Artificial Intelligence – can it
deliver improved patent search?
a personal view.

Magister Ltd, Roche, United Kingdom

Stephen Adams

Managing Director of Magister Ltd.

stephen.adams@magister.co.uk

1

Personal backgroundIn a paper-based search, this modification of myobjectives would result in me having to abandon

In considering whether artificial intelligence

my initial search strategy and my half-finished(AI) could replace the human being in thelist of answers, and start again – a huge waste

conduct of intellectual processes such as aof effort.

patentability search, I have to start by giving
some personal background, since this article

With these first-generation search systems,

is an essentially personal view of the possible

the final quality of the search was still entirely

impact upon my chosen profession.

dependent upon the decisions of the humansearcher.

172

All the important tasks, such as

I completed my Masters degree in Information

selecting the appropriate database, looking up

Science in 1982. Almost all of our researchcontrolled vocabulary or indexing terms, andwork, and our understanding of information

judging relevance of answers, formed part of

retrieval and the use of scientific and technicalthe “art” of literature search, not the “science”.

documents, was paper-based.

Nonetheless,

Information professionals learnt to rely upon

I did complete my first literature search inthese search systems in their everyday work,that year, on the Dialog ® online system. It

to deliver results in response to the humanpresented two clear advantages; it was fast, andsearcher’s skill, confident that the machine

it was interactive. The speed made it possible

was not in any way replacing the human. The

to complete a search across many year’s-worth

technology behind an electronic search was aof the Chemical Abstracts database in one

simple algorithm which carried out essentiallystep, instead of repeating my manual search inthe same work processes as a paper-baseda succession of 5-year or 10-year segments,

search. A searcher who understood their paper-using the cumulative indexes. The degree ofbased search tools could quickly understandinteractivity, such as reading the titles of somewhat was going on “behind the keyboard”, andsearch results, trying different search terms, orto trust the computer as a reliable “co-worker”.

recombining search sets, enabled me to modify

Electronic information systems were therefore

my ideas of “a relevant answer” in mid-search.

seen as little real threat to human jobs, becausethey were entirely reliant upon human skill toresults are unexpected, or irrelevant, thanoperate effectively, just like a dumb robot.

when they appear to be correct. Smaller errors
may be entirely invisible to the inexperienced

Over the last three decades, there have been

searcher.

many developments in electronic literaturesearching. From the point of view of this article,

The second change has been the expansionof different forms of search process.

the data, the other to the process of search.

operations which are carried out on ourThedatabases today are substantially different to
The first change is the development andthe simple duplication of paper-based searchuse of databases which have never existed

steps, which were used by the first generation

in a paper-based form before. My first searchof online systems.

results from online Chemical Abstracts

include the use of built-in taxonomies or

improved in direct proportion to my skill andthesauri, word-stemming, proximity searching,understanding of how to use the corresponding

automatic synonym or cross-lingual term

paper indexes.

The more I used the paper

selection, relevance ranking and more advanced

form of Chemical Abstracts, the better I could

developments such as latent semantic analysis

understand the electronic search fields, howor the replacement of Boolean matching with athey had been created and their significance for

Support Vector Machine. The challenge for theefficient searching. By contrast, if an electronic

information professional in this area is not “Howdatabase can be searched in a way which

well do I understand the database?”, but “Howhas no human analogue, it may be harder forwell do I understand what my search engine

the searcher to conceive of the possibilities –is doing?” (or even “How much do I need toor pitfalls – of that searching technique. For

understand?”).

These new processes

example, any full text database – newspapers,
journals or even patents – offers a search

The outcome of these two changes means

capability which is simply not available tothat the online keyboard can no longer be

the human searcher, because it is physicallytreated as if it was still the same dumb robot

impossible to read every word of every recordof 30 years ago. Some of the intellectual skill

and extract a meaningful result from theof achieving a good search result has alreadycorresponding paper sources. The ability to

been taken away from the human. In the patent

search inside thousands or even millions ofsearch environment, searchers have been used

documents and to locate a single exact word

to taking a high degree of responsibility for thehas given us unimagined possibilities for

quality of their output, and we rightly take aretrieval. However, since the human searcher

professional pride in the significance of what we

has never tried to carry out this process

do. With these changes in search – and now the

manually, they may struggle to develop an

appearance of AI – it can appear that the humanaccurate mental model of what the machine issearcher is being asked to hand over evenactually doing – and more importantly, what canmore responsibility to a ‘black box’ of poorly-

go wrong. Frequently, a searcher learns more

understood technology, which is operating on

about a full text database when their search

an inconceivably complex database structure.

