DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to the Application filed on 01/17/2024. Claims 1-8 and 11-22 are pending in the case. Claims 1, 12, and 18 are independent claims.
Claim Rejections - 35 U.S.C. § 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 1-8 and 11-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-8 and 11 are directed towards the statutory category of a process. Claims 12-17 are directed towards the statutory category of an article of manufacture. Claims 18-22 are directed towards the statutory category of a machine.
With respect to claim 1:
2A Prong 1: This claim is directed to a judicial exception.
A method for task execution based on a knowledge graph obtained through entity alignment, the method comprising (mental process):
obtaining a knowledge graph pair that comprises a first knowledge graph and a second knowledge graph (mental process);
selecting a target entity from entities in the first knowledge graph (mental process);
determining a centrality of the target entity based on an entity corresponding to a neighboring node of a node corresponding to the target entity in the first knowledge graph within a preset adjacency range (mental process);
determining an uncertainty of the target entity based on alignment probabilities between one or more first entities in the second knowledge graph and the target entity, and alignment probabilities between the entity corresponding to the neighboring node and one or more second entities in the second knowledge graph (mental process); and
constructing a sample entity pair based on the centrality of the target entity and the uncertainty of the target entity (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
training a to-be-trained entity alignment model based on the sample entity pair (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
performing entity alignment on to-be-aligned knowledge graphs according to the trained entity alignment model, to obtain a merged knowledge graph after the entity alignment (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
executing a target task based on the merged knowledge graph (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
training a to-be-trained entity alignment model based on the sample entity pair (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
performing entity alignment on to-be-aligned knowledge graphs according to the trained entity alignment model, to obtain a merged knowledge graph after the entity alignment (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
executing a target task based on the merged knowledge graph (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 2:
2A Prong 1: This claim is directed to a judicial exception.
wherein selecting the target entity from the entities in the first knowledge graph comprises (mental process):
determining an out-degree of each of the entities in the first knowledge graph based on connection relationships between the entities in the first knowledge graph (mental process); and
filtering the entities in the first knowledge graph based on the out-degree of each of the entities in the first knowledge graph to obtain the target entity (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 3:
2A Prong 1: This claim is directed to a judicial exception.
wherein determining the centrality of the target entity based on the entity corresponding to the neighboring node of the node corresponding to the target entity in the first knowledge graph within the preset adjacency range comprises (mental process):
determining a single centrality of the target entity based on an out-degree of the target entity (mental process);
determining a single centrality of the entity corresponding to the neighboring node based on an out-degree of the entity corresponding to the neighboring node of the node corresponding to the target entity in the first knowledge graph within the preset adjacency range (mental process); and
determining the centrality of the target entity based on the single centrality of the target entity and the single centrality of the entity corresponding to the neighboring node (mental process);
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 4:
2A Prong 1: This claim is directed to a judicial exception.
wherein determining the uncertainty of the target entity based on the alignment probabilities between the one or more first entities in the second knowledge graph and the target entity, and the alignment probabilities between the entity corresponding to the neighboring node and the one or more first entities in the second knowledge graph comprises (mental process):
determining the alignment probabilities between the target entity and the one or more first entities in the second knowledge graph (mental process);
determining a single uncertainty of the target entity based on the alignment probabilities between the target entity and the one or more first entities in the second knowledge graph (mental process);
determining the alignment probabilities between the entity corresponding to the neighboring node and the one or more second entities in the second knowledge graph based on the entity corresponding to the neighboring node and the one or more second entities in the second knowledge graph (mental process);
determining a single uncertainty of the entity corresponding to the neighboring node based on the alignment probabilities between the entity corresponding to the neighboring node and the one or more second entities in the second knowledge graph (mental process); and
determining the uncertainty of the target entity based on the single uncertainty of the target entity and the single uncertainty of the entity corresponding to the neighboring node (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 5:
2A Prong 1: This claim is directed to a judicial exception.
wherein determining the single uncertainty of the target entity based on the alignment probabilities between the target entity and the one or more first entities in the second knowledge graph comprises (mental process):
determining a difference between the alignment probabilities between the target entity and the one or more first entities in the second knowledge graph based on the alignment probabilities between the target entity and the one or more first entities in the second knowledge graph (mental process); and
determining the single uncertainty of the target entity based on the difference between the alignment probabilities between the target entity and the one or more first entities in the second knowledge graph (mental process);
wherein determining the single uncertainty of the entity corresponding to the neighboring node based on the alignment probabilities between the entity corresponding to the neighboring node and the one or more second entities in the second knowledge graph comprises (mental process):
determining a difference between the alignment probabilities between the entity corresponding to the neighboring node and the one or more second entities in the second knowledge graph based on the alignment probabilities between the entity corresponding to the neighboring node and the one or more second entities in the second knowledge graph (mental process); and
determining the single uncertainty of the entity corresponding to the neighboring node based on the difference between the alignment probabilities between the entity corresponding to the neighboring node and the one or more second entities in the second knowledge graph (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 6:
2A Prong 1: This claim is directed to a judicial exception.
