462347 48996652SEA
23 289129312 327902045 50318244
23 289129312 296433878 20546518
23 289129312 297350607 24023115
23 289129312 296449840 47357637
APPLN_ID PAT_PUBLN_ID CITED_PAT_PUBLN_ID CITED_APPLN_ID
705 306929092 309035661 22852464SEA
705 306929092 309035659 22771241SEA
705 306929092 307833757 50586872SEA
705 306929092 385933558 9632978SEA
705 316022028 337326981 16587695APP
705 316022028 310119518 16447723APP
705 316022028 310119519 48241555APP
705 316022028 314809416 50308201APP
705 316022028 314809413 9718355APP
705 316022028 314809416 50308201APP
705 316022028 314809416 50308201APP
705 316022028 314809413 9718355APP
705 316022028 314809416 50308201APP
28. Citations double counts (IV)
publn_auth n pub cited
n app
cited ratio
GB 6174382 4038130 0,65
US 250838242 1,86E+08 0,74
DE 9428710 7927305 0,84
AT 331095 287720 0,86
JP 29539409 26474356 0,89
10
n pub cited n app cited ratio
225.858.262 189.409.321 0,83862
How does it perform allover
patstat?
Focused in (offices
with ratio < 0.9):
29. Citations doublecount (V)
47K cases of self citations…
10
APPLN_ID PAT_PUBLN_ID CITED_APPLN_ID CITED_PAT_PUBLN_ID
53803383 55765553 53803383 278711582
US7478445 US2008052829
But is the same patent…
30. Solution: count distinct citations by
appln_id citing and cited;
Move to a separate table citation origin
data;
Use also number of citing docdb families
(provided in TLS201).
Citations double counts (VI)
31. Counting correctly number of claims
Number of claims is often used an indicator of value
US data: relates to granted patents only (A documents
until 2000, B1 or B2 documents afterwards) which
were published on or after 1975-01-01
EP data: relates to both published applications (kind
code "A") from 1978 and granted patents (kind code
"B") from 1980.
The number of claims will be "0" for all EP A
documents originating from a PCT published in
English, French or German (so called "Euro-PCTs").
10
32. Counting correctly number of claims (II)
Claims number changes overtime: select
the publication phase more relevant to your
research question; also language may
change number of claims (but PATSTAT
keeps the higher number)
10
PAT_PUBLN_ID PUBLN_AUTH PUBLN_NR PUBLN_KIND PUBLN_DATE PUBLN_CLAIMS Colonna1
311822768 'EP' '1878578' 'A1' '2008-01-16' '11'
311822783 'EP' '1878578' 'B1' '2009-09-30' '22'
100%
more…
311822763 'EP' '0000034' 'A1' '1978-12-20' '14'
311822766 'EP' '0000034' 'B1' '1984-05-23' '7' 50% less
33. Counting correctly number of claims (IV)
Average change in
number of claims
10
SELECT PUBLN_AUTH,
sum(Min_PUBLN_CLAIMS) as min_claims,
sum(Max_PUBLN_CLAIMS) as
max_claims
from
(SELECT
b.PUBLN_AUTH,
b.appln_id,
Max(cast(b.PUBLN_CLAIMS as
unsigned)) AS Max_PUBLN_CLAIMS,
Min(cast(b.PUBLN_CLAIMS as
unsigned)) AS Min_PUBLN_CLAIMS
FROM
patstat.tls211_pat_publn b
WHERE
(b.PUBLN_AUTH = 'EP' OR
b.PUBLN_AUTH = 'US') AND
cast(b.PUBLN_CLAIMS as unsigned) > 0
GROUP BY
b.PUBLN_AUTH, b.appln_id) b
GROUP BY PUBLN_AUTH
PUBLN_AUTH min_claims max_claims ratio
EP 29.005.016 31.407.694 0,9235
US 87.474.259 87.474.617 0,999996
Average number of
claims can be a good
proxy
34. Conclusions
PATSTAT is a great source of data but
cannot be taken ‘as is’.
Data collection is examiner centered, thus
all ‘accessories’ data need a validation.
Seeking data gaps ex-ante can save a lot
of work ex-post
10
35. Conclusions (II)
10
A saint has a past; a sinner has a future
(Lord Illingworth).
Both sentences mean a lot of work to do
when using patent data! (myself).
A saint is a sinner who never gave up
(Yogananda)