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I would highlight two in particular; one relates toIt is no wonder, then, that some are questioning

record the condition of their digestive system(1).

whether AI systems are worthy of that degree

In these applications, the machine does not inof trust, either now or in the foreseeable future.

any sense ‘replace’ a human worker, since it is

The older generation of searchers may be

carrying out operations which were not possible

inherently more sceptical than the younger

for a human at all.

generation, but they are also more able to probe
beyond the hype.

A slightly more controversial use of machines– at least in the early periods of the Industrial

2

Revolution of the 19th and early 20th century –Technology backgroundis to replace humans in repetitive manual tasks,

In the last few years, it seems as if manysuch as building products on a factory assembly

different industries have begun to be awareline. In these cases, humans are capable ofof the possibility of using AI to improve theirperforming the same tasks, but machines

business processes.

Some authors haveoffer the chance to increase productivity andproposed that AI is capable of penetrating tooften to drive down manufacturing costs. As

all areas of intellectual property work, including

a consequence, these sort of developments

the drafting, proof-reading and prosecution ofhave often resulted in the fear of “Will thisapplications, portfolio management, renewal

machine take my job away?”. Robots can alsodecisions, current awareness etc.

As aproduce a more consistent product, as well as

professional searcher, my concern in this article

more quantity in less time. A welding robot,

is whether AI will be able to perform in the areafor example, can be programmed to repeat theof patent search, including patentability, state-same sequence of welds on a car body timeof-the-art and freedom to operate or clearance

after time, without needing to take a breaksearches.

or losing concentration.

Because of this,robotics has been at the heart of improvements
At first sight, all AI developments may appear

in manufacturing quality in many industries.

to be simply the next logical step in the use ofIndeed, some quality control initiatives, such as

any sort of machine to complete a task. It is

Six Sigma, have at their heart the principle ofalready well known to replace a human being“improving the quality of the output of a processwith a machine in some aspects of life, andby .... removing the causes of defects andthese developments have largely been accepted

minimizing variability...” (2).

as beneficial. A machine may be able to performfunctions which are dangerous or impossible

174

However, AI is rather different to both of these

for a human being, such as activities whichuses of machines.

take place under water, in a confined space orare being made to develop “learning systems”.

exposed to high temperatures or toxic fumes.

These are defined in a recent WIPO report as

For example, it is routine to use a remotely-‘machines that can become better at a task

controlled vehicle to inspect gas pipelines, or atypically performed by humans, and to achievemini-submarine to take underwater pictures. It

that level of performance with limited or nois even known to install miniature cameras into

human intervention’ (3) .

a pill which can be swallowed by a patient, to

has not always understood this distinction.

In the AI field, attempts

The popular press

Newspaper reports have sometimes described

Put simply, if an AI system is to gain widespreada particular computer-based system as

acceptance, it must be able to beat the best

exhibiting “artificial intelligence”, even when itsof what a human can do, and not just once, butfunctionality has been achieved merely by virtue

continually.

of advances in computational power and speed,
allied to a huge base of reference data. True AI

the tasks which they may be able to perform

three questions. These are:
A.Will AI-based patent search systems be

extend far beyond mere high-speed, tirelessrepetition. They hold out the potential of being

AGNOSTIC but AGILE?
B.Will AI-based patent search systems beable to penetrate into areas of intellectual andcreative work, which up to now have been seen

BIASED?
C.Will AI-based patent search systems beas the sole preserve of the human mind. TrueCONSISTENT?

