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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 9/22/2023, 1/16/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Status
Claims 1-20 are pending.
Claims 1-20 are rejected.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Figure 3, Item 310, Figure 5, Item 510, and Figure 6, Item 603. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The use of the term Bluetooth, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Objections
Claim 2 is objected to because of the following informalities: “subject” in line 3 should be “subjecting”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 18 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “few” in claim 18 is a relative term which renders the claim indefinite. The term “few” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term “few” is used to modify the number of instances required for information (triple types) to be filtered out, and as such renders indefinite the threshold by which information is to be filtered.
Claim Rejections - 35 USC § 101
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method, system and CRM for dpredicting unknown triples within a knowledge graph. The judicial exception is not integrated into a practical application because while claims 1-20 attempt to integrate the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea, or it is insignificant extra solution activity and simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically methods (claims 1-18), a system (claim 19), and a CRM (claim 20).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claims 1, 19, and 20: Generating a list of reference triple types, extracting a set of semantic rules, extracting a query triple type, generating a set of candidate triples, and generating a tanked list of predicted unknown triples are processes of identifying, selecting, comparing/contrasting, and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. Expanding the query triple instance based on the specified criteria is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 2: The ranked list comprising the specified data is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 3: Extracting the ontology from the input knowledge graph is a process of identifying, selecting, comparing/contrasting, and calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 4: The ontology being extracted based upon the specified schema is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 5: The query triple comprising the specified data is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 6: The query triple instance comprising the specified information is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 7: The query triple instance comprising the specified information is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 8: The query triple instance comprising the specified information is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 9: Converting the specified information into corresponding entity types or predicate types is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 10: Identifying a set of plausible types, and generating the set of candidate triples via expansion of the query triple instance are processes of comparing/contrasting, selecting, and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 11: The set of semantic rules comprising the specified information is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 12: The directionality for each triple type indicating whether each triple type is reciprocal is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 13: Deriving directionality from a combination of the ontology and knowledge graph is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 14: Cardinality for each triple type indicating a multiplicity of subject/object instance within each type is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 15: Deriving cardinality by a counting procedure of a first, second and third number of instances within each triple is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 16: Directionality being used to bypass the prediction circuitry to generate at least one direct unknown triple is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 17: Cardinality being used to bypass the prediction circuitry to generate at least one direct unknown triple is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 18: Using a counting procedure to further reduce the list of reference triple types, and removing triple types with few triple instances are processes of calculating, comparing/contrasting, and selecting information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claims 1, 19, and 20: A computer, system, memory, instructions, processor, and non-transitory computer-readable medium are all generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)]. Receiving the input knowledge graph, obtaining an ontology, and receiving a query triple instance are insignificant extra solution activities specifically, mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827- 28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claim 2: Subjecting a physical entity of the specific gene to a wet lab test for evaluation is an insignificant extra solution activity specifically, mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827- 28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional, nonspecific, or insignificant extra solution activity. These additional elements include:
The additional elements of a computer, system, memory, instructions, processor, and non-transitory computer-readable medium are all generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of receiving the input knowledge graph, obtaining an ontology, receiving a query triple instance, and subjecting a physical entity of the specific gene to a wet lab test for evaluation (Conventional: Determining the level of a biomarker in blood by any means, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017), Using polymerase chain reaction to amplify and detect DNA, Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016); Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1377, 115 USPQ2d 1152, 1157 (Fed. Cir. 2015), and Detecting DNA or enzymes in a sample, Sequenom, 788 F.3d at 1377-78, 115 USPQ2d at 1157); Cleveland Clinic Foundation 859 F.3d at 1362, 123 USPQ2d at 1088 (Fed. Cir. 2017)) are all insignificant extra solution activities, specifically mere data gathering (See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-20, when the limitations are considered individually and as a whole, are rejected
under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-2, and 5-13 are rejected under 35 U.S.C. 103 as being unpatentable over Meznar et al. (Machine learning and knowledge extraction (2022) 1107-1123) and Liu et al. (Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (2021) 3308-3318).
