Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
This Office Action is responsive to Applicants' Amendment filed on February 2, 2026, in which claims 1, 8, 15, 22, and 24 are currently amended. Claims 1-2, 4-9, 11-16, and 18-25 are currently pending.
Response to Arguments
Applicant’s arguments with respect to rejection of claims 1-2, 4-9, 11-16, and 18-25 under 35 U.S.C. 101 based on amendment have been considered, however, are not persuasive.
With respect to Applicant’s arguments on p. 12 of the Remarks submitted 2/2/2026 that “the pending claims do not recite mental processes because the limitations of the pending claims cannot be practically performed in the human mind”, Examiner respectfully disagrees. While Examiner agrees that the limitations “providing a network connection to a rules generator to allow the rules generator to access the knowledge graph” and “providing a network connection to the intelligent application to allow the intelligent application to access the knowledge graph” cannot be performed in the mind, these limitations are seen as mere instructions to apply the judicial exception to a practical application. The claims as a whole are explicitly directed towards deriving existential logic rules, and as shown in the Non-Final Office Action mailed 11/10/2025 and as restated below the logic reasoning can readily be performed in the mind. The additional elements of performing the logic using generic computer memory and network connection amounts to mere instructions to apply the judicial exception using a generic computer and do not integrate the judicial exception into a practical application (MPEP 2106.07(a)(II) "employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application"). For at least these reasons and those further detailed below Examiner asserts that it is reasonable and appropriate to maintain the rejection under 35 U.S.C. 101.
Applicant’s arguments with respect to rejection of claims 1-2, 4-9, 11-16, and 18-25 under 35 U.S.C. 103 based on amendment have been considered, however, are not persuasive.
With respect to Applicant's arguments on pp. 13-14 that Sowa does not disclose the sequence recited in claim 1, Examiner respectfully disagrees. Sowa discloses the same ordered inference loop in conceptual-graph terms: (i) graph joining/unification ([p. 41] "If m=1 (a Horn clause), a copy of each graph ui is joined to some graph in C by a covering join. Then the assertion v is added to the resulting collection C'."), (ii) constraint checking , blocking, and undoing inconsistent jobs ([p. 41] "If m,=() (a denial or the empty clause), the collection C is said to be blocked." [p. 43] "With an appropriate canon, many undesirable graphs are ruled out as noncanonical, but the canonical graphs are not necessarily true. T~) ensure that only true graphs are derived from true graphs, the laws discussed in Section 4 eliminate inconsistent combinations"), and (iii) adding the resulting assertion to the context ([p. 41] "Let C be a collection of canonical graphs, and let s be the sequent ul ..... u, -,- v~ ..... v,,,. If the conditions of s are satisfied by C, then s may be applied to C"). Examiner also notes that even if for the sake of argument the specific ordering of steps are different (which Examiner does not concede) MPEP 2144.04(IV)(C) explicitly states ("In reBurhans, 154 F.2d 690, 69 USPQ 330 (CCPA 1946) (selection of any order of performing process steps is prima facie obvious in the absence of new or unexpected results)"). Here, the supposed distinction is just the explicit ordering of known conceptual-graph operations (join, consistency check, and add the conclusion) without any identified new result from that ordering.
With respect to Applicant's arguments on p. 14 of the Remarks submitted 2/2/2026 that "in Sowa, at least two of the processes cited by the Office Action are conditioned on the value of variable m, which indicates that these processes are conditional, and may not even be performed in some cases". Examiner notes that just because a step is conditional does not mean that the step is not part of the process or that the sequence is not taught. Sowa explicitly performs joins, checks constraints, may reject or backtrack, and then adds conclusions. The fact that a constraint may fail or a branch might not execute does not change the fact that the pipeline itself is defined.
Applicant's arguments on pp. 15-16 of the Remarks submitted 2/2/2026 restate the previous arguments directed towards Sowa.
Claim Rejections - 35 USC § 101
101 Rejection
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-9, 11-16, and 18-25 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 1: Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 1 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass rules based inference, including the following:
representing […] a knowledge graph that supports an intelligent application using a geometric embedding (observation, evaluation, and judgement)
constructing, by the rules generator, a set of premises defining syllogistic forms for classes of the knowledge graph, wherein premises included in the set of premises identified as deriving useful existential rules are selected to avoid evaluating every possible syllogism logic representation in the knowledge graph (observation, evaluation, and judgement)
transforming, by the rules generator, the geometric embedding to syllogism logic representations using the set of premises (observation, evaluation, and judgement)
deriving, by the rules generator, one or more existential rules using standard transformation rules present in the syllogism logic representations, wherein deriving an existential rule of the one or more existential rules includes performing a sequence of: (1) unifying […] respective syllogism logic representations into a unified diagram, (2) resolving one or more conflicts identified in the unified diagram to form […] a resolved unified diagram, and (3) transforming […] the resolved unified diagram into a conclusion diagram that graphically represents a conclusion of the existential rule (observation, evaluation, and judgement)
adding, by the rules generator, the one or more existential rules to the knowledge graph to increase an amount of knowledge for use by the intelligent application (observation, evaluation, and judgement)
Therefore, claim 1 recite an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 1 recites additional elements “in computer memory”, “providing a network connection to a rules generator to allow the rules generator to access the knowledge graph”, and “providing a network connection to the intelligent application to allow the intelligent application to access the knowledge graph”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Therefore, claim 1 is directed to a judicial exception.
Step 2B Analysis: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 1 amount to no more than mere instructions to apply the judicial exception using a generic computer component.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. The rejection applies equally to dependent claims 2 and 4-7 which depend on claim 1.
This rejection applies equally to independent claim 8 which is directed towards a computer program product for performing the method of claim 1. Specifically, claim 8 recites “one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising” which amounts to mere instructions to apply the judicial exception using generic computer components. This rejection applies equally to dependent claims 9 and 11-14 that depend on claim 8.