AI – so-called “super-intelligence”, where AI
systems become able to take on any intellectual
task without guidance – has not been achieved

4

Agnostic

yet, and may be a long time coming. As theI am using the term ‘agnostic’ in its

writer of the foreword to the same WIPO report,

more recent sense of ‘not holding a strong

Andrew Ng, concedes(4), “Effective ‘unsupervised

opinion one way or the other’. Why is this anlearning’ – learning without labelled data –important factor for search? It arises out ofremains a holy grail of AI.”the recognition that a search in a literature

3

Irrational fear, or reasonable
doubt?

database is never a standard product.
Searchers are not sitting on a production line,
turning out batches of results which have been

In the course of preparing this article, I have‘manufactured’ using the same tools and lookassumed that it is unlikely that my generationmuch the same each time, with only slightof information professionals will ever have tovariation or customisation. Each patentability

make a binary decision; “Do I use AI in patent

search is unique, designed to fulfil a single

search, or not?”. Like most technologies or work

objective – to establish the absolute novelty

practices, it is likely to penetrate the profession

and/or inventive step underlying each new

gradually, initially working in parallel with existing

patent application. The search stands alone,systems before – possibly – replacing themand the results must not be influenced byaltogether. Therefore, I think it is reasonable forwhat was done before, even if the search istoday’s patent search professional to examine

carried out on behalf of the same applicant

the promises of AI in the light of what thecompany working in the same narrow sector ofhuman searcher is already capable of, whentechnology for many years. This is in complete

performing at their highest level of competence.

contradiction to the development trends of

If AI systems are being designed to replace

new search algorithms and search engines,

the human searcher, we must ensure that their

which actively retain what they ‘learn’ about

performance is at least as good as our current

the searcher’s preferences. The phenomenon

tools in the hands of a skilled human searcher.

of the ‘filter bubble’(5) is now well recognised

YEAR BOOK 2O19 1752

特許情報の高度な活用

systems are more than “robots on a chip”, andI will consider these factors in the form of

– the fact that many common search engineswith the closest term(s) used in other recent

retain information about user behaviour, andsearches; in other words, certain trending

over a period of time will start to give greater

topics are suggested as being the most likely

prominence to results which are similar to whattarget of your search.

the user has accepted in the past. Electronic

user to become lazy, and complete their searchnews feeds begin to produce answers which areusing the same strategy as previous users. If

in line with the user’s known political or social

professional searchers begin to rely on search

viewpoint, and to suppress answers which

systems like this, there is a risk that theypresent a contradictory view. The technology

will lose the skills and confidence necessary

behind ‘cookies’ is now being extended between

to over-ride these suggestions, and simply

online platforms, such that you begin to see

adopt the path of least resistance by choosing

advertisements on one website relating to

the suggested search terms.

goods that you have searched for on anothersearcher is steered away from an ab initio

website.

It is arguable that there may beapproach to the data, and towards a searchhelpful aspects to this for day-to-day searchwhich ‘looks like’ other common searches at theand retrieval, but it is definitely unhelpful in thesame time, in the same information source, orscientific field.

by the same user. To my way of thinking, this isThis encourages the

In this way, aa completely unacceptable feature for a patent
If AI is to be truly helpful for scientific orsearch process; neither the search input nor thepatent search, then the technology behind theretrieval mechanism must be influenced by any

search process must remain entirely neutral‘previous experience’ or ‘retained knowledge’

with respect to how the search is conducted

about what is, or is not, relevant or current

and how the results are sorted and presented(trendy).

to the user. For example, a chemical structurebased search may sometimes yield results for

A third aspect of the influence of machine

molecules which are useful in more than one

learning on effective search is perhaps bestindustry, such as drugs or agrochemicals. If

described by another ‘A’ – instead of asking

the machine ‘learns’ that the user is mostly

systems to be ‘AGNOSTIC’, they must alsointerested in drug applications, there is a risk

be ‘AGILE’. A skilled human searcher knowsthat eventually, any references relating tothat they will often have to adapt their search

agrochemicals may get filtered out. Then, on

strategy as they move between different

the isolated occasion when the same user

inf or m at ion dat abases, or even dif f ere n t

wants to focus on the agrochemical uses, theforms of literature. For example, an efficientalgorithms behind the search machine may

search in a patent database may use a pre-be blocking them from view, or worse still, notdefined classification scheme such as theretrieving them at all.