Claim 1 is directed to a method for predicting unknown triples in an input knowledge graph.
Claim 19 is directed to a system for predicting unknown triples in an input knowledge graph.
Claim 20 is directed to a non-transitory computer-readable medium for predicting unknown triples in an input knowledge graph.
Meznar et al. teaches on page 1109, paragraph 3 “In our work, we propose to embed ontologies with graph-based methods to identify potentially novel relations for ontology completion”, and page 1111, paragraph 4 “In our work, we mainly focus on link prediction using embedding and proximity based methods as they do not require a specific representation or additional knowledge about the graph. By exploiting the semantic information of the knowledge graph, one can find missing links”, reading on a computer-implemented method for predicting unknown triples in an input knowledge graph, comprising: receiving the input knowledge graph; automatically obtaining an ontology of the input knowledge graph. Meznar et al. teaches on page 1112, paragraph 2 “Projection rules transform class subsumptions and property assertions between individuals directly into predefined triplets, without loss of information, while more complex logical expressions, such as property restrictions, are approximated with simple triplets that do not keep the exact logical relationships”, reading on generating a list of reference triple types for the input knowledge graph based on the ontology. Meznar et al. teaches on page 1111, paragraph 5 “One of the common approaches is to represent ontologies as graphs where nodes represent classes or individuals, and links encode semantic relationships defined by the ontology”, reading on extracting a set of semantic rules associated with the input knowledge graph. Meznar et al. teaches on page 1113, paragraph 2 “Recommendations for missing edges are obtained by selecting the elements in the matrix of the non-existing edges with the highest scores”, reading on automatically generating a ranked list of predicted unknown triples from the set of candidate triples using a pretrained link prediction circuitry.
Meznar et al. does not teach using query triples to extract corresponding triples from the knowledge graph.
Liu et al. teaches in the abstract “This paper introduces comparative reasoning over knowledge graphs, which aims to infer the commonality and inconsistency with respect to multiple pieces of clues… In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs”, on page 3311, column 2, paragraph 2 “The main idea behind these two functions is that we use a knowledge segment to express the semantic meaning of each query triple, and use influence function to discover a set of important elements in the knowledge segments”, on page 3311, column 2, paragraph 3 “Pairwise comparative reasoning aims to infer the commonality and/or inconsistency with respect to a pair of clues according to their knowledge segments”, and on page 3313, column 1, paragraph 2 “Then, we extract the knowledge segments for these two queries, and check whether these two segments are true”, reading on receiving a query triple instance; extracting a query triple type corresponding to the query triple instance, and a set of semantic rules based on the input knowledge graph and the ontology; generating a set of candidate triples by expanding the query triple instance based on and as restricted by the query triple type, the input knowledge graph, and the set of semantic rules.
It would have been obvious at the time of first filing to have modified the teachings of Menzar et al. for the construction of a knowledge graph using ontologies to provide ranked lists of predicted unknown triples, with the teachings of Liu et al. for a comparative knowledge graph triple search as the latter teaches in the abstract “We envision that the comparative reasoning will complement and expand the existing point-wise reasoning over knowledge graphs. In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs… Empirical evaluations demonstrate the efficacy of the proposed KompaRe”. One would have had a reasonable expectation of success given that the latter is designed to function with knowledge graphs for improved segmentation extraction based on comparative reasoning. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 2 is directed to the method of claim 1 but further specifies that the ranked list of triples comprises the specified information.
Menzar et al. teaches in Table 1, on page 1110 “Gene Function Prediction based on Gene Ontology Hierarchy Preserving Hashing; Gene Ontology terms are represented by a hierarchy-preserving hash function before computing semantic similarity for gene–function prediction”, reading on where the ranked list of predicted unknown triples comprises at least one triple including a node indicating a specific gene, and the method further comprises subject a physical entity of the specific gene to a wet lab testing for experimental evaluation according to the at least one triple.
Claim 5 is directed to the method of claim 1 but further specifies that thee triple instance comprises a placeholder for one of the three elements of the triple.