The rejection applies equally to claim 15, which recites a system comprising “one or more computer processors, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising” which amounts to mere instructions to apply the judicial exception using generic computer components. This rejection applies equally to depend claims 16 and 18-21 which depend on claim 15.
The additional limitations of the dependent claims are addressed briefly below:
Dependent claim 2, 9, and 16 recite additional observation, evaluation, and judgement “the syllogism logic representation comprises a selection from the group consisting of: a Venn diagram with shading and x-sequences, and a Carroll's diagram” (Examiner notes that Euler diagrams existed well before mechanical computers such that it would be very reasonable to interpret Venn diagram creation as something that can practically be performed entirely in the mind with or without the assistance of tools such as pen and paper)
Dependent claims 3, 10, and 17 recite additional observation, evaluation, and judgement “constructing a set of premises defining syllogistic forms for classes of the knowledge graph.”
Dependent claims 4, 11, and 18 recite additional observation, evaluation, and judgement “transforming the geometric embedding to the syllogism logic representations comprises applying the set of premises to the geometric embedding.”
Dependent claims 5, 12, and 19 recite additional observation, evaluation, and judgement “deriving the existential rules comprises applying a selection from the group consisting of unification rules, resolution rules, and transformation rules”.
Dependent claims 6, 13, and 20 recite additional observation, evaluation, and judgement “the knowledge graph comprises classes”.
Dependent claims 7, 14, and 21 recite additional observation, evaluation, and judgement “the geometric embedding comprises concept hierarchies”
Regarding Claim 22: Claim 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 22 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 22 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass rules based inference, including the following:
Dividing, […], a knowledge graph that supports an intelligent application into classes (observation, evaluation, and judgement),
Organizing, by the rules generator, geometric embeddings for entities in the knowledge graph (observation, evaluation, and judgement)
constructing, by the rules generator, premises, wherein the premises comprise syllogistic relationships for a pair of classes and wherein the premises are selected based on being identified as deriving useful existential rules so as to avoid evaluating every possible syllogism logic representation in the knowledge graph (observation, evaluation, and judgement)
transforming, by the rules generator, the geometric embeddings into Venn diagrams (observation, evaluation, and judgement)
transforming, by the rules generator, the Venn diagrams into Venn diagrams with shading and x-sequence using the premises (observation, evaluation, and judgement)
deriving, by the rules generator, one or more existential rules using the Venn diagrams with shading and x-sequence, wherein deriving an existential rule of the one or more existential rules includes performing a sequence of: (1) unifying […] respective syllogism logic representations into a unified Venn diagram, (2) resolving one or more conflicts identified in the unified Venn diagram to form […] a resolved unified Venn diagram, and (3) transforming […] the resolved unified Venn diagram into a conclusion Venn diagram that graphically represents a conclusion of the existential rule (observation, evaluation, and judgement) (Examiner notes that Euler diagrams existed well before mechanical computers such that it would be very reasonable to interpret Venn diagram creation as something that can practically be performed entirely in the mind with or without the assistance of tools such as pen and paper)
adding, by the rules generator, the one or more existential rules to the knowledge graph to increase an amount of knowledge for use by the intelligent application (observation, evaluation, and judgement)
Therefore, claim 22 recite an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 22 recites additional elements “in computer memory”, “providing a network connection to a rules generator to allow the rules generator to access the knowledge graph”, and “providing a network connection to the intelligent application to allow the intelligent application to access the knowledge graph”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Therefore, claim 22 is directed to a judicial exception.
Step 2B Analysis: Claim 22 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 22 amount to no more than mere instructions to apply the judicial exception using a generic computer component.
For the reasons above, claim 22 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to dependent claim 23. The additional limitations of the dependent claims are addressed briefly below:
Dependent claim 23 recites additional observation, evaluation, and judgement “the geometric embedding comprises concept hierarchies”
Regarding Claim 24: Claim 24 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 24 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 24 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass rules based inference, including the following:
dividing […] a knowledge graph that supports an intelligent application into classes (observation, evaluation, and judgement),
organizing, by the rules generator, geometric embeddings for entities in the knowledge graph (observation, evaluation, and judgement)
constructing, by the rules generator, premises, wherein the premises comprise syllogistic relationships for a pair of classes and wherein the premises are selected based on being identified as deriving useful existential rules so as to avoid evaluating every possible syllogism logic representation in the knowledge graph (observation, evaluation, and judgement)
transforming, by the rules generator, the geometric embeddings into Carroll's diagrams (observation, evaluation, and judgement)
transforming, by the rules generator, using the premises, the Carroll's diagrams into transformed Carroll's diagrams (observation, evaluation, and judgement)
deriving, by the rules generator, one or more existential rules using the transformed Carroll's diagrams, wherein deriving an existential rule of the one or more existential rules includes performing a sequence of: (1) unifying […] respective syllogism logic representations into a unified Carroll's diagram, (2) resolving one or more conflicts identified in the unified Carroll's diagram to form […] a resolved unified Carroll's diagram, and transforming […] the resolved unified Carroll's diagram into a conclusion Carroll's diagram that graphically represents a conclusion of the existential rule (observation, evaluation, and judgement)
adding, by the rules generator, the one or more existential rules to the knowledge graph to increase an amount of knowledge for use by the intelligent application (observation, evaluation, and judgement)
Therefore, claim 24 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 1 recites additional elements “in computer memory”, “providing a network connection to a rules generator to allow the rules generator to access the knowledge graph”, and “providing a network connection to the intelligent application to allow the intelligent application to access the knowledge graph”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Therefore, claim 1 is directed to a judicial exception.
Step 2B Analysis: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 1 amount to no more than mere instructions to apply the judicial exception using a generic computer component.
For the reasons above, claim 24 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to dependent claim 25. The additional limitations of the dependent claims are addressed briefly below:
Dependent claim 25 recites additional observation, evaluation, and judgement “wherein the transformed Carroll's diagrams comprise a selection from the group consisting of: 1's and 0's.”