International Patent Classification (IPC) or
the Japanese FI system. In order to complete176

A second aspect of this lack of ‘search

the same subject search in the non-patentneutrality’ is seen today in a typical Google

literature (NPL), it is usually necessary to

search.

As soon as a user begins to typereplace the IPC or FI classes with some otherin a search term, it is now auto-completed

equivalent search terms (e.g. text or a parallelcontrolled vocabulary scheme).

That is, thein the 2019 WIPO study related to ontology

search strategy has to be adapted, to utilise

engineering or probabilistic reasoning.

the strengths of each individual database or

AI search system which hopes to replace thepublication type. Given that AI systems needhuman searcher will have to learn about nota base set of training material in order toonly today’s scientific publications, but also thelearn how to search, will AI systems have towider context of the technical field. They will

be completely re-trained in order to discoveralso probably have to be re-trained and broughtcorresponding NPL during the same search

up to date (as indeed humans are) as the fieldsession? If this is an inefficient process, orchanges and develops.

is not allowed for in the building of the searchwhich has learnt about past technologies

engine, there is a risk that future searching will

be able to recognise a new invention which

tend to ignore all the valuable meta-data which

breaks the paradigm? This goes to the heart

has been built into some of the major patentof patentability search, and the establishment

and NPL databases, and concentrate solelyof inventive step (non-obviousness) argumentson the minimum common search parameter,

during patent prosecution. There are anecdotal

which is simple text. No matter how ‘intelligent’

stories that when the first patent applications

search algorithms are, any system which isfor the hovercraft were submitted to the UK

reduced to searching solely on this basis will

Patent Office, it took some time before it was

only perform to the same level of efficiency as aagreed which examiner should consider thenaïve beginner at the beginning of their training

case; was it a boat or an aeroplane, since it

as a patent searcher. Such an AI system will beshared characteristics of both? In the end, thea very unsatisfactory replacement for the skilled

first of Sir Christopher Cockerell’s patents

patent searcher.

was classified under the IPC in sub-class B64D

Any

(6)

(for aircraft) but today would be placed in B60V
A further aspect of an AGILE search system(for air-cushion vehicles). The ability to placeis one which is capable of adapting its searcha given patent application in the wider context

strategy in response to the user’s perceptionof science is a crucial step in successful

and feedback of how well the initial results

adaptation of retrieval strategies.

fulfil the search goals. One way of achieving

quality search strategy must be scaleable andthis is by moving up and down the ontology ofadaptable, both to user feedback and to thea given scientific or technical discipline during

age of literature which it is searching, to takethe search. It is somewhat concerning to me

account of different terminology over time andto note that the vast majority of research – as

the state of common general knowledge.

A goodmeasured by the patent applications – in thearea of deep learning is based on the analysisof original text structures. This approach will
result in systems which have only a limited

5

BiasedThe question of bias in AI systems is alreadyunderstanding of how scientific terminology

well known.

Current algorithms depend

develops over time, or how sub-disciplines

upon sets of learning data in order to build awithin a technical field mature and adapt. Less

background of experience, and to deliver futurethan 1% of the AI patent families reported

results. If the learning data contains certain

YEAR BOOK 2O19 177

特許情報の高度な活用

Will an AI system2biases, this can generate a negative feedback

for these other languages were not available,loop, such that the AI inherits a biased viewpoint

and were effectively invisible to the searcher.

on how future data should look. The system

Inexperienced searchers who were unaware

may then suggest actions which tend to confirmof this might attempt a search using English

its predictions. This problem has already been

search terms only. Any records corresponding

encountered in predictive policing systems suchto English-language specifications would be

as PredPol . Similar problems have been foundcertainly located, but also some non-English

in recruiting systems, which used AI to pre-documents by virtue of the existence of an

select candidates for a job interview .