wherein constructing the sample entity pair based on the centrality of the target entity and the uncertainty of the target entity comprises (mental process):
determining a centrality weight for the centrality of the target entity, and an uncertainty weight for the uncertainty of the target entity (mental process);
determining a representative value of the target entity based on the centrality of the target entity, the centrality weight, the uncertainty of the target entity, and the uncertainty weight (mental process);
filtering target entities based on representative values of the target entities to determine at least one target entity (mental process); and
constructing the sample entity pair based on a determined target entity (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 7:
2A Prong 1: This claim is directed to a judicial exception.
determining the centrality weight for the centrality of the target entity, and the uncertainty weight for the uncertainty of the target entity comprises (mental process):
determining the centrality weight for the centrality of the target entity, and the uncertainty weight for the uncertainty of the target entity based on a difference between the centrality of the target entity and the uncertainty of the target entity (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 8:
2A Prong 1: This claim is directed to a judicial exception.
determining the centrality weight for the centrality of the target entity, and the uncertainty weight for the uncertainty of the target entity comprises (mental process):
determining the centrality weight for the centrality of the target entity, and the uncertainty weight for the uncertainty of the target entity based on a number of a current training round of an entity alignment model, wherein the larger is the number of the current training round, the smaller is the centrality weight for the centrality of the target entity, and the greater is the uncertainty weight for the uncertainty of the target entity (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 11:
2A Prong 1: This claim is directed to a judicial exception.
wherein constructing the sample entity pair based on the determined target entity comprises (mental process);
after determining the determined target entity from the first knowledge graph, determining an entity in the second knowledge graph corresponding to the determined target entity based on correspondence relationships between entities in the first knowledge graph and entities in the second knowledge graph (mental process); and
constructing the sample entity pair based on the determined target entity in the first knowledge graph and the determined entity in the second knowledge graph (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The remaining claims 12-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more for at least the same reasons as those given above with respect to claims 1-11 with only the addition of generic computer components under step 2A prong 1. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2). Limitations that merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not integrate the judicial exception into a practical application under step 2A prong 2. Refer to MPEP §2106.04(d). Moreover, the limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not recite any additional elements/limitations that amount to significantly more. Accordingly, the claimed invention recites an abstract idea without significantly more.
Claim Rejections - 35 U.S.C. § 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 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1, 2, 12, 13, 18, and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Liu et al. (Liu, Bing, Harrisen Scells, Guido Zuccon, Wen Hua, and Genghong Zhao. "ActiveEA: Active learning for neural entity alignment." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3364-3374. 2021, hereinafter Liu) in view of Berrendorf et al. (Berrendorf, Max, Evgeniy Faerman, and Volker Tresp. "Active learning for entity alignment." In European Conference on Information Retrieval, pp. 48-62. Cham: Springer International Publishing, 2021, hereinafter Berrendorf), Xin et al. (Xin, Kexuan, Zequn Sun, Wen Hua, Wei Hu, Jianfeng Qu, and Xiaofang Zhou. "Large-scale entity alignment via knowledge graph merging, partitioning and embedding." In Proceedings of the 31st ACM international conference on information & knowledge management, pp. 2240-2249. 2022, hereinafter Xin), and Hu et al. (U.S. Pat. App. Pub. No. 2016/0189028, hereinafter Hu).
As to independent claims 1, 12, and 18, Liu teaches a method for task execution based on a knowledge graph obtained through entity alignment, the method comprising:
obtaining a knowledge graph pair that comprises a first knowledge graph and a second knowledge graph (Figure 1, 1st graph and 2nd graph);
selecting a target entity from entities in the first knowledge graph (Page 3366, "select entities from one KG");…
determining an uncertainty of the target entity based on alignment probabilities between one or more first entities in the second knowledge graph and the target entity, and alignment probabilities between the entity corresponding to the neighboring node and one or more second entities in the second knowledge graph (Page 3366, section entitled "3.3 Structure-aware uncertainty sampling");
constructing a sample entity pair based on the centrality of the target entity and the uncertainty of the target entity (Page 3366, "matchable entities L+π,B");
training a to-be-trained entity alignment model based on the sample entity pair (Page 3366, "produce a training set Lπ,B. We train the model on Lπ,B");….
Liu does not appear to expressly teach determining a centrality of the target entity based on an entity corresponding to a neighboring node of a node corresponding to the target entity in the first knowledge graph within a preset adjacency range; performing entity alignment on to-be-aligned knowledge graphs according to the trained entity alignment model, to obtain a merged knowledge graph after the entity alignment; and executing a target task based on the merged knowledge graph.