Therefore, when considering the elements separately and in combination, they do not add significantly more to the inventive concept. Accordingly, claims 1-2, 4-9, 11-16, and 18-25 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-8, 11, 12-15, and 18-21 are rejected under U.S.C. §103 as being unpatentable over the combination of Sowa and (“Semantics of Conceptual Graphs”, 1979) and Latapie (US20210390423A1).
Regarding claim 1, Sowa teaches A computer-implemented method for deriving existential rules from knowledge graph data, comprising:([p. 40] "Conceptual graphs are a kind of semantic network. See Findler (1979) for surveys of a variety of such networks that have been used in AI. The diagram above illustrates some features of the conceptual graph notation" See FIG. on p. 40)
constructing, by the rules generator, a set of premises defining syllogistic forms for classes of the knowledge graph, ([p. 39] "The following conceptual graph shows the concepts and relationships in the sentence "Mary hit the piggy bank with a hammer." The boxes are concepts and the circles are conceptual relations. Inside each box or circle is a type label that designates the type of concept or relation. The conceptual relations labeled AONI". INST. and PTNT represent the linguistic cases agent, instrument, and patient of case grammar" [p. 40] "A method for defining new concept types: some canonical graph is specified as the differentia and a concept in that graph is designated the genus of the new type" Type interpreted as synonymous with classes. See set of premises)
wherein premises included in the set of premises identified as deriving useful existential rules are selected to avoid evaluating every possible syllogism logic representation in the knowledge graph([p. 39 §1] "the infinite sets commonly used in logic are intractable both for computers and for the human brain [...] If the domain is infinite, this specification (as well as many operations with such entities) may require non-trivial set theoretical assumptions. The process is thus often non-finitistic. It is doubtful whether we can realistically expect such structures to be somehow actually involved in our understanding of a sentence or in our contemplation of its meaning, notwithstanding the fact that this meaning is too often thought of as being determined by the class of possible worlds in which the sentence in question is true. It seems to me much likelier that what is involved in one's actual understanding of a sentence S is a mental anticipation of what can happen in one's step-by-step investigation of a world in which S is true")
deriving, by the rules generator, one or more existential rules using standard transformation rules present in the syllogism logic representations,(See standard transformation rules on p. 43 (Copy, Restrict, Join) Sowa derives existential rules using Standard transformation rules Copy (equivalent of existential specification/instantiation), Restrict (Dictum de omni), Join (M->P,All S->M=>All S->P))
wherein deriving an existential rule of the one or more existential rules includes performing a sequence of: (1) unifying [in computer memory] respective syllogism logic representations into a unified diagram,([p. 41] "If m=1 (a Horn clause), a copy of each graph ui is joined to some graph in C by a covering join. Then the assertion v is added to the resulting collection C'." The joined premises (copies of each condition u_i covering-joined to graphs in C) inside C' interpreted as unified diagram.)
(2) resolving one or more conflicts identified in the unified diagram to form [in computer memory] a resolved unified diagram, ([p. 41] "If m,=() (a denial or the empty clause), the collection C is said to be blocked." [p. 43] "With an appropriate canon, many undesirable graphs are ruled out as noncanonical, but the canonical graphs are not necessarily true. T~) ensure that only true graphs are derived from true graphs, the laws discussed in Section 4 eliminate inconsistent combinations" Blocking undesirable graphs/inconsistent combinations interpreted as resolving conflicts identified from the unified diagram)
and (3) transforming [in computer memory] the resolved unified diagram into a conclusion diagram that graphically represents a conclusion of the existential rule([p. 41] "Let C be a collection of canonical graphs, and let s be the sequent ul ..... u, -,- v~ ..... v,,,. If the conditions of s are satisfied by C, then s may be applied to C" Assertion graph v interpreted as graphical conclusion diagram representing a conclusion of the existential rule.)
and adding, by the rules generator, the existential rules to the knowledge graph to increase an amount of knowledge for use by the intelligent application.([p. 41] "A sequent is like a conditional assertion in Belnap's sense: When its conditions are not satisfied, it asserts nothing. But when they are satisfied, the assertions must be added to the current context. The next axiom states how they are added").
However, Sowa does not explicitly teach representing, in computer memory, a knowledge graph that supports an intelligent application using a geometric embedding;
providing a network connection to a rules generator to allow the rules generator to access the knowledge graph;
transforming, by the rules generator, the geometric embedding to syllogism logic representations using the set of premises;
and providing a network connection to the intelligent application to allow the intelligent application to access the knowledge graph.
Latapie, in the same field of endeavor, teaches representing, in computer memory, a knowledge graph that supports an intelligent application using a geometric embedding;([¶0054] "a conceptual space is a geometrical structure which is defined by a set of quality dimensions to allow for the measurement of semantic distances between instances of concepts and for the assignment of quality values to their quality dimensions" [¶0070] "This model may be represented as a DFRE knowledge graph and/or network simulation" [¶0087] "DFRE KB 416 acts as a dynamic and generic memory structure" [¶0058] "an output 314 coming from symbolic layer 306 may be provided to a user interface (UI) for review. For example, output 314 may comprise a video feed/stream augmented with inferences or conclusions made by the DFRE" Latapie explicitly discloses a DFRE that maintains a symbolic knowledge base. Application layer (alert module / UI / web services consumer) interfacing with DRFE interpreted as intelligent application)
providing a network connection to a rules generator to allow the rules generator to access the knowledge graph;([¶0009] "FIG. 3 illustrates an example hierarchy for a deep fusion reasoning engine (DFRE)" [¶0042] "The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein" [¶0137] "any number of DFRE agents 404 (e.g., a first DFRE agent 404 a through an Nth DFRE agent 404 n) may be executed by devices connected via a network 602" Latapie DFRE agent interpreted as rules generator. Latapie explicitly states that the DFRE maintains a symbolic knowledge base and that a conceptual space is a geometrical structure with conceptual vectors embedded in that space. The DFRE is explicitly network accessible.)