Liu et al. teaches on page 3315, column 2, paragraph 3 “Many effective reasoning methods have been developed for predicting the missing relation (i.e., link prediction) or the missing entity (i.e., entity prediction). In link prediction, given the ‘subject’ and the ‘object’ of a triple, it predicts the existence and/or the type of relation…In entity prediction, given the ‘subject’ and the ‘predicate’ of a triple, it predicts the missing ‘object’”, reading on wherein the query triple instance comprises a placeholder for one of a subject node, object node, and a predicate.
Claim 6 is directed to the method of claim 5, and thus claim 1, but further specifies the use of a placeholder for the object node.
Liu et al. teaches on page 3315, column 2, paragraph 3 “Many effective reasoning methods have been developed for predicting the missing relation (i.e., link prediction) or the missing entity (i.e., entity prediction). In link prediction, given the ‘subject’ and the ‘object’ of a triple, it predicts the existence and/or the type of relation…In entity prediction, given the ‘subject’ and the ‘predicate’ of a triple, it predicts the missing ‘object’”, reading on wherein the query triple instance comprises the subject node, the predicate, and the placeholder for the object node.
Claim 7 is directed to the method of claim 5, and thus claim 1, but further specifies the use of a placeholder for the subject node.
Liu et al. teaches on page 3315, column 2, paragraph 3 “Many effective reasoning methods have been developed for predicting the missing relation (i.e., link prediction) or the missing entity (i.e., entity prediction). In link prediction, given the ‘subject’ and the ‘object’ of a triple, it predicts the existence and/or the type of relation…In entity prediction, given the ‘subject’ and the ‘predicate’ of a triple, it predicts the missing ‘object’”, reading on wherein the query triple instance comprises the object node, the predicate, and the placeholder for the subject node.
Claim 8 is directed to the method of claim 5, and thus claim 1, but further specifies the use of a placeholder for the predicate.
Liu et al. teaches on page 3315, column 2, paragraph 3 “Many effective reasoning methods have been developed for predicting the missing relation (i.e., link prediction) or the missing entity (i.e., entity prediction). In link prediction, given the ‘subject’ and the ‘object’ of a triple, it predicts the existence and/or the type of relation…In entity prediction, given the ‘subject’ and the ‘predicate’ of a triple, it predicts the missing ‘object’”, reading on wherein the query triple instance comprises the subject node, the object node, and the placeholder for the predicate.
Claim 9 is directed to the method of claim 1 but further specifies the triple type being extracted by converting the subject, object and predicate into corresponding entity types except for the placeholder.
Liu et al. teaches on page 3310, paragraph 3 “Knowledge Segment (KS for short) is a connection subgraph of the knowledge graph that describes the semantic context of a piece of given clue (i.e., a node, a triple or a query graph)”, and on page 3315, column 2, paragraph 2 “Many effective reasoning methods have been developed for predicting the missing relation (i.e., link prediction) or the missing entity (i.e., entity prediction). In link prediction, given the ‘subject’ and the ‘object’ of a triple, it predicts the existence and/or the type of relation…In entity prediction, given the ‘subject’ and the ‘predicate’ of a triple, it predicts the missing ‘object’”, reading on wherein the query triple type is extracted by converting the subject node, object node, and the predicate into corresponding entity types or predicate types except the placeholder.
Claim 10 is directed to the method of claim 5, and thus claim 1, but further specifies identification of plausible types for the placeholder based on the type and reference of knowledge graph and generating the set of candidate triples.
Meznar et al. teaches on page 1113, paragraph 2 “Recommendations for missing edges are obtained by selecting the elements in the matrix of the non-existing edges with the highest scores”.
Liu et al. teaches on page 3316, column 1, paragraph 1 “In entity prediction, given the ‘subject’ and the ‘predicate’ of a triple, it predicts the missing ‘object’. For example, GQEs [16] embeds the graph nodes in a low dimensional space, and treats the logical operators as learned geometric operations”, reading on wherein generating the set of candidate triples comprises: identifying a set of plausible entity or predicate types for the placeholder of the query triple instance based on the query triple type and the list of reference triple types of the input knowledge graph; and generating the set of candidate triples by expanding the placeholder of the query triple instance with a set of nodes or predicates of the input knowledge graph that conform with the set of plausible entity or predicate types for the placeholder.
