=Paper=
{{Paper
|id=None
|storemode=property
|title=First Approaches on Knowledge Representation of Elementary (Patent) Pragmatics
|pdfUrl=https://ceur-ws.org/Vol-1004/paper3.pdf
|volume=Vol-1004
|dblpUrl=https://dblp.org/rec/conf/ruleml/Ramakrishna13
}}
==First Approaches on Knowledge Representation of Elementary (Patent) Pragmatics==
https://ceur-ws.org/Vol-1004/paper3.pdf
First Approaches on Knowledge Representation
of Elementary (Patent) Pragmatics
Shashishekar Ramakrishna1,2
1
Freie Universität Berlin, Königin-Luise-Str. 24-26, 14195 Berlin, Germany,
2
Teles PRI GmbH, Ernst-Reuter-Platz 8, 10587 Berlin, Germany,
s.ramakrishna@teles.de
Abstract. The focus of this article is to provide first approachs to a
possible key solution representation and construction of legal norms, es-
pecially the national patent law norms. A semantic-system based on these
approaches, complementary to the FSTP/IES-Expert system, would aim
at (semi)-automatically translating the parts of the notion legal certainty
from its natural language non procedural presentation to a declarative
logical presentation by formal modeling through interpreting the prag-
matics facts based within a National Legal Systems. This paper covers
the initial abstract solutions and possible outcomes as gathered during
the first year of PhD research3 .
Keywords: Facts Screening and Transformation Processor (FSTP), In-
novation Test, 35 U.S.C (§§ 112, 102/103, and 101)
1 Motivation
Current emerging technologies are mostly ’Model’ based inventions i.e. intangible
subject matter based. In general, an innovation claimed through its patent ap-
plication, can be seen as a pair , wherein the
specification (including drawings) forms the second part of the pair. An inventive
property/statement of an invention, disaggregated on levels of abstraction or on
grains of mental resolution into elementarily properties henceforth referred as bi-
nary inventive concept. It provides the required degrees of separation of concerns
for evaluating such properties independently in the light of its subject matter.
Next to trivial elementary inventive concepts are logically error resistant as they
represent a single/separated concern. The same holds for a non-inventive concept
of a claimed inventions element, describing one of its non-inventive properties.
(Semi)-/Automatic evaluation by means of applying elementary pragmat-
ics, ’EP’ and National Patent Laws on such binary (non-)inventive concepts re-
quires a semanticsystem for reasoning against the considered concepts, capable of
the acquisition and processing of enormous amounts of background knowledge
in a machine understandable format, keeping in mind its interdependence to
3
This Ph.D thesis is being supervised by Prof. Adrian Paschke, Freie Universität
Berlin, Germany.
each other. Such a (sub)-system working in conjunction with the existing Facts
Screening and Transformation Processor, FSTP [1]/Innovation Expert System,
IES [2], enables a person of pertinent skill, who is needed for recognizing non-
elementary pragmatics, to recognize automatically and/or guided interactively
by the FSTP/-IES to consider whether such elementary properties of an inno-
vation at issue (after its disaggregation) can be considered as Anticipate (A),
Not-Anticipate (N) to its prior arts/considered reference set (RS).
2 Background - The Fact Screening and Transformation
As described in [1], [2] an innovation/creation over existing knowledge, provided
as a reference set RS of prior art documents, is representable by a technique
teaching, TT.0 which goes beyond the knowledge of the RS - just as in a paten-
t/application. This compound of knowledge, representing an innovation, is called
“PTR”, standing for a “pair of TT.0 and RS”.
The Innovation Expert System (IES) thus is the PTR Expert System, defined
by the epistemological and practical requirements it meets: For any PTR to
which it is applied, it is supporting its user in
1. deriving from it all technical and legal facts alias relations between TT.0 and
a given RS respectively a given context, such as a given legal system (in the
U.S e.g. to 35 U.S.C §§ 112, 102/103, and 101) and then
2. leveraging on this analysis instantly recognizing and answering any reason-
able query for any such relation
The PTR Expert System (ES) is built around the PTRs “FSTP Test” [3],
hence is also called FSTP ES. The FSTP Test of a PTR supports structuring
of its PTR. This PTR-DS is disaggregated into three levels of knowledge rep-
resentations (KR), “o/AD/BID”-KR. Wherein, o refers to “original”, AD to
“Aggregated ∧ Disclosed”, and BID to “Binary ∧ Independent ∧ Disclosed”.