transforming, by the rules generator, the geometric embedding to syllogism logic representations using the set of premises; ([¶0051] "At the opposing end of hierarchy 300 may be symbolic layer 306 that may leverage symbolic learning. In general, symbolic learning includes a set of symbolic grammar rules specifying the representation language of the system, a set of symbolic inference rules specifying the reasoning competence of the system" [¶0060] "a DFRE generally refers to a cognitive engine capable of taking sub-symbolic data as input (e.g., raw or processed sensor data regarding a monitored system), recognizing symbolic concepts from that data, and applying symbolic reasoning to the concepts, to draw conclusions about the monitored system." [¶0059] "By way of example of symbolic reasoning, consider the ancient Greek syllogism: (1.) All men are mortal, (2.) Socrates is a man, and (3.) therefore, Socrates is mortal. Depending on the formal language used for the symbolic reasoner, these statements can be represented as symbols of a term logic." See FIG. 3)
and providing a network connection to the intelligent application to allow the intelligent application to access the knowledge graph([¶0009] "FIG. 3 illustrates an example hierarchy for a deep fusion reasoning engine (DFRE)" [¶0042] "The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein" [¶0137] "any number of DFRE agents 404 (e.g., a first DFRE agent 404 a through an Nth DFRE agent 404 n) may be executed by devices connected via a network 602" Latapie DFRE agent interpreted as rules generator. Latapie explicitly states that the DFRE maintains a symbolic knowledge base and that a conceptual space is a geometrical structure with conceptual vectors embedded in that space. The DFRE is explicitly network accessible.).
Sowa as well as Latapie are directed towards symbolic reasoning. Therefore, Sowa as well as Latapie are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Sowa with the teachings of Latapie by implementing the method in Sowa on a network connected computer for syllogism logic representations. Latapie provides as additional motivation for combination that this allows ([¶0049] “the system to operate across the full spectrum of sub-symbolic data all the way to the symbolic level.”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 4, the combination of Sowa, and Latapie teaches The method of claim 1, wherein transforming the geometric embedding to the syllogism logic representations comprises applying the set of premises to the geometric embedding.(Latapie [¶0054] "More formally, a conceptual space is a geometrical structure" [¶0057] "In general, the conceptual space at conceptual layer 304 allows for the discovery of regions that are naturally linked to abstract symbols used in symbolic layer 306. The overall model is bi-directional as it is planned for predictions and action prescriptions depending on the data causing the activation in sub-symbolic layer 302." [¶0058] "For practical applications, the reasoning logic in symbolic layer 306 may be non-axiomatic and constructed around the assumption of insufficient knowledge and resources (AIKR)" [¶0126] "FIG. 5 illustrates an example 500 showing the different forms of structural learning that the DFRE framework can employ. More specifically, the inference rules in example 500 relate premises S→M and M→P, leading to a conclusion S→P. Using these rules, the structural learning herein can be implemented using an ontology with respect to an Assumption of Insufficient Knowledge and Resources (AIKR) reasoning engine, as noted previously.").
Regarding claim 5, the combination of Sowa, and Latapie teaches The method of claim 1, wherein deriving the existential rules comprises applying a selection from a group consisting of unification rules, resolution rules, and transformation rules.(Latapie [¶0046] "DFRE process 248 may employ deep learning, in some embodiments. Generally, deep learning is a subset of machine learning that employs ANNs with multiple layers, with a given layer extracting features or transforming the outputs of the prior layer." [¶0049] "Typically, a reasoning engine is a form of inference engine that applies inference rules defined via an ontology language").
Regarding claim 6, the combination of Sowa, and Latapie teaches The method of claim 1, wherein the knowledge graph comprises classes.(Latapie [¶0143] "the DFRE knowledge graph groups information into four different levels, which are labeled L0, L1, L2, and L* and represent different levels of abstraction").
Regarding claim 7, the combination of Sowa, and Latapie teaches The method of claim 1, wherein the geometric embeddings comprise concept hierarchies.(Latapie [¶0143] "the DFRE knowledge graph groups information into four different levels, which are labeled L0, L1, L2, and L* and represent different levels of abstraction" [¶0053] "Linking sub-symbolic layer 302 and symbolic layer 306 may be conceptual layer 304 that leverages conceptual spaces. In general, conceptual spaces are a proposed framework for knowledge representation by a cognitive system on the conceptual level that provides a natural way of representing similarities. Conceptual spaces enable the interaction between different type of data representations as an intermediate level between sub-symbolic and symbolic representations." [¶0054] "More formally, a conceptual space is a geometrical structure which is defined by a set of quality dimensions to allow for the measurement of semantic distances between instances of concepts and for the assignment of quality values to their quality dimensions, which correspond to the properties of the concepts" [¶0077] "For example, DFRE agent 404 may perform semantic graph decomposition on DFRE KB 416 (e.g., a knowledge graph)").
Regarding claims 8 and 11-14, claims 8 and 11-14 are directed towards a computer program product for performing the method of claims 1 and 4-7, respectively. Therefore, the rejections applied to claims 1 and 4-7 also apply to claims 8 and 11-14. Claims 8 and 11-14 recite additional elements one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising: program instructions (Latapie ([¶0043] “various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein”).
Regarding claim 15 and 18-21, claims 15 and 18-21 are directed towards a system for performing the method of claims 1 and 4-7, respectively. Therefore, the rejection applied to claims 1 and 4-7 also apply to claims 15 and 18-21. Claims 15 and 18-21 recite additional elements one or more computer processors, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising: program instructions to Latapie ([¶0043] “various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein”).
Claims 2, 9, and 16 are rejected under U.S.C. §103 as being unpatentable over the combination of Sowa and Latapie in further view of Kurisu ("A Network Model For Computer Reasoning", 1988).
Regarding claim 2, the combination of Sowa, and Latapie teaches The method of claim 1.
However, the combination of Sowa, and Latapie doesn't explicitly teach wherein the syllogism logic representation comprises a selection from a group consisting of: a Venn diagram with shading and x-sequences, and a Carroll's diagram.