Claim 11 is directed to the method of claim 10, and thus claim 1, but further specifies that the extracted semantic rules comprise at least one of directionality and cardinality.
Meznar et al. teaches on page 1111, paragraph 5 “One of the common approaches is to represent ontologies as graphs where nodes represent classes or individuals, and links encode semantic relationships defined by the ontology”.
Liu et al. teaches on page 3313, column 1, paragraph 2 “After we extract the knowledge segments for <o1, isTypeOf, o2> and <o2, isTypeOf, o1>, we treat each knowledge segment as a directed graph, and calculate how much information can be transferred from the subject to the object”, and it would be inherent that the extraction of triples from a directed knowledge graph would result in the semantic rules containing directionality of the graph, thereby reading on wherein the set of semantic rules comprise at least one of a directionality and a cardinality of each triple type of the list of reference triple types for the input knowledge graph.
Claim 12 is directed to the method of claim 11, and thus claim 1, but further specifies that the directionality indicates whether each triple is reciprocal with respect to subject and object.
Liu et al. teaches on page 3313, column 1, paragraph 2 “After we extract the knowledge segments for <o1, isTypeOf, o2> and <o2, isTypeOf, o1>, we treat each knowledge segment as a directed graph, and calculate how much information can be transferred from the subject to the object”, and on page 3311, column 2, paragraph 1 “In order to find the edge-specific knowledge segments for each ei ∈ EQ, we again use the k-simple shortest path method to extract the paths with the lowest cost. The cost of a path is equal to the sum of the reciprocal of the predicate-predicate similarity of all edges in the path”, reading on wherein the directionality for each triple type of the list of reference triple types indicates whether each triple type is reciprocal with respect to a subject and an object within each triple type.
Claim 13 is directed to the method of claim 12, and thus claim 1, but further specifies that directionality be derived from a combination of the ontology and the input knowledge graph.
Meznar et al. teaches on page 1109, paragraph 3 “In our work, we propose to embed ontologies with graph-based methods to identify potentially novel relations for ontology completion”, and page 1111, paragraph 4 “In our work, we mainly focus on link prediction using embedding and proximity based methods as they do not require a specific representation or additional knowledge about the graph. By exploiting the semantic information of the knowledge graph, one can find missing links”.
Liu et al. teaches on page 3313, column 1, paragraph 2 “After we extract the knowledge segments for <o1, isTypeOf, o2> and <o2, isTypeOf, o1>, we treat each knowledge segment as a directed graph, and calculate how much information can be transferred from the subject to the object”, reading on where the directionality is derived from a combination of the ontology and the input knowledge graph.
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Meznar et al. (Machine learning and knowledge extraction (2022) 1107-1123) and Liu et al. (Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (2021) 3308-3318) as applied to claims 1-2 and 5-13 above, and further in view of Jin et al. (Proceedings of the 2022 conference of the North American chapter of the Association for Computational Linguistics: human language technologies (2022) 2013-2025).
Claim 3 is directed to the method of claim 1 but further specifies the extraction of the ontology from the knowledge graph.
Meznar et al. and Liu et al. teach the method of claim 1 as previously described.
Meznar et al. and Liu et al. do not teach the extraction of the ontology from the knowledge graph.
Jin et al. teaches in the abstract “To induce event schemas from historical events, previous work uses an event-by-event scheme, ignoring the global structure of the entire schema graph. We propose a new event schema induction framework using double graph autoencoders, which captures the global dependencies among nodes in event graphs. Specifically, we first extract the event skeleton from an event graph and design a variational directed acyclic graph (DAG) autoencoder to learn its global structure. Then we further fill in the event arguments for the skeleton, and use another Graph Convolutional Network (GCN) based autoencoder to reconstruct entity-entity relations as well as to detect coreferential entities. By performing this twostage induction decomposition, the model can avoid reconstructing the entire graph in one step, allowing it to focus on learning global structures between events”, reading on wherein the ontology is automatically extracted from the input knowledge graph.