English abstract; this gives the impression

(7)

(8)

that the entire database has been searched
Experts in AI may challenge this viewpoint

to the same depth. The searcher would thenas ‘scaremongering’, which may be partly

close their search session, unaware that theirjustified – in the popular imagination, there

results were significantly incomplete andmay be only a short step between reading

biased. During the analysis phase, a search

about shortcomings of the PredPol system andsystem based on relevance ranking of results

remembering the plotline of the science-fiction

by word frequency would bring the English full-

film “Minority Report”. Once such problems are

text specifications to the top of the list, simply

detected, they can usually be remedied, andbecause of the larger volume of text for thosepast mistakes are not a strong argument forrecords and the higher hit-term frequency withinstopping the development of newer and better

them. As a result, any highly relevant answers

search engines. However, I believe that thesewhich had been located purely on the basis

are real issues which need to be addressed inof the English abstract would be relegated tothe specific context of developing AI for patenta position much lower down in the relevance-

search.

ranked list, and could easily be overlooked.

What are the possible implications

for a patent search if there is bias (consciousor unconscious) built into either the selection

Learning sets may have built-in biases, whichof an appropriate database, or the building

will get reproduced, perpetuated and possiblyof a search strategy which is optimised for aeven enhanced by machines. However, when

specific database?

so-called ‘expert systems’ were being built in
the 1980s, their underlying structure was a178

Human patent searchers already encounter

rules base which attempted to encapsulate asuch challenges to their skills every day,human skill as a series of logic decisions. One

and grow in experience of how to address

criterion for a successful expert system wasthem. For example, when the first databasesthat it could not only arrive at a decision, but

of full text PCT documents were launched,also be able to justify how it arrived at that

only certain languages were available.

Only

point. By contrast, the design of modern AI

documents published in English, French or

systems appears to have as its goal the ability

German were truly available in full text. Later,of a system to function autonomously; that is,

texts published in Spanish, Russian or Asianit will not only fail to deliver such accountability,

languages like Japanese or Chinese began to

but will be unable to deliver it, thus denying thebe added, but initially the character-coded texts

human user any insight into the search process

or any opportunity to correct it.

6

Consistent

There are two principal aspects to

into patent searching relates to the form

consistency of search which I think are ofof information which is found in a complex

concern in the development of AI-based patentdocument such as a patent application.

Itsearch systems. The first is the question of

appears to me that current AI system

whether the same search engine will deliver thedevelopers are working on the assumption

same results to every searcher, irrespective ofthat the entire teaching, or representation,

their location or circumstances. The secondof a new invention in the form of a patentis whether it will be possible to archive theapplication is accessible through analysis

processes of search at a particular point in time,of the text. However, anyone who has donesuch that it is always possible to reconstructpatent searching for any length of time quickly

what was retrieved on a certain date. Both of

discovers that many patent applications

these questions are important whenever there

use text very loosely; they are totally unlike

are challenges concerning the completenessany other word-based document, such as aor competence of a search (for example, in

newspaper, book or even a scientific journal

litigation when one party has to show what they

article. Not only does a patent use a highly

knew on or before the filing date of a contested

specialised form of scientific language, but it ispatent application).

also found that much more information about
the invention is not expressed in words at all,

The first question is a special aspect ofbut embedded in entirely different ‘languages’,

search localisation, and is also relevant in myincluding tabular data, figures and diagrams,

discussions above about search neutrality

chemical and biochemical structures, equations,and bias. We can only argue by analogy with

software code etc. An AI system which can

the popular search systems of today, but if

only search text, and which will give the mostAI search systems follow the same approach,prominence to those records which contain thewe could find that the “same” search engine“most relevant” text (i.e. containing the greatest

is delivering different results sets to different

volume of useful words or word pairs), is highly

users of the same database on the same day.

likely to miss any answers which describe theFor example, would the machine ‘learn’ thatsame invention in non-text language, and toa searcher located in Japan is ‘always more

mis-represent what it does find. Teaching ainterested’ in Japanese-language documents,

machine to “understand text” is only part of theand act in a way which directs their searchsolution towards judging content and thereby

towards a Japan-centric view of the prior art?

understanding relevance. Although there have

Google has already implemented such searchbeen some advances in direct searching of non-localisation, so that a searcher located in the

text data, they are a long way from being able toUnited Kingdom cannot effectively search the(9)contribute significantly to the search process .