Kurisu, in the same field of endeavor, teaches The method of claim 1, wherein the syllogism logic representation comprises a selection from a group consisting of: a Venn diagram with shading and x-sequences, and a Carroll's diagram.(See FIG. 3.3 and FIG. 3.4).
The combination of Sowa and Latapie as well as Kurisu are directed towards logic embeddings of knowledge graphs. Therefore, the combination of Sowa and Latapie as well as Kurisu are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Sowa and Latapie with the teachings of Kurisu by using a Venn diagram as the syllogism logic representation. Kurisu provides as additional motivation for combination ([p. 31 §5] "Using the method of hypothetical reasoning, which allows our system to exhibit learning, we develop an improved consistent and coherent set from a set initially containing a questionable assertion.").
Regarding claim 9, claim 9 is directed towards a computer program product for performing the method of claim 2. Therefore, the rejection applied to claim 2 also applies to claim 9.
Regarding claim 16, claim 16 is directed towards a system for performing the method of claim 2. Therefore, the rejection applied to claim 2 also applies to claim 16.
Claims 22 and 23 are rejected under U.S.C. §103 as being unpatentable over the combination of Ren ("QUERY2BOX: REASONING OVER KNOWLEDGE GRAPHS IN VECTOR SPACE USING BOX EMBEDDINGS", 2020), Howse (“Spider Diagrams: A Diagrammatic Reasoning System”, 2001) and Sowa.
Claims 22 and 23 are rejected under U.S.C. §103 as being unpatentable over the combination of Ren, Latapie, Howse, and Sowa.
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FIG. 1 of Ren
Regarding claim 22, Ren teaches A computer-implemented method for deriving rules for a knowledge graph, comprising: dividing, [in computer memory], a knowledge graph that supports an intelligent application into classes;([Abstract] "to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query" [p. 1 §1] "Knowledge graphs (KGs) capture different types of relationships between entities" Knowledge graph entity relationship interpreted as synonymous with knowledge graph class. See FIG. 1 classes "Win", "Citizen", "Graduate", etc.)
organizing, by the rules generator, geometric embeddings for entities in the knowledge graph;([p. 2] "In QUERY2BOX, nodes of the KG are embedded as points in the vector space. We then obtain query embedding according to the computation graph (B) as a sequence of box operations: start with two nodes TuringAward and Canada and apply Win and Citizen projection operators, followed by an intersection operator (denoted as a shaded intersection of yellow and orange boxes) and another projection operator. The final embedding of the query is a green box and query’s answers are the entities inside the box" See FIG. 1)
constructing, by the rules generator, premises, wherein the premises comprise syllogistic relationships for a pair of classes;([p. 2 §1] "Here we present QUERY2BOX, an embedding-based framework for reasoning over KGs that is capable of handling arbitrary Existential Positive First-order (EPFO) logical queries (i.e., queries that include any set of ∧, ∨, and ∃) in a scalable manner. First, to accurately model a set of entities, our key idea is to use a closed region rather than a single point in the vector space. Specifically, we use a box (axis-aligned hyper-rectangle) to represent a query (Fig. 1(D)). This provides three important benefits: (1) Boxes naturally model sets of entities they enclose; (2) Logical operators (e.g., set intersection) can naturally be defined over boxes similarly as in Venn diagrams (Venn, 1880); (3) Executing logical operators over boxes results in new boxes, which means that the operations are closed; thus, logical reasoning can be efficiently performed in QUERY2BOX by iteratively updating boxes according to the query computation graph (Fig. 1(B)(D)). [...] we first answer individual conjunctive queries and then take the union of the answer entities." See class union in FIG. 1(D).)
and wherein the premises are selected based on being identified as deriving useful existential rules so as to avoid evaluating every possible syllogism logic representation in the knowledge graph;([p. 4 §3.1] "Given a set of entities S ⊆ V, and relation r ∈ R, this operator obtains ∪v∈SAr(v), where Ar(v) ≡ {v0 ∈ V : r(v, v0) = True}" [p. 6 §3.3] "The proof is provided in Appendix A, where the key is that with the introduction of the union operation any subset of denotation sets can be the answer, which forces us to model the powerset {∪qi∈SJqiK : S ⊆ {q1, . . . , qM}} in a vector space" Getting the union interpreted as selecting premises with existential rules to avoid evaluating every possible syllogism logic representation (but rather only evaluating the elements in the union).)
transforming, by the rules generator, the geometric embeddings into Venn diagrams;([p. 3 §2] "In this sense our work is also related to classical Venn Diagrams (Venn, 1880), where boxes are essentially the Venn Diagrams in vector space")
transforming, by the rules generator, the Venn diagrams into Venn diagrams with shading and x-sequence using the premises; and([p. 3 §2] "In this sense our work is also related to classical Venn Diagrams (Venn, 1880), where boxes are essentially the Venn Diagrams in vector space" See FIG. 1 which shows the embedding as Venn Diagrams with shading and entity x sequences.)
deriving, by the rules generator, one or more existential rules using the Venn diagrams with shading and x-sequence.([p. 2 §1] "Here we present QUERY2BOX, an embedding-based framework for reasoning over KGs that is capable of handling arbitrary Existential Positive First-order (EPFO) logical queries (i.e., queries that include any set of ∧, ∨, and ∃) in a scalable manner. First, to accurately model a set of entities, our key idea is to use a closed region rather than a single point in the vector space. Specifically, we use a box (axis-aligned hyper-rectangle) to represent a query (Fig. 1(D)). This provides three important benefits: (1) Boxes naturally model sets of entities they enclose; (2) Logical operators (e.g., set intersection) can naturally be defined over boxes similarly as in Venn diagrams (Venn, 1880); (3) Executing logical operators over boxes results in new boxes, which means that the operations are closed; thus, logical reasoning can be efficiently performed in QUERY2BOX by iteratively updating boxes according to the query computation graph (Fig. 1(B)(D)). [...] we first answer individual conjunctive queries and then take the union of the answer entities.").