It would have been obvious at the time of first filing to have modified the teachings of Meznar et al. and Liu et al. for the method of claim 1, with the teachings of Jin et al. for extraction of ontology from a knowledge graph as the latter teaches in the abstract “By performing this two stage induction decomposition, the model can avoid reconstructing the entire graph in one step, allowing it to focus on learning global structures between events. Experimental results on three event graph datasets demonstrate that our method achieves state-of-the-art performance and induces high-quality event schemas with global consistency”. One would have had a reasonable expectation of success given that Jin et al. is taking the types of knowledge graphs that are constructed in Meznar et al. and Liu et al., and rebuilding the ontologies they are created from and would therefore merely be a substitution of one known method for another. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 4 is directed to the method of claim 1 but further specifies that the ontology is extracted based on the schema from which the knowledge graph is created.
Meznar et al. and Liu et al. teach the method of claim 1 as previously described.
Meznar et al. and Liu et al. do not teach the extraction of the ontology from the knowledge graph.
Jin et al. teaches in the abstract “To induce event schemas from historical events, previous work uses an event-by-event scheme, ignoring the global structure of the entire schema graph. We propose a new event schema induction framework using double graph autoencoders, which captures the global dependencies among nodes in event graphs. Specifically, we first extract the event skeleton from an event graph and design a variational directed acyclic graph (DAG) autoencoder to learn its global structure. Then we further fill in the event arguments for the skeleton, and use another Graph Convolutional Network (GCN) based autoencoder to reconstruct entity-entity relations as well as to detect coreferential entities. By performing this twostage induction decomposition, the model can avoid reconstructing the entire graph in one step, allowing it to focus on learning global structures between events”, reading on wherein the ontology is extracted based on a schema from which the input knowledge graph is instantiated.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Meznar et al. (Machine learning and knowledge extraction (2022) 1107-1123) and Liu et al. (Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (2021) 3308-3318) as applied to claims 1-2 and 5-13 above, and further in view of Principe et al. (The VLDB Journal (2022) 851-876).
Claim 14 is directed to the method of claim 12, and thus claim 1, but further specifies that the cardinality for each triple indicates a multiplicity of the subject or object within the triples.
Meznar et al. and Liu et al. teach the method of claim 1 as previously described.
Meznar et al. and Liu et al. do not teach the cardinality for each triple indicates a multiplicity of the subject or object within the triples.
Principe et al. teaches in the abstract “Profiling approaches have been proposed to summarize large KGs with the aim to produce concise and meaningful representation so that they can be easily managed. However, constructing profiles and calculating several statistics such as cardinality descriptors or inferences are resource expensive. In this paper, we present ABSTAT-HD, a highly distributed profiling tool that supports users in profiling and understanding big and complex knowledge graphs”, and on page 856, column 2, paragraph 5 “Cardinality descriptors are divided into direct cardinality descriptors and inverse cardinality descriptors. Given a pattern (C, P, D) the maximum (minimum, average) direct cardinality is the maximum (minimum, average) number of distinct entities of type C (in subject position) linked to a single entity of type D through the predicate P. Similarly, the maximum (minimum, average) inverse cardinality is the maximum (minimum, average) number of distinct entities of type D (in object position) linked to a single entity of type C through the predicate P”, reading on wherein the cardinality for each triple type of the list of reference triple types indicates a multiplicity of a subject instance and/or an object instance within each triple type.