IES supports, initially screening its documents/technical teachings for elemen-
tary building blocks of its creativity/inventivity, i.e. for its inventive and non-
inventive concepts. Technical informal inventive and non inventive concepts/
properties are then transformed into technical formal inventive concepts/facts,
then transforming those into the technical primary facts, and finally transform-
ing them into the technical secondary facts, called basic resp. semantic (alias
creative) resp. textbfpragmatic (alias innovative) facts. These technical sec-
ondary facts use metrics induced by the Highest Courts precedents on creativi-
ty/innovation by their numbers of BID-inventive concepts embodied by TT.0.
From these BID-inventive concepts, the classical yes/no answer to the question,
whether TT.0 is indicated obvious over RS, can be derived by this metric. The
semantic/creative and pragmatic/innovative facts extend this metric much fur-
ther by first defining a PTR plcs specific (plcs = patent law carrying semantic)
innovation geometry, which depicts the plcs-height/-creativity of its TT.0 over its
RS. Based on plcs-height/-creativity, TT.0s pragmat-ic/innovative height over
RS additionally takes into account the PTRs pmgp (pmgp= patent monopoly
granting pragmatics) in any National Patent System (NPS) which represents the
national/socio/economic principles underlying the idea of rewarding an innova-
tion by granting a 20 years monopoly to its TT.0.
3 Goals/Aim
The object of our concern in this thesis is to create a semantic-system, capable of
(semi-) automatically translating the parts of the notion “legal certainty” such
as patent laws (e.g. in U.S, 35 U.S.C §§ 112, 102/103, and 101) from its natural
language non procedural presentation to a declarative logical presentation by
formal modeling through interpreting pmgp based on NLS/ (NNI = National
Normative judicial Interpretation of facts).
Figure 1, shows few (10+) basic tests as proposed in [4] enabled by its inven-
tive concepts, automatically prompting their user through exploratively checking
its meeting the requirements as stated by few NPS’es (e.g.: 35 USC 112, 102/103,
and 101). Applying these tests to inventive concepts requires the requirements
of the NPS’es to be modeled into declarative rules, due to their modular feature
and their capability to use the same knowledge in many different ways. Modeled
rules are used in deductive (non-monotonic) reasoning for legal interpretations.
NPS’es, like complex computer systems, constantly face questions that aim to
ascertain the state of things or the correctness of a certain contention, like these
modeled rules/tests. Hence, the legal questions regarding which of a number of
modeled/competing legal rules could apply in a given situation amount to some
error and inconsistencies, thereby leading to inconsistent reasoned output/le-
gal interpretations. One such non-trivial approach would be that such modeled
rulebases are updated manually/guided by the system using inductive learning
techniques (applying rules on case laws). Such an approach would be a long term
goal and is not considered for the current use-cases shown in this thesis. The list
below re- expresses the intended aim, providing possible approaches/solutions
to the research question stated in Section 4 of this thesis in detail:
1. To manually(and/guided-by-system) analyze and extract the rules and on-
tological concepts described in the natural language descriptions of NPS’es.
2. To identify the required semantics and inference rules needed for legal rea-
soning with NPS’es and for the legal interpretation enabling the separating
of novel innovations from obvious steps.
3. Logic-based declarative representation of these chains of complex rules for
legal reasoning on top of structured formal ontology domains representing
the conceptualization of the NPS’es and the underlying domains of skill and
elementary pragmatics.
4. Developing a legal reasoning sub-system to the FSTP ES which allows pmgp
dependent information to be derived from the NPS knowledge bases and to
be used in the FSTP for semi-automated legal decision support and compli-
ance checks with the applicable NPS for a PTR. This includes
(a) Address the trade-off between required expressiveness of the knowledge
representation and its computational complexity of the legal reasoning
in FSTP.
(b) Provide support for the different roles involved, such as inventor, per-
son of pertinent skill, examiner, patent agent etc. This requires different
representation languages from natural language format for expressing
questions, answers, proofs and explanations to platform-independent se-
rializations in XML and Semantic Web formats to platform-specific ex-
ecutable formats on the logical reasoning layer.
(c) Provide support for life cycle management of knowledge. This addresses
e.g., collaborative knowledge engineering and management (versioning,
different roles such as author, maintenance), updates in the NPSes by
new decisions which lead to corresponding isomorphic updates in the
NPSes knowledge bases, integration of internal and external (semantic)
background knowledge e.g. about skill, elementary pragmatics, usage
data (annotations, proofs, etc.).