German version of Google (www.google.de) even

The sole exception to this is in the chemical

when they wish to concentrate on German-

field, where there is now a variety of tools forlanguage results, without making changes tosearching by chemical structure.

their strategy or browser settings(10). This is

YEAR BOOK 2O19 1792

特許情報の高度な活用

A further aspect of how bias may creepclearly not satisfactory when the user wishes

factor may be highly relevant during patent

to make a global search, as in the case of

litigation, in order to establish what was in thepatentability.

public domain or part of the common generalknowledge. It remains to be seen whether a

The second issue relates more to thestorage and re-use of search strategies.

judge would consider that the use of an AI-

Inbased search system absolves the user of any

many companies, it is standard procedure todegree of responsibility for becoming aware of‘top-up’ an initial patentability search during

prior disclosures.

the period that a patent application is on
file but unpublished (typically between 1218 months).

With current search systems,

7

Is AI the only way forward?

this can be achieved by archiving the exact

I have tried to consider in this article

sequence of search commands used, together

whether there is a reasonable chance of thewith information about the date of searchdevelopment of AI support for the work of patentand the update status of the database(s)searching.

used. Repeating the search to locate any new

on some 30 years of experience in patentmaterial is then simply a matter of runningsearching, and are intended to highlight somethe same strategy and limiting the results toof my expectations for what AI can deliver, but

only those which have been added since thealso some factors which – as far as I know – areprevious date – a procedure which is quite

not well recognised by system developers thus

straightforward in Boolean systems. However,far. However, these technical arguments may

this becomes much more complex in the caseeventually be over-ridden by a more pressingof the present generation of semantic searches,

economic one; namely, “Can we afford NOT to

and is likely to be completely impossible withinvest in AI ? – is it the only hope for the future

more advanced AI systems. This is becauseof patent searching?”.

My arguments above are based

the search algorithms are constantly evolving,180

and cannot be rolled back to the state they

One of the stated motivations for developing

were in at some point in the past. Therefore, re-AI to handle search work is that “the humanrunning the same strategy (even if it could bebeing cannot cope”. This argument is frequently

retrieved and archived from the ‘black box’ onbacked up by statistics on the number ofthe original date) has the potential to retrieve

patent applications being published, or patents

different results from the original time period of

granted, each year around the world, and thethe search, as well as any new material added

likely rise in the numbers of candidate answers

to the database since then. In this environment,

which a typical search will be expected toit becomes effectively impossible to answer the

retrieve. This is indeed a major problem facing

question, “What was searched, and how, on thisanyone doing a patentability search today,

date?”. This means that the original searcher

but the solutions which are being proposed

has lost access to much of the evidence which

commonly focus on automatic screening,they need in order to demonstrate that theirrelevance ranking or classification (clustering or

past search was conducted in a competentso-called ‘faceting’) of a very large answer set,manner, in line with best practice.

rather than the creation of the answer set inThisthe first place – that is, to improve post-search

References

analysis rather than the search process itself.

(1) Reduced size imaging device. US 7662093-

It may turn out that the way forward will be

B2. 16 February 2010.

continued improvement in this aspect, combined(2) Six Sigma. Wikipedia entry, available atwith genuine changes in the process of search,https://en.wikipedia.org/wiki/Six_Sigmausing well-designed AI systems. I prefer at this[Accessed 2019.08.13](3) Artificial Intelligence.

WIPO Technology

IBM, called Augmented Intelligence instead ofTrends 2019. Publication No. 1055E/19.

Artificial Intelligence, will remain the preferred

Geneva: WIPO, 2019., p.39.

route for some time. The human searcher will(4) Ibid, p.8.

still be needed for the critical first stages of a(5) Filter Bubble. Wikipedia entry, available atpatent search, which are the reference interview

https://en.wikipedia.org/wiki/Filter_bubble

with the client in order to draw out the real[Accessed 2019.08.27]

information need through informed discussion,(6) Improvements in or relating to vehicles

subject analysis and iterative trial searches.

for travelling over land and/or water. GB

One of my principal concerns about the entire

854211-A. 16 November 1960.

automation model is that it is based on an(7) D. Ensign et al. Runaway feedback loops

attempt to achieve an optimum answer in a

in predictive policing. 2017.

single step. In my experience, simply throwing

arxiv.org/abs/1706.09847v3 [Accesseda ‘relevant patent’ into a full-text based search2019.08.27]

engine and expecting to get closely related(8) J . D a s t i n .