It would have been obvious at the time of first filing to have modified the teachings of Meznar et al. and Liu et al. for the method of claims 1-2, and 5-13, with the teachings of Principe et al. for the calculation of cardinality using a multiplicity of the subject/object of the triples as the latter teaches in the abstract “Processing large-scale and highly interconnected Knowledge Graphs (KG) is becoming crucial for many applications such as recommender systems, question answering, etc. Profiling approaches have been proposed to summarize large KGs with the aim to produce concise and meaningful representation so that they can be easily managed… In this paper, we present ABSTAT-HD, a highly distributed profiling tool that supports users in profiling and understanding big and complex knowledge graphs. We demonstrate the impact of the new architecture of ABSTAT-HD by presenting a set of experiments that show its scalability with respect to three dimensions of the data to be processed: size, complexity and workload. The experimentation shows that our profiling framework provides informative and concise profiles, and can process and manage very large KGs”. One would have had a reasonable expectation of success given that the method is designed to summarize the knowledge graphs being used by the other two pieces of cited art and this would merely be substitution of known methods. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 15 is directed to the method of claim 14, and thus claim 1, but further specifies that cardinality is derived from a counting procedure of a first, second, and third number of triple, subject, and object instances.
Meznar et al. and Liu et al. teach the method of claim 1 as previously described.
Meznar et al. and Liu et al. do not teach that cardinality is derived from a counting procedure of a first, second, and third number of triple, subject, and object instances.
Principe et al. teaches in the abstract “Profiling approaches have been proposed to summarize large KGs with the aim to produce concise and meaningful representation so that they can be easily managed. However, constructing profiles and calculating several statistics such as cardinality descriptors or inferences are resource expensive. In this paper, we present ABSTAT-HD, a highly distributed profiling tool that supports users in profiling and understanding big and complex knowledge graphs”, and on page 856, column 2, paragraph 5 “Cardinality descriptors are divided into direct cardinality descriptors and inverse cardinality descriptors. Given a pattern (C, P, D) the maximum (minimum, average) direct cardinality is the maximum (minimum, average) number of distinct entities of type C (in subject position) linked to a single entity of type D through the predicate P. Similarly, the maximum (minimum, average) inverse cardinality is the maximum (minimum, average) number of distinct entities of type D (in object position) linked to a single entity of type C through the predicate P”, reading on where the cardinality is derived by a counting procedure of a first number of triple instances a second number of unique subject instances and a third number of unique object instances associated with each triple type.
Claims 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Meznar et al. (Machine learning and knowledge extraction (2022) 1107-1123), Liu et al. (Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (2021) 3308-3318), and Principe et al. (The VLDB Journal (2022) 851-876) as applied to claims 14-15 above, and further in view of Ott et al. (arXiv preprint (2021) 1-18).
Claim 16 is directed to the method of claim 14, and thus claim 1, but further specifies that the directionality be used to bypass the pretrained link to generate a triple.
Meznar et al., Liu et al., and Principe et al. teach the method of claim 14 as previously described.
Meznar et al., Liu et al., and Principe et al. do not teach that the directionality be used to bypass the pretrained link to generate a triple.
Ott et al. teaches in the abstract “current approaches for aggregating predictions made by multiple rules are affected by redundancies. We improve upon AnyBURL by introducing the SAFRAN rule application framework, which uses a novel aggregation approach called Non-redundant Noisy-OR that detects and clusters redundant rules prior to aggregation”, on page 6, paragraph 2 “Furthermore as relations may vary in multiplicity the direction of the prediction has to taken into account…a distinct threshold for the prediction of heads and prediction of tails for each relation is required in order to be able to optimally cluster an entire rule set… For each combination of rule types an independent threshold parameter is used to determine redundancy. Conclusively, the threshold of similarity for rules that predict entities of direction d (head or tail) of a h-triple (we use h to refer to a relation, while r is used to refer to rules) is given… SAFRAN uses one of two search strategies for finding the optimal thresholds: grid search (parameter sweep) and random search. For grid search the range of possible thresholds [0:0; 1:0] is divided by n equally distant steps and each threshold is subsequently used for clustering. For the grid search, we do not distinguish between rule-type specific parameters but use a single relation-specific fix parameter to limit the search space”, reading on wherein the directionality is used at least to bypass the pretrained link prediction circuitry to generate at least one direct unknown triple that is considered as a true triple of the input knowledge graph.