Berrendorf teaches determining a centrality of the target entity based on an entity corresponding to a neighboring node of a node corresponding to the target entity in the first knowledge graph within a preset adjacency range (Page 52, section entitled "Node Centrality").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the Active Learning for Neural Entity Alignment of Liu to include the Active Learning for Entity Alignment techniques of Berrendorf to increase labeling efficiency (see Berrendorf at abstract).
Xin teaches performing entity alignment on to-be-aligned knowledge graphs according to the trained entity alignment model, to obtain a merged knowledge graph after the entity alignment (Figure 2, Input source and target KGs, KG merging);.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the Active Learning for Neural Entity Alignment of Liu to include the Entity Alignment via Knowledge Graph Merging techniques of Xin to reduce the structure and alignment loss (see Xin at abstract).
Hu teaches executing a target task based on the merged knowledge graph (Paragraph 14, "using the knowledge graph data to generate and output a media content recommendation").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the Active Learning for Neural Entity Alignment of Liu to include the knowledge graph recommendation techniques of Hu to make curation of recommendations less arduous (see Hu at paragraph 2).
As to dependent claims 2, 13, and 19, Liu further teaches filtering the entities in the first knowledge graph based on the out-degree of each of the entities in the first knowledge graph to obtain the target entity (Page 3368, "selects entities with high degrees").
Liu does not appear to expressly teach selecting the target entity from the entities in the first knowledge graph comprises: determining an out-degree of each of the entities in the first knowledge graph based on connection relationships between the entities in the first knowledge graph.
Berrendorf teaches selecting the target entity from the entities in the first knowledge graph comprises: determining an out-degree of each of the entities in the first knowledge graph based on connection relationships between the entities in the first knowledge graph (Page 52, section entitled "Node Centrality").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the Active Learning for Neural Entity Alignment of Liu to include the Active Learning for Entity Alignment techniques of Berrendorf to increase labeling efficiency (see Berrendorf at abstract).
Claims 3, 14, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Liu in view of Berrendorf, Xin, Hu, and Cai et al. (Cai, Hongyun, Vincent W. Zheng, and Kevin Chen-Chuan Chang. "Active learning for graph embedding." arXiv preprint arXiv:1705.05085 (2017), hereinafter Cai).
As to dependent claims 3, 14, and 20, the respective rejections of claims 2, 13, and 18 are incorporated.
Liu does not appear to expressly teach determining the centrality of the target entity based on the entity corresponding to the neighboring node of the node corresponding to the target entity in the first knowledge graph within the preset adjacency range comprises: determining a single centrality of the target entity based on an out-degree of the target entity.
Berrendorf teaches determining the centrality of the target entity based on the entity corresponding to the neighboring node of the node corresponding to the target entity in the first knowledge graph within the preset adjacency range comprises: determining a single centrality of the target entity based on an out-degree of the target entity (Page 52, section entitled "Node Centrality").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the Active Learning for Neural Entity Alignment of Liu to include the Active Learning for Entity Alignment techniques of Berrendorf to increase labeling efficiency (see Berrendorf at abstract).
Liu does not appear to expressly teach determining a single centrality of the entity corresponding to the neighboring node based on an out-degree of the entity corresponding to the neighboring node of the node corresponding to the target entity in the first knowledge graph within the preset adjacency range; and determining the centrality of the target entity based on the single centrality of the target entity and the single centrality of the entity corresponding to the neighboring node.
Cai teaches determining a single centrality of the entity corresponding to the neighboring node based on an out-degree of the entity corresponding to the neighboring node of the node corresponding to the target entity in the first knowledge graph within the preset adjacency range; and determining the centrality of the target entity based on the single centrality of the target entity and the single centrality of the entity corresponding to the neighboring node (Page 4, "we adopt PageRank centrality to calculate centrality").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the Active Learning for Neural Entity Alignment of Liu to include the Active Learning for Graph Embedding techniques of Cai such to select the subset of training data to label so as to maximize the graph analysis task performance (see Cai at abstract).
Subject Matter Allowable over the Prior Art
Claims 4-8, 11, 15-17, 21, and 22 are allowable over the prior art and would be allowed if they were to overcome the outstanding 101 rejections.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhang Ziheng et al. (Chinese. Pat. App. Pub. No. CN-112966124-A) teaches acquiring a first entity pair set, wherein the first entity pair set comprises a plurality of first entity pairs which are not marked with alignment results; screening out a plurality of first candidate entity pairs from each first entity pair based on the predicted alignment probability of each first entity pair; calculating the alignment difficulty of each first candidate entity pair; screening out a plurality of first target entity pairs from each first candidate entity pair based on the alignment difficulty of each first candidate entity pair; and acquiring the label alignment result of each first target entity pair, and obtaining a target knowledge graph alignment model according to the predicted alignment probability and the label alignment result of each first target entity pair. Through twice screening, the number of entity pairs needing to be marked is greatly reduced, the time cost is saved, the training speed of the model is improved, and the alignment efficiency is improved.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Casey R. Garner/Primary Examiner, Art Unit 2123
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