However, Ren does not explicitly teach providing a network connection to a rules generator to allow the rules generator to access the knowledge graph;
wherein deriving an existential rule of the one or more existential rules includes performing a sequence of: (1) unifying in computer memory respective syllogism logic representations into a unified Venn diagram,
resolving one or more conflicts identified in the unified Venn diagram to form in computer memory a resolved unified Venn diagram,
and (3) transforming in computer memory the resolved unified Venn diagram into a conclusion Venn diagram that graphically represents a conclusion of the existential rule
adding by the rules generator the existential rules to the knowledge graph to increase an amount of knowledge for use by the intelligent application
and providing a network connection to the intelligent application to allow the intelligent application to access the knowledge graph.
Latapie, in the same field of endeavor, teaches providing a network connection to a rules generator to allow the rules generator to access the knowledge graph;([¶0009] "FIG. 3 illustrates an example hierarchy for a deep fusion reasoning engine (DFRE)" [¶0042] "The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein" [¶0137] "any number of DFRE agents 404 (e.g., a first DFRE agent 404 a through an Nth DFRE agent 404 n) may be executed by devices connected via a network 602" Latapie DFRE agent interpreted as rules generator. Latapie explicitly states that the DFRE maintains a symbolic knowledge base and that a conceptual space is a geometrical structure with conceptual vectors embedded in that space. The DFRE is explicitly network accessible.)
and providing a network connection to the intelligent application to allow the intelligent application to access the knowledge graph([¶0009] "FIG. 3 illustrates an example hierarchy for a deep fusion reasoning engine (DFRE)" [¶0042] "The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein" [¶0137] "any number of DFRE agents 404 (e.g., a first DFRE agent 404 a through an Nth DFRE agent 404 n) may be executed by devices connected via a network 602" Latapie DFRE agent interpreted as rules generator. Latapie explicitly states that the DFRE maintains a symbolic knowledge base and that a conceptual space is a geometrical structure with conceptual vectors embedded in that space. The DFRE is explicitly network accessible.).
Ren as well as Latapie are directed towards symbolic reasoning. Therefore, Ren as well as Latapie are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Ren with the teachings of Latapie by implementing the method in Ren on a network connected computer for syllogism logic representations. Latapie provides as additional motivation for combination that this allows ([¶0049] “the system to operate across the full spectrum of sub-symbolic data all the way to the symbolic level.”). This motivation for combination also applies to the remaining claims which depend on this combination.
However, the combination of Ren and Latapie does not explicitly teach wherein deriving an existential rule of the one or more existential rules includes performing a sequence of: (1) unifying in computer memory respective syllogism logic representations into a unified Venn diagram,
resolving one or more conflicts identified in the unified Venn diagram to form in computer memory a resolved unified Venn diagram,
and (3) transforming in computer memory the resolved unified Venn diagram into a conclusion Venn diagram that graphically represents a conclusion of the existential rule
adding by the rules generator the existential rules to the knowledge graph to increase an amount of knowledge for use by the intelligent application.
Howse, in the same field of endeavor, teaches wherein deriving an existential rule of the one or more existential rules includes performing a sequence of: (1) unifying in computer memory respective syllogism logic representations into a unified Venn diagram, ([p. 22] "Their combined diagram is formed by adding syntactic elements into the Venn diagram whose set of contours is C(D1)XC(D2)")
resolving one or more conflicts identified in the unified Venn diagram to form in computer memory a resolved unified Venn diagram, ([p. 19] "The Shading Condition. Erasing any shading remaining in only a part of a zone (z 4 for example in Figure 19) only changes the Shading Condition by removing conjuncts. Therefore, the validity of the first part of the rule follows by Lemma 4.1" [p. 22] "two diagrams D1 and D2 are given which do not contain conflicting information. Their combined diagram is formed by adding syntactic elements into the Venn diagram whose set of contours is C(D1)XC(D2)" See Rule 5 on p. 18. Erasing contours of combined graphs to ensure no conflicting information interpreted as synonymous with resolving one or more conflicts identified in the unified Venn diagram.)
and (3) transforming in computer memory the resolved unified Venn diagram into a conclusion Venn diagram that graphically represents a conclusion of the existential rule([p. 25] "This system is both sound and complete. The basic strategy to prove completeness (i.e. if MD1, D2,2, DnN " D@ then MD1, D2,2, DnN 7D@), is firstly to combine the individual diagrams in MD1, D2, 2, DnN into a single diagram D*; then expand both D* and D@ into compound diagrams in a way similar to the disjunctive normal form in symbolic logic").
The combination of Ren and Latapie as well as Howse are directed towards symbolic reasoning with Venn diagrams. Therefore, the combination of Ren and Latapie as well as Howse are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Ren and Latapie with the teachings of Howse by unifying logic representations into a unified Venn diagram, resolving conflicts, and transforming the unified Venn diagram into a conclusion diagram using Venn spider diagrams. Howse provides as additional motivation for combination ([p. 25 §5] "The general aim of this work is to provide the necessary mathematical underpinning for the development of software tools to aid reasoning with diagrams. In particular, we aim to prove similar results for constraint diagrams and to develop the tools that will enable them to become part of the software development standard").
However, the combination of Ren, Latapie, and Howse does not explicitly teach adding by the rules generator the existential rules to the knowledge graph to increase an amount of knowledge for use by the intelligent application.
Sowa, in the same field of endeavor, teaches adding by the rules generator the existential rules to the knowledge graph to increase an amount of knowledge for use by the intelligent application([p. 41] "A sequent is like a conditional assertion in Belnap's sense: When its conditions are not satisfied, it asserts nothing. But when they are satisfied, the assertions must be added to the current context. The next axiom states how they are added").