Fig. 1. 10+ In-C tests to be applied on an inventive concept for patent eligibility, in
accordance to US patent law
4 Research Questions
The research question will be refined and detailed after the literature review
and baseline study, from the following general problem domains of a knowledge
representation.
1. Syntax:
(a) Which representation and interchange format for the representation of
the knowledge on different representation layers? (human-oriented com-
putational independent, platform-independent supporting integration and
interchange, platform-specific logical reasoning).
2. Semantics:
(a) Which inference and interpretation semantics (non-monotonic vs. mono-
tonic, expressiveness vs. computational complexity, closed-world vs. open
world, “ontologies vs./and rules”, )
3. Association problem:
(a) How to connect the formal representation with the real-world resources
and norms?
Requirements derived from these knowledge representation problem domains
can be distinguished according to functional requirements for the concrete knowl-
edge representation and non-functional requirements during design time (devel-
opment / engineering of the knowledge) and run time (use of the knowledge).
1. Functional Requirements:
(a) e.g., expressiveness, ...
2. Non functional requirements at design time:
(a) e.g., composability and extensibility, interoperability, declarative imple-
mentability, modifiability and evolvability, reusability and interchange-
ability,...
3. Non functional properties at runtime:
(a) e.g., usability, understandability and explanation, correctness and qual-
ity, scalability and efficiency, safety and information hiding (need-to-
know principle),...
5 Proposed Approach
An abstract model of the system envisioned as a solution to the problem can
be seen in Figure 2. An existing state-of-the-art prior art search module, using
a semantic search engines like, Cognition [5], DeepDyve, etc retrieves patents
through large databases which forms the required RS (if previously not specified
by the jury) for the TT.0. Thus formed PTR-DS will be transformed from their
natural language texts into some standard representation formats like XML,
using text-mining, semantic recognition and annotation techniques supporting
human knowledge engineers in the fact screening and transformation process.
Similar to the PTR-DS, the existing patent rules from NPS have to be trans-
formed from their natural language format to more standardized rule represen-
tation formats. We propose to use LegalRuleML [6], an XML standard for legal
knowledge representation based on RuleML [7] which supports the modeling of
norms.
Parallely, we map patent norms as used in landmark case law decisions to
a workflow using some configurable workflow model. Where, each node on the
Fig. 2. Cognitive system (abstract model )
workflow (B-tree) represents a complex legal rule represented using the Reac-
tionRuleML [8] representation format. This resolves complex legal questions and
automates the analysis of a large number of patent norms with respect to their
logical coherence in a given NPS. The workflow itself is represented using Legal-
RuleML, which provides the functionalities likes reusability, lifecycle manage-
ment of nodes or the entire workflow to capture the changes over time of the
rules when the legal binding text changes.
LegalRuleML is also be used to point out logical inconsistencies in current
case law decisions and can also be used to evaluate the compliance of semantic
facts with case law and positive law. Thereby, providing a powerful and declar-
ative way to control and reuse such semantically linked meanings with the help
of independent micro-ontologies about the NPSs and domain specific pragmatic
contexts (skill ontologies, elementary pragmatics, standards etc.) for flexible pro-
cessing and legal reasoning. The required (patent) rules/constraints are built by
the rule creator module, which uses a distributed rule inference services network
like Prova [9], a java based open source rule language for reactive agents and
event processing.
Figure 3 shows the process of generating a generic workflow pertaining legal
rule from landmark decisions’ specific workflow. This allows capturing the differ-
ent interpretations of the same law on different use cases and, thereby, arriving
at a generalized workflow.
6 Elementary Pragmatics
Elementary Pragmatics are disclosures (explicit/implicit) of certain art which
can be easily understood by a person of pertinent skill. According to certain
National Patent Systems, an EP must not be just claimed to exist, but must be
documented in an enabling way.
Fig. 3. specific workflow to generic workflow
EP can be divided into 4 types as shown in Figure 4 :
1. EP from Formal Rules for Filing, EP-PFP
2. EP from Patentability Conditions, EP-P
3. EP from Post Grant Procedures, EP-PGP
4. EP from Litigation, EP-L
Fig. 4. Classification of EP in a National Patent System.
We narrow down our focus on Elementary Pragmatics from Patentability
Conditions, EP-P. Specifically on four paragraphs (35 USC §§ 112, 102/103, and
101) of the U.S patent system [10]. Figure 5 shows the general evaluation pro-
cedure for an inventive concept under patentability. A set of inventive concepts
is patent eligible, ’pe’ if and only if it satisfies all the patentability criterias or
EP- P’s.