https://

Amazon scraps secret AI

disclosures may yield some relevant results,

recruiting tool that showed bias againstbut generally falls far short of the quality which

women. Reuters Business News, 10

major corporate applicants expect from theirOctober 2018. https://www.reuters.com/(human) colleagues. Provided the “money vs.

article/ us-amazon-com-jobs-automation-time” arguments do not over-rule the needinsight/amazon-scraps-secret-ai-recruiting-to develop usable systems, the intelligent

tool-that-showed-bias-against-women-human patent searcher will have a job for theidUSKCN1MK08G [Accessed 2019.08.27].

foreseeable future.

(9) S. Vrochidis et al. Concept-based patentimage retrieval. World Patent Information,
34(4), 2012, 292-303.
(10) A. Barysevich. How you can see Google
search results for different locations. 26
February 2019. SearchEngineJournal.com.
Available at www.searchenginejournal.
com/see-google-search-result-differentlocation/294829/#close [Accessed
2019.08.28].

YEAR BOOK 2O19 1812

特許情報の高度な活用

point to think that the approach adopted by人工知能―改善された特許検索を提供できるか？ 個人的な見解（日本語抄録）Stephen Adams

【個人的な背景】

くない状態でそのレベルのパフォーマンスを達成できる

筆者は 1982 年に情報科学の修士号を取得した。私マシン」と定義している。知的および創造的な仕事の分

達の研究活動と理解は紙ベースだった。だから、データ野に侵入することができる可能性を保持している「真のの仕組みと、それらがどのように作られるかが理解できAI」、いわゆる「スーパーインテリジェンス」は、まだた。

達成されておらず、実現には長い時間がかかる。

最近の情報検索の 2 つの利点は、高速とインタラク「効果的な「教師なし学習」- ラベル付きデータなし

ティブである。検索はワンステップで完了するに変わり、で学習することは、AI の聖杯である」と AI 研究の大御検索中に「関連する回答」の検索方針を変更することが所（Andrew Ng 氏）は述べている。

できるように変わった。
最初のデータの変更は、レコードのすべての単語を読【不合理な恐怖、または合理的な疑い？】

むだけである。だから論文から意味のある結果を抽出す

特許検索の専門家は最高の能力を発揮して検索調査す

ることは不可能である。検索結果が予期しない、またはると並行して、AI の可能性を検討するのが合理的と筆無関係である場合には、経験の浅い検索者には、小さな者は思う。

エラーを全く見えなくしている。
2 番目の検索プロセスの変更は、さまざまな形式のAI を検討する際には、３つの質問を投げるのが良い。
A. AI ベースの特許検索システムは偏見なき中立者で、検索プロセスの拡張である。新しいプロセスは、シソー

かつ、機智者か？

ラスの使用、単語ステミング、近接検索、自動同義語まB. AI ベースの特許検索システムにはバイアスが掛かる

たは言語間の用語選択、関連性ランキング、潜在セマン

か？

ティック分析への置き換えなどを含み、ベクター（行列C. AI ベースの特許検索システムには一貫性があるか？

計算）マシンに変わった。
データの変更とプロセスの拡張性という変更の結果【偏見なき中立者】

は、知的スキルの一部を人間から奪い、技術の「ブラッ筆者は、現状の AI は「偏見なき中立者」であるべきクボックス」の責任を情報プロフェッショナルに負わせと思う。決して AI は標準的な製品ではないと認識する。

ている。私達は、AI システムが信頼に値するかどうか

特許検索は、絶対的な新規性および / または進歩性を疑問視し、よく調査すべきである。

確立するためにユニークな業務であり、毎回同じように見えるが、決してそうではない。本来、結果は以前に行【技術の背景】
一見、AI 開発は、あらゆる種類のマシンを使用してしかしながら、AI は検索者の好みについて「学習」