It would have been obvious at the time of first filing to have modified the teachings of Meznar et al., Liu et al., and Principe et al. for the method of claim 14, with the teachings of Ott et al. for the use of rules-based thresholding for various parameters including directionality, in link prediction as the latter states in the abstract “SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmarks FB15K-237, WN18RR and YAGO3-10. Furthermore, it exceeds the results of multiple established embedding-based algorithms on FB15K-237 and WN18RR and narrows the gap between rule-based and embedding-based algorithms on YAGO3-10”. One would have had a reasonable expectation of success given that this method is designed for reducing the total number of rules used within a knowledge graph, while maximizing the effects of the rules used and would therefore be a mere substitution of one known method for another. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 16 is directed to the method of claim 14, and thus claim 1, but further specifies that the directionality be used to bypass the pretrained link to generate a triple.
Meznar et al., Liu et al., and Principe et al. teach the method of claim 14 as previously described.
Meznar et al., Liu et al., and Principe et al. do not teach that the directionality be used to bypass the pretrained link to generate a triple.
Ott et al. teaches in the abstract “current approaches for aggregating predictions made by multiple rules are affected by redundancies. We improve upon AnyBURL by introducing the SAFRAN rule application framework, which uses a novel aggregation approach called Non-redundant Noisy-OR that detects and clusters redundant rules prior to aggregation”, on page 6, paragraph 2 “Furthermore as relations may vary in multiplicity the direction of the prediction has to taken into account…a distinct threshold for the prediction of heads and prediction of tails for each relation is required in order to be able to optimally cluster an entire rule set… For each combination of rule types an independent threshold parameter is used to determine redundancy. Conclusively, the threshold of similarity for rules that predict entities of direction d (head or tail) of a h-triple (we use h to refer to a relation, while r is used to refer to rules) is given…SAFRAN uses one of two search strategies for finding the optimal thresholds: grid search (parameter sweep) and random search. For grid search the range of possible thresholds [0:0; 1:0] is divided by n equally distant steps and each threshold is subsequently used for clustering. For the grid search, we do not distinguish between rule-type specific parameters but use a single relation-specific fix parameter to limit the search space”, and it would be obvious to a person skilled in the art that if cardinality were being used as a rule previously (See Principe et al.) then it too would be part of the deterministic triple prediction of Ott et al., thereby reading on wherein the cardinality is used at least to bypass the pretrained link prediction circuitry to generate at least one direct unknown triple that is considered as a true triple of the input knowledge graph.
Claim 18 is directed to the method of claim 14, and thus claim 1, but further specifies the list of triples being further reduced using a counting procedure and outlier/error classification.
Meznar et al., Liu et al., and Principe et al. teach the method of claim 14 as previously described.
Menzar et al. teaches on page 1116, paragraph 3 “We evaluated the link prediction capabilities on transformed ontologies by using a five-fold cross-validation. We created these folds as follows. We started with a directed (multi)graph with multiple edges between each pair of nodes. We transformed this graph into a simple undirected graph and removed elements on the diagonal of the adjacency matrix”.
Liu et al. teaches on page 3312, column 2, paragraph 2 “In order to find the key elements, we propose to use the influence function w.r.t. the knowledge segment similarity. The basic idea is that if we perturb a key element (e.g., change the attribute of a node or remove a node/edge), it would have a significant impact on the overall similarity between these two knowledge segments. Let KS1 and KS2 be the two knowledge segments. We can treat the knowledge segment as an attributed graph, where different entities have different attributes.We use random walk graph kernel with node attribute to measure the similarity between these two knowledge segments”.
Principe et al. teaches on page 857, column 2, paragraph 2 “Consider Step 3 in Fig. 3, since alias GT alternativeName, the triple < Cher alternative Name "Cher Bono"> is considered redundant as < Cher alias "Cher Bono"> is also present in AP, therefore it is removed”, reading on wherein the list of reference triple types for the input knowledge graph is further reduced using a counting procedure performed on the input knowledge graph and removing triple types with few triple instances that can be counted as outlier or error.
Conclusion
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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