Fig. 5. Evaluation procedure.
7 Proposed Framework
Fig. 6. Proposed Framework.
We propose a legal information system framework [11] as shown in Figure 6. The
proposed framework is based on a general information system research frame-
work [12]. The central ’Research’ module is fed with information from both
’Environment’ and ’Knowledge Base (KB)’ modules. Information/raw material
such as FSTP facts, which include the norms from various NPS’es, are fed by the
’Environment’ module and the syntax, semantics, pragmatics and instantiations
encompassing a norm are fed by the ’Knowledge Base’ module. The central re-
search module works towards building the inference rules required for the legal
reasoner. The ’develop/build’ sub-module including legal reasoner is evaluated
for the norms expressiveness, extensibility and interoperability criteria’s. Based
on the results, the rules and the reasoner are refined again. This iterative process
of (re-)assessing and refining is completed when all criteria are effectively evalu-
ated. Processed information is fed back to the environment module for its actual
usage within the FSTP ES. Additional information for the lifecycle management
of a norm and its contexts is sent back to KB module.
7.1 Methodologies
Elaborating on the process of mapping patent norms to workflow models we start
with the representation of all landmark case-law decisions concerning a specific
norm onto a workflow. Wherein, LegalRuleML is used for such representation.
We reiterate the formal template of LegalRuleML showing the most general
representation syntax as defined in [13].
/< lrml:LegalRuleML >
< l r m l : L e g a l S o u r c e > ...
< l r m l : T i m e I n s t a n t s > ...
< l r m l : T e m p o r a l C h a r a c t e r i s t i c s > ...
< lrml:Agents > ...
< l r m l : A u t h o r i t i e s > ...
< lrml:Context >
< lrml:appliesRole >
< lrml:appliesStrength >
< l r m l : a p p l i e s A u t h o r i t y > ...
< l r m l : a p p l i e s J u r i s d i c t i o n > ..
< l rm l: St a te me nt s >
< Rule >
..
..
Listing 1.1. General LegalRuleML syntax
Each node on the workflow represents a disaggregated rule/norm. Such rule
or set of rules are embedded inside < lrml: Statements > using Reaction-
RuleML. Listing 1.2 shows a formal template of ReactionRuleML, showing the
most general representation syntax as defined in [8].
/ < Rule >
< meta > ...
< scope > ...
< evaluation > ...
< signature > ...
< qualification > ...
< quantif ication > ...
< on > ...
< if > ...
< then > ...
< do > ...
< after > ...
< else > ...
< elsedo > ...
Listing 1.2. General ReactionRuleML syntax
7.2 Develop/Build (Legal Reasoner)
Disaggregated patent rules are wrapped to form a reactive agent as shown in
Figure 7. A generic module “semantic- interface” here is used to depict the need
for hybrid reasoning due to the fuzzy nature of patent rules. ReactionRuleML
messaging is used for distributed reasoning. Listing 1.3 shows general message
syntax.
Fig. 7. Reactive agent with hybrid reasoning.
/ < Message directive = " PRAGMATIC CONTEXT " >
< oid >
< protocol >
< sender >
< receiver >
< content >
Listing 1.3. ReactionRuleML Messaging syntax
8 Examples
8.1 35 U.S.C § 112 6th paragraph
Consider the latest Court of Appeals of Federal Circuit (CAFC) decision on
§ 112 6th paragraph in Lighting Ballast Control LLC v. Philips Electronics
and Universal Lighting Technologies, Inc’ [14]. Under this decision the court re-
explained the norms within the 6th paragraph of § 112 (35. U.S.C Patent law).
For our analysis, we map the decision and its citations into workflow. Figure 8[a]
shows some excerpts from the decision itself. Figure 8[b] shows the workflow
mapped from the decision for the analysis of “Means-Plus-Functions-Claiming”
In its decision, the CAFC with the help of citations explains how other factors
influencing this decision have to be handled.
Fig. 8. CAFC decision ’Lighting Ballast v Philips Electron’ [14] mapped to a workflow.
We propose to use inductive approaches by populating the workflow with
related decisions (’Biomedino LLC v. Water Techs Corp’ [15], ’MIT v. Abacus
Software’ [16], ’Greenberg v. Ethicon Endo Surgery, Inc’ [17] etc) to obtain a
generic workflow for 6th paragraph of § 112 as shown in Figure 9. Where, the
norm in workflow format is represented using LegalruleML representation format
described before and the nodes/rules are represented using ReactionRuleML. For
this example we use Stanford parser, SentiwordNet, Prova and Pre defined legal
lexicons, PUBPAT as semantic interface for reasoning the norms. A person of
pertinent ordinary skill and creativity (posc) as defined by USSC affirms every
reasoned result.