The combination of Ren, Latapie, and Howse as well as Sowa are directed towards symbolic reasoning. Therefore, the combination of Ren, Latapie, and Howse as well as Sowa are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Ren, Latapie, and Howse with the teachings of Sowa by adding the derived inference rules into the knowledge graph. Sowa provides as additional motivation for combination that the knowledge graph is extendable by definition ([p. 4] “A world basis has three components: a canon C, a finite set of sequents L called laws, and one or more finite collections of canonical graphs {Ct ..... Co} called contexts. No context C~ may be blocked by any law in L [...] The contexts are like Hintikka's surface models: they are finite, but extendible. The graphs in the canon provide default or plausible information that can be joined to extend the contexts, and the laws are constraints on the kinds of extensions that are possible”).
Regarding claim 23, the combination of Ren, Latapie, Howse, and Sowa teaches The method of claim 22, wherein the geometric embedding comprises concept hierarchies.(Ren [p. 2 º3] "Box embeddings have also been used to model hierarchical nature of concepts in an ontology with uncertainty").
Claims 24 and 25 are rejected under U.S.C. §103 as being unpatentable over the combination of Sowa, Latapie, and Gursoy (“A new algorithmic decision for categorical syllogisms via Carroll’s diagrams”, 2019).
Regarding claim 24, Sowa teaches A computer-implemented method for deriving rules for a knowledge graph, comprising: dividing, [in computer memory], a knowledge graph that supports an intelligent application into classes;([p. 39] "The following conceptual graph shows the concepts and relationships in the sentence "Mary hit the piggy bank with a hammer." The boxes are concepts and the circles are conceptual relations. Inside each box or circle is a type label that designates the type of concept or relation. The conceptual relations labeled AONI". INST. and PTNT represent the linguistic cases agent, instrument, and patient of case grammar" [p. 40] "A method for defining new concept types: some canonical graph is specified as the differentia and a concept in that graph is designated the genus of the new type" Type interpreted as synonymous with classes. See set of premises)
organizing, by the rules generator, geometric embeddings for entities in the knowledge graph;([p. 40] "Conceptual graphs are a kind of semantic network. See Findler (1979) for surveys of a variety of such networks that have been used in AI. The diagram above illustrates some features of the conceptual graph notation" See FIG. 1)
constructing, by the rules generator, premises, wherein the premises comprise syllogistic relationships for a pair of classes;([p. 40] "Conceptual graphs are a kind of semantic network. See Findler (1979) for surveys of a variety of such networks that have been used in AI. The diagram above illustrates some features of the conceptual graph notation" [p. 42] "Every conceptual relation has one or more arc~, each of which must be attached to a concept. If the relation has n arcs. it is said to be n-adic, and its arcs are labeled I, 2 ..... n." See FIG. 1 which shows explicitly shows premises between type pairs (dyadic conceptual relation))
and wherein the premises are selected based on being identified as deriving useful existential rules so as to avoid evaluating every possible syllogism logic representation in the knowledge graph([p. 39 §1] "the infinite sets commonly used in logic are intractable both for computers and for the human brain [...] If the domain is infinite, this specification (as well as many operations with such entities) may require non-trivial set theoretical assumptions. The process is thus often non-finitistic. It is doubtful whether we can realistically expect such structures to be somehow actually involved in our understanding of a sentence or in our contemplation of its meaning, notwithstanding the fact that this meaning is too often thought of as being determined by the class of possible worlds in which the sentence in question is true. It seems to me much likelier that what is involved in one's actual understanding of a sentence S is a mental anticipation of what can happen in one's step-by-step investigation of a world in which S is true")
and adding by the rules generator, the one or more existential rules to the knowledge graph to increase an amount of knowledge for use by the intelligent application([p. 41] "A sequent is like a conditional assertion in Belnap's sense: When its conditions are not satisfied, it asserts nothing. But when they are satisfied, the assertions must be added to the current context. The next axiom states how they are added").
However, Sowa does not explicitly teach providing a network connection to a rules generator to allow the rules generator to access the knowledge graph;
transforming, by the rules generator, the geometric embeddings into Carroll's diagrams;
transforming, by the rules generator, using the premises, the Carroll's diagrams into transformed Carroll's diagrams; and
deriving, by the rules generator, one or more existential rules using the transformed Carroll's diagrams, wherein deriving an existential rule of the one or more existential rules includes performing a sequence of: (1) unifying in computer memory respective syllogism logic representations into a unified Carroll's diagram,
(2) resolving one or more conflicts identified in the unified Carroll's diagram to form in computer memory a resolved unified Carroll's diagram,
and (3) transforming in computer memory the resolved unified Carroll's diagram into a conclusion Carroll's diagram that graphically represents a conclusion of the existential rule
and providing a network connection to the intelligent application to allow the intelligent application to access the knowledge graph.
Latapie, in the same field of endeavor, teaches providing a network connection to a rules generator to allow the rules generator to access the knowledge graph;([¶0009] "FIG. 3 illustrates an example hierarchy for a deep fusion reasoning engine (DFRE)" [¶0042] "The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein" [¶0137] "any number of DFRE agents 404 (e.g., a first DFRE agent 404 a through an Nth DFRE agent 404 n) may be executed by devices connected via a network 602" Latapie DFRE agent interpreted as rules generator. Latapie explicitly states that the DFRE maintains a symbolic knowledge base and that a conceptual space is a geometrical structure with conceptual vectors embedded in that space. The DFRE is explicitly network accessible.)
and providing a network connection to the intelligent application to allow the intelligent application to access the knowledge graph([¶0009] "FIG. 3 illustrates an example hierarchy for a deep fusion reasoning engine (DFRE)" [¶0042] "The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein" [¶0137] "any number of DFRE agents 404 (e.g., a first DFRE agent 404 a through an Nth DFRE agent 404 n) may be executed by devices connected via a network 602" Latapie DFRE agent interpreted as rules generator. Latapie explicitly states that the DFRE maintains a symbolic knowledge base and that a conceptual space is a geometrical structure with conceptual vectors embedded in that space. The DFRE is explicitly network accessible.).
