II-SDV 2014 Analysing Patent Full Text – Comparison against analysis of abstract and bibliographic...

Analyzing Patent Full-Text

A Study1 April 7, 2014

Analysing Patent Full TextRichard Gynn - LexisNexis



Agenda

1) Full Text Availability

2) Analyzing full text

- Discussion/considerations

- Big picture analysis

- Detailed analysis - Study

3) Conclusions

Full Text content available from vendors has evolved to a point

where most of the top publishing authorities are readily available.

Analysing Patent Full Text. Availability

Full Text Availability – Top 10 Publishing Authorities (available from most big vendors)

April 7, 2014Analyzing Patent Full-Text

A Study4

China, Korea, Japan are not

the big deal they used to be!Text can be available to analyse in English

Full Text Availability – Authorities available from at least one vendor


A Study5

Full Text Availability by volume- > 100k publications


A Study6

0

5

10

15

20

25

JP US

CN

DE

EP

KR

GB

FR

WO

CA

AU

TW SU ES

AT

SE IT

RU

CH

NL

BE FI

BR

DK IN NO PL IL

DD ZA

MX

HU PT

CS

AR IE NZ

CZ

GR

Mil

lio

ns

Full Text Availability by volume- > 100k publications


A Study7

0

5

10

15

20

25

JP US

CN

DE

EP

KR

GB

FR

WO

CA

AU

TW SU ES

AT

SE IT

RU

CH

NL

BE FI

BR

DK IN NO PL IL

DD ZA

MX

HU PT

CS

AR IE NZ

CZ

GR

Mil

lio

ns

31 of these 39 are currently

available from vendorsAccount for vast majority of total volume

Full Text Availability by volume - < 100k publications


A Study8

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

HK

YU

RO SG TR

MY

LU BG

PH

UA

TH CL

EA ID HR SK

CO SI

VN PE

UY

OA

EG IS EC



A Study9

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

HK

YU

RO SG TR

MY

LU BG

PH

UA

TH CL

EA ID HR SK

CO SI

VN PE

UY

OA

EG IS EC

Much smaller amounts currently

available from vendors ~ 300,000If all were to become available would add about 1.5% to full text

that is currently available, e.g. equivalent to Spain or Taiwan



A Study10

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000M

A

AP

VE

EE

LV GT

CU LT

MD CR

PA CY

DO

MC

ZM

ZW SV

SM JO PY

GE

DZ

KE

MT

HN

MW N

I

ME TJ

GC

BO

MN

BA KZ

BY

TT



A Study11

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000M

A

AP

VE

EE

LV GT

CU LT

MD CR

PA CY

DO

MC

ZM

ZW SV

SM JO PY

GE

DZ

KE

MT

HN

MW N

I

ME TJ

GC

BO

MN

BA KZ

BY

TT

One currently available from vendorsIn total these would add about 0.1% to full text that is currently available



• Are we nearly there yet?• There’s a lot of full text available to make use • Most vendors have a significant volumes• Rapidly diminishing returns for each authority added

Full Text Availability

Bringing You The World• We are already in a good place

• In terms of % availability at least

Analysing Patent Full Text. Discussion/considerations



Full Text – What Is It?

Full-text – what is it?• Everything of course?!

― …will concentrate on:

Considerations


A Study15

There’s clearly a lot out

there, so why don’t we see

so much analysis of patent

full text?



Considerations - Language• Can only compare like for like in same language

…non-Latin character issues too• Noise – Patent full-text likes to state things like

…the complete opposite of what it’s about!

Considerations - Language

How I might introduce myself…If I was a patent!

나는사람들이밥, 앤드류, 데이브앨런같은이름이, 이름이. 나는밥, 앤드류, 데이브나앨런아니에요. 내이름은리처드입니다

I have a name, people have names like

Bob, Andrew, Dave and Alan. I’m not

Bob, Andrew, Dave or Alan.

My name is Richard

私は人々がボブ、アンドリュー、デイブとアラ私は人々がボブ、アンドリュー、デイブとアラ私は人々がボブ、アンドリュー、デイブとアラ私は人々がボブ、アンドリュー、デイブとアランのような名前を持っている、名前を持っていンのような名前を持っている、名前を持っていンのような名前を持っている、名前を持っていンのような名前を持っている、名前を持っています。私はボブ、アンドリュー、デイブかアランます。私はボブ、アンドリュー、デイブかアランます。私はボブ、アンドリュー、デイブかアランます。私はボブ、アンドリュー、デイブかアラン

ないよ。私ないよ。私ないよ。私ないよ。私の名の名の名の名前はリチャードです前はリチャードです前はリチャードです前はリチャードです

Considerations

Other Considerations:

• Massive amounts of data

– Time?

– How deal with ?

• Will it contain anything useful?

/benefit outweigh effort?


A Study17

• Tools

– Big picture?

– Details?