タスクを完了する２つの論理的なステップにすぎないよ

した内容を積極的に保持するから良くない。科学分野（特うに見える。①人間にとってまったく不可能な操作を実許分野）ではそのやり方は間違いなく役に立たない。

行している場合（人間に置換できない場合）と、②人間

この「検索中立性」の欠如は、他の最近の検索で使用と同じタスクを実行できる場合である。後者は、「このされた最も近い用語で自動補完され、特定のトレンドト

マシンが私の仕事を奪ってしまうのではないか？」といピックが検索の最も可能性の高いターゲットとして提案

う恐怖をもたらした。しかし、現状の AI は、これらのされてしまう。他の一般的な検索と「似ている」検索にマシンの使用の両方とはかなり異なる。

向かって誘導されてしまう。これは特許検索プロセスに

WIPO レポートでは、AI は「人間が通常実行するタ
スクでより良くなり、人間の介入が制限されているか全

182

われた特許検索に影響されてはいけない。

とって全く受け入れられない機能である。
熟練した人間の検索者は、異なる情報データベース間、

または異なる形式の文献間を移動するときに、検索戦略

2 つ目は、特定の時点で検索されたものを常に再構築を適応させる。特許情報を検索する際には、AI システ

できるように、特定の時点での検索プロセスをアーカイ

ムを完全に再トレーニングする必要がある。そうでなけ

ブで再現できないという問題です。

れば、初心者の初心者と同じレベルの効率でのみ検索実行がされてしまう。

例えば Google は検索ローカリゼーションを実装していて、AI 検索システムが同じ検索エンジンが同じデー
タベースの異なるユーザーに同じ日に異なる結果セット

非常に不十分な代替品である。AGILE 検索システムにを配信している。例えば、日本の検索者は、日本語の文

おいては、質の高い検索戦略が履行できるように設計す

献は検索できるが英語の文献を AI は検索に行かない。

べきである。①ユーザーの認識と②フィードバックに応

2 番目の問題は、検索戦略の保存と再利用に関連する。

じて、検索戦略を適応させることができるものを開発す

検索アルゴリズムが常に進化しており、過去のある時点べきである。

での状態にロールバックできない。この環境下では、元の検索者が過去の検索がベストプラクティスに沿って適【バイアス】
現在のアルゴリズムは、一般経験の背景を構築し、将来の結果を提供するために、学習データのセットに依存

切な方法で行われたことを示すために必要な多くの証拠にアクセスできなくなった。特許訴訟に関連して重要な課題である。

し過ぎている。学習データに特定のバイアスが含まれて、偏った視点を継承してしまう。不完全な予測システムを実装しているから問題である。
特許検索用の AI を開発するという特定のコンテキス
トで対処する必要がある。
現状の AI は、テキストは読むが、非テキスト言語は読めなく無視してしまう。単語の頻度による結果で、ラ【AI は前進する唯一の方法ですか？】
AI を開発する動機の 1 つは、特許出願の数、特許の数に関する統計、候補回答の数の増加に対して「人間は対処できない」という理由からです。この時点で、人工知能の代わりに拡張知能と呼ばれるアプローチを優先ルートとして筆者は好む。

ンキングに基づいてリストの先頭に表示されたりする。

特許調査の重要な第 1 段階では、人間のサーチャー

バイアスは、マシンによって再現、永続化、さらには強が必要で、インフォームドディスカッション、主題分析、化される。

反復試行調査を通じて実際の情報ニーズを引き出すべき最新の AI システムの設計は、システムが自律的に機だ。

能する能力を目標としている。その結果、人間のユーザー

筆者の主な懸念の 1 つは、「現在開発中の AI の品質は、が検索プロセスに対する洞察やそれを修正する機会を否

人間・同僚に期待する品質にはほど遠い」ことである。

定する。

知的な人間の特許検索者は予見可能な未来に向けて仕事

発明に関するより多くの情報が非テキストで表現されを行い、AI に代わることはない。

て、表形式のデータ、図、図表を含む全く異なる「言語」に埋め込まれていることもわかっているが、AI はそれを考慮していない。唯一の例外は化学構造による検索で
ある。

【一貫性】
AI ベースの特許検索システムの開発では、検索の一貫性で 2 つ問題がある。
1 つ目は、同じ検索エンジンが、場所や状況に関係なく、すべての検索者に同じ結果を提供できないという検索のローカリゼーション問題である。

YEAR BOOK 2O19 1832

特許情報の高度な活用

現状の AI システムは、熟練した特許調査員にとって