Fig. 9. 6th paragraph of § 112 (35. U.S.C Patent law)..
9 Conclusion and Future Steps
The solution to have a sub-system, based on configurable EP which connects
the FSTP ES, thus making it full/-semi automatized in handling queries per-
taining to EP and NLS thereby, providing a uniform platform for standardizing
the generation and representation of complex rules (built using fewer NPS goal
clauses/(patent) rules. Such a system would serve as a ready reckoner in draw-
ing legal conclusions on top of scientific fact determined during FSTP analysis.
This would then help in applying the (elementary) cognitive norms required for
interpretation and evaluation of such identified facts.
10 Acknowledgements
The author would like to thank Prof. Adrian Paschke and Prof. Sigram Schindler
for their constructive comments and suggestions. This work has been partially
supported by the Fact Screening and Transformation Project (FSTP) funded by
the TelesPRI GmbH: www.fstp-expert-system.com.
References
1. Schindler S.: THE FSTP EXPERT SYSTEM, WO 2012/022612 A1 (to Sigram
Schindler Beteiligungsgesellschaft mbh [DE/DE]; Ernst-Reuter-Platz 8 10587
Berlin (DE)), 23 February 2012.
2. Schindler S.: INNOVATION EXPERT SYSTEM, IES, AND ITS PTR DATA
STRUCTURE, PTR-DS (to Sigram Schindler Beteiligungsgesellschaft mbh
[DE/DE]; Ernst-Reuter-Platz 8 10587 Berlin (DE)), May 2013.
3. Ramakrishna S., Karam N., Paschke A.: The FSTP Test: a novel approach for an
invention’s non-obviousness analysis, JURIX 2012, pp 129-132, 2012.
4. Brief of Amicus Curiae Sigram Schindler Beteiligungsgesellschaft mbH in support
of neither party, LIGHTING BALLAST v. PHILIPS ELECTRON (2008), No.
2012-1014 (USSC, pending), 28 May 2013.
5. Dahlgren K.: Technical Overview of Cognition s Semantic NLP (as Applied to
Search ), ReCALL, pp. 120, 2007.
6. Palmirani M., Governatori G., Rotolo A., Tabet S., Boley H., and Paschke
A.:LegalRuleML: XML-Based Rules and Norms, in Rule - Based Modeling and
Computing on the Semantic Web, vol. 7018, Springer Berlin Heidelberg, 2011, pp.
298-312.
7. Boley H., Paschke A., and Shafiq O.:RuleML 1.0: The Overarching Specification of
Web Rules, in RuleML, 2010, pp. 162-178.
8. Paschke A., Boley H., Zhao Z., Teymourian K., and Athan T.:Reaction RuleML
1.0: Standardized Semantic Reaction Rules, in RuleML 2012, 2012.
9. Kozlenkov A.:Prova Rule Language Version 3.0 User’s Guide, Internet:
http://prova. ws/index. html, 2010.
10. Title 35 of the United States Code. 1952.
11. Ramakrishna S.: Cognitive System for Knowledge Representation of Elementary
Pragmatics, in Proceedings of the RuleML2012 Challenge, at the 6th International
Symposium on Rules, 2012, online ceur-ws.org/Vol-874/paper2.pdf.
12. Hevner A R., March S T., Park J., and Ram S.:Design science in information
systems research, MIS Quarterly, 2004.
13. Athan T., Boley H., Governatori G., Palmirani M., Paschke A., and Wyner
A.:OASIS LegalRuleML, in Proceedings of 14th International Conference on Arti-
ficial Intelligence and Law (ICAIL 2013), 2013.
14. Lighting Ballast Control LLC v. Philips Electronics and Universal Lighting Tech-
nologies, Inc No. 2012-1014 (Fed. Cir. January 2, 2013).
15. Biomedino, LLC v. Waters Techs. Corp., 490 F.3d 946, 949 (Fed. Cir. 2007).
16. MIT v. Abacus Software, 462 F.3d 1344, 1353 (Fed. Cir. 2006).
17. Greenberg v. Ethicon Endo-Surgery, Inc., 91 F.3d 1580, 1583 (Fed. Cir. 1996).