Big Picture - Landscape Analysis


A Study18

Big picture, topographic mapping (Discussion)

Here more full text could provide:• Broader country analysis (often full-text not available)• More consistency across authorities – e.g. more claims

― Compare like for like, e.g. not claims, title & abstract against title

• Full text more useful for details

• Themes/commonalities easier to

find using claims, title, abstract

• Whilst useful, vast majority of

landscape analysis done elsewhere,

…i.e. details rather than big picture

Analysing Patent Full Text. Study

The Details - Study

Detailed analysis – looking for what?• New/emerging, different• Competitive/market comparisons• Strength, weakness, opportunity, threat


A Study20

What can I find using the full

text that I couldn’t using title,

abstract and bibliography?

The Details - The Technology


A Study21

Terahertz analysis, e.g. imaging, spectroscopy?Terahertz radiation - between Infra-red and microwave

The Details - The Search


A Study22

• Broad Strategy― Analysis IPCs + Terahertz

Radiation Synonyms

― Keyword Terahertz

Imaging & Spectroscopy

5,955 documents/3,365 families

Study - PatentOptimizer



Analysis Details:

• Small/emerging areas of 6-7 families

• Look at terms & phrases, parts, claim

elements (all numbers represent families)

PatentOptimizer™ Analysis of EP, PCT & US results• English Translations

PatentOptimizer – Terms & Phrases


A Study24

Diagnosis - General



A Study25

Not found in Title, Abstract (or claims) –

All From Spectral Image IncLearned – Something seemingly unique to them

SAME DOCUMENTS



A Study26

Not found in Title, Abstract (or claims) – All

monitoring vitamin K concentration in bloodLearned – A more recent (emerging?) use

Diagnosis - General

PatentOptimizer – Parts


A Study27

Remote monitoring, e.g. of Bluetooth® headset userLearned – Interesting, but not massively relevant result, would like to

investigate applications further

Diagnosis - general

PatentOptimizer – Claim Elements


A Study28

Looking for infiltration or extravasation

during intravenous infusionLearned – New possibly interesting area, seemingly

dominated by one organisation

Diagnosis – general

A61M – introducing remedies

Study - VantagePoint



Analysis Details:

• Data Statistics

• Terms uniquely appearing in full text

• Highly occurring terms used in small

numbers of documents

• Investigate terms unique to 2013

priority onward

Vantage Point Analysis of TotalPatent full text results• English Translations

Vantage Point - Statistics

Very low percent of terms and words, available for analysis are actually in the title and abstract

Title & Abstract

• 42,614 words & phrases

• 16,251 words

Claims

• ~132k words and phrases not in Title or Abstract

• ~44k words in Title or Abstract

Full-text

• ~1.3M unique words & phrases

• ~650k unique words


A Study30

Vantage Point – Terms only appearing in full text 2013 onwards


A Study31



A Study32

Detection of tetracycline drug –

concern in resistance to antibioticsLearned – New area (clearer language in full-text)

optical investigation



A Study33

Looking for gas hydrates (fracking)Learned – New area (uncovered by more consistent

repetition in full text)

general investigation,

sampling

Analysing Patent Full Text. Conclusions

Findings


A Study35

• Full text useful

• Claims less so (in this case)

Most words and phrases in the “full text”, did not appear in Abstract & Title

• Text mined wasn’t necessarily applications, but pointed towards

• More consistent repetition in full text

Helped mainly find new/niche applications

• Probably wouldn’t have found other ways

Interesting companies & technologies to look at further

Conclusions

Conclusions (Noise and huge amounts of info):

• Background did not really come in as an issue

• Used English translations to avoid language issues

• Most noise was from search results

• My judgement – about 50% proved somewhat

interesting upon further investigation

• Can this be automated/put into a process?

• 4/5+ family groupings seems to be about the

sweet spot


A Study36

What More?

What more?

Further this:

• Life Sciences

• Define processes

Dedicated machine?

• Detailed full-text analysis

Study analysis of parts

• Sellers, inventors, manufacturers etc.


A Study37

Easier than expectedMore possible & better timescales

Questions


A Study38

Analysing Patent Full Text. Study – Additional Examples



A Study40

2 of 6 have tattoo in Abstract OR Title

(same if include claims)Learned – THz radiation can be used for tattoo removal

Diagnosis, surgery - General



A Study41

Not found in Abstract & Title

(One claimed -Optical Diagnostics)

Determining microorganism

presence/kind



A Study42

SAME DOCUMENTS

Identifying/determining antimocrobial

resistance of Burkholderia CepaciaLearned – Smaller more niche areas?



A Study43

Not found in Title, Abstract (or claims) – All Some

detectors, some looking for heavy metal contaminationLearned – Some areas to investigate further?



A Study44

Glucose Monitoring – Far-IR (5/7 have in Abstract & Title)Learned – Not much more than from Title & Abstract

Blood measurement

II-SDV 2014 Analysing Patent Full Text – Comparison against analysis of abstract and bibliographic...

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