Sowa as well as Latapie are directed towards symbolic reasoning. Therefore, Sowa as well as Latapie are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Sowa with the teachings of Latapie by implementing the method in Sowa on a network connected computer for syllogism logic representations. Latapie provides as additional motivation for combination that this allows ([¶0049] “the system to operate across the full spectrum of sub-symbolic data all the way to the symbolic level.”). This motivation for combination also applies to the remaining claims which depend on this combination.
However, the combination of Sowa, and Latapie does not explicitly teach transforming, by the rules generator, the geometric embeddings into Carroll's diagrams;
transforming, by the rules generator, using the premises, the Carroll's diagrams into transformed Carroll's diagrams; and
deriving, by the rules generator, one or more existential rules using the transformed Carroll's diagrams, wherein deriving an existential rule of the one or more existential rules includes performing a sequence of: (1) unifying in computer memory respective syllogism logic representations into a unified Carroll's diagram,
(2) resolving one or more conflicts identified in the unified Carroll's diagram to form in computer memory a resolved unified Carroll's diagram,
and (3) transforming in computer memory the resolved unified Carroll's diagram into a conclusion Carroll's diagram that graphically represents a conclusion of the existential rule.
Gursoy, in the same field of endeavor, teaches transforming, by the rules generator, the geometric embeddings into Carroll's diagrams;([p. 3] "Carroll’s diagrams thought up in 1884 are Venn-type diagrams where the universes are represented with a square")
transforming, by the rules generator, using the premises, the Carroll's diagrams into transformed Carroll's diagrams; and([p. 4 §3] "To obtain the conclusion of a syllogism, the knowledge of two premises is carried out on a trilateral diagram. This presentation is more useful for the elimination method than the Venn diagram view. In this way, one can observe the conclusion of the premises truer and quicker from a trilateral diagram. By dint of this method, we demean the data from a trilateral diagram to a bilateral diagram" demeaning data from trilateral diagram to bilateral diagram interpreted as transforming the resolved unified Carrolls diagram into a conclusion Carrolls diagram.)
deriving, by the rules generator, one or more existential rules using the transformed Carroll's diagrams, wherein deriving an existential rule of the one or more existential rules includes performing a sequence of: (1) unifying in computer memory respective syllogism logic representations into a unified Carroll's diagram, ([p. 4] "To obtain the conclusion of a syllogism, the knowledge of two premises is carried out on a trilateral diagram" [p. 3] "In the categorical syllogistic system, there are 64 different syllogistic forms for each figure. These are called moods. Therefore, the categorical syllogistic system is composed of 256 possible syllogisms. Only 24 of them are valid in this system. And they divided into two groups of 15 and 9. The syllogisms in the first group are valid unconditionally which are given in Table 3. The syllogisms in the second group called strengthened syllogism are valid conditionally or valid existential import, which is an explicit supposition of being of some terms, are given in Table 4." [p. 2] "the applications more typically include how to program a computer to carry out these tasks in artificial intelligence" Gursoy explicitly states that two premises are unified into a single trilateral Carrolls diagram.)
(2) resolving one or more conflicts identified in the unified Carroll's diagram to form in computer memory a resolved unified Carroll's diagram, ([p. 3] "Each cell in a trilateral diagram is marked with a 0, if there is no element and is marked with an I if it is not empty and another using of I, it could be on the line where the two cells is intersection, this means that at least one of these cells is not empty. So, I is different from 1. In addition to these, if any cell is blank, it has two possibilities, 0 or I" [p. 4] "If the quarter of the trilateral diagram contains an “I” in either cell, then it is certainly occupied, and one may mark the corresponding quarter of the bilateral diagram with a “1” to indicate that it is occupied" [p. 2] "the applications more typically include how to program a computer to carry out these tasks in artificial intelligence" Gursoy explicitly teaches that if a conflict exists in a quarter of the trilateral diagram (cells contain 0 and "I" or blank and "I") then the cell is resolved to 1. Gursoy second rule interpreted as conflict resolution to form a resolved unified Carrolls diagram.)
and (3) transforming in computer memory the resolved unified Carroll's diagram into a conclusion Carroll's diagram that graphically represents a conclusion of the existential rule([p. 4 §3] "To obtain the conclusion of a syllogism, the knowledge of two premises is carried out on a trilateral diagram. This presentation is more useful for the elimination method than the Venn diagram view. In this way, one can observe the conclusion of the premises truer and quicker from a trilateral diagram. By dint of this method, we demean the data from a trilateral diagram to a bilateral diagram" [p. 2] "the applications more typically include how to program a computer to carry out these tasks in artificial intelligence" demeaning data from trilateral diagram to bilateral diagram interpreted as transforming the resolved unified Carrolls diagram into a conclusion Carrolls diagram.).
The combination of Sowa and Latapie as well as Gursoy are directed towards symbolic reasoning. Therefore, the combination of Sowa and Latapie as well as Gursoy are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Sowa and Latapie with the teachings of Gursoy by using Carroll’s diagrams to derive existential rules to be added to the knowledge graph. Gursoy provides as additional motivation for combination ([Abstract] “We show that this logical reasoning is closed under the syllogistic criterion of inference. Therefore, the calculus system is implemented to let the formalism which comprises synchronically bilateral and trilateral diagrammatical appearance and naive algorithmic nature. And also, there is no need specific knowledge or exclusive ability to understand this decision procedure as well as to use it in an algorithmic system. Consequently, the empirical contributions of this paper are to design a polynomial-time algorithm at the first time in the literature to conduce to researchers getting into the act in different areas of science used categorical syllogisms such as artificial intelligence, engineering, computer science etc”).
Regarding claim 25, the combination of Sowa, Latapie, and Gursoy teaches The method of claim 24, wherein the transformed Carroll's diagrams comprise a selection from the group consisting of: 1's and 0's.(Gursoy See Table 10-17).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Minervi (US20180144252A1) is directed towards a computer based system for deriving existential rules from a knowledge graph.
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
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