DETAILED ACTION
This action is in response to the RCE filed 27 July 2026.
Claims 1–11 are pending. Claims 1 and 9 are independent.
Claims 1–11 are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Continued Examination
A request for continued examination under 37 C.F.R. § 1.114, including the fee set forth in 37 C.F.R. § 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 C.F.R. § 1.114, and the fee set forth in 37 C.F.R. § 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 C.F.R. § 1.114. Applicant's submission filed on 27 July 2026 has been entered.
Response to Arguments
Applicant's arguments, see remarks, filed 27 July 2026, with respect to the rejection(s) of claim(s) 1–11 under § 103 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kim et al.
Claim Rejections—35 U.S.C. § 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.
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 is 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 1–11 are rejected under 35 U.S.C. § 103 as being unpatentable over Costabello et al. (US 2020/0356874 A1) [hereinafter Costabello] in view of Kim et al. (“Fact Checking in Knowledge Graphs by Logical Consistency”) [hereinafter Kim] and Song et al. (US 2020/0175406 A1) [hereinafter Song]
Regarding independent claim 1, Costabello teaches [a] computer implemented method for training a machine learning model, comprising the following operations, wherein the operations are performed by components, and wherein the components are software components executed by one or more processors and/or hardware components: System circuitry including, e.g., processors and memory (Costabello, ¶ 55). training, by a training module, a machine learning model based on known facts; A knowledge graph is used as a training dataset for an embeddings generator [machine learning model] (Costabello, ¶¶ 17–20, 48). generating, by an active learning module using the machine learning model, candidate triples; The embeddings generator selects a set of candidate facts (Costabello, ¶¶ 25–27). The facts in the knowledge graph and the candidate facts are triples (Costabello, ¶¶ 18, 33). […] outputting, by the expert system, novel facts, with the novel facts representing the result of the verification, […]; and The candidate facts are analyzed to detect their level of truthfulness and surprise, and a new surprising fact is selected and added [output] to the knowledge graph (Costabello, ¶¶ 49–51). […]
Costabello teaches determining a truthfulness of the candidate facts, but does not expressly teach using an expert system (Costabello, ¶ 49). However, Kim teaches: reasoning, by an expert system containing an inference engine and logical rules, in order to verify the candidate triples by processing the candidate triples and the logical rules, wherein the expert system acts as an oracle for the machine learning model; A system for fact checking [verifying] comprising logical rules that are generated and subsequently applied to fact triples (Kim, pp. 10–11, § 3.6, p. 7, fig. 2). [Kim does not explicitly use the terms “inference engine” or “oracle”, however, the plain and ordinary meaning of the term “inference engine” is a software component that applies logical rules to a knowledge base; furthermore, the meaning of an “oracle” is a source of correct information, i.e., the fact checking system acts as an “oracle”.] wherein each candidate triple is output together with a true, false, or unknown value A truth score is output ranging from 0.0 (false) to 1.0 (true) [therefore scores between 0.0 and 1.0 are “unknown”] (Kim, p. 5, § 3.1).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Costabello with those of Kim. One would have been motivated to do so in order to more accurately assess the truth of the candidate triples (Kim, p. 1, § 1).
Costabello/Kim teaches generating novel facts using a ML model, but does not expressly teach retraining the model based on the truth of the generated facts. However, Song teaches: retraining, by the training module, the machine learning model by processing the novel facts output by the expert system. A Bayesian Program Learning (BPL) model generates beliefs or hypotheses [novel facts] which are verified using an auditor module; the results of the auditor module are used to retrain the BPL model (Song, ¶¶ 39, 41).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Costabello/Kim with those of Song. One would have been motivated to do so in order to improve the accuracy and reliability of the system (Song, ¶ 39).
Regarding dependent claim 2, the rejection of claim 1 is incorporated and Costabello/Kim/Song further teaches: wherein the active learning module generates candidate triples that lie close to a decision boundary of the machine learning model, by: using uncertainty sampling; using Bayesian optimization; or A method including identifying facts based on a knowledge graph data structure, and generating hypotheses from the graph, using a Bayesian learning model (Song, ¶ 7). The model is improved [optimized] based on the new fact/hypothesis (Song, ¶¶ 17, 59). learning a selection policy using reinforcement learning.
Regarding dependent claim 3, the rejection of claim 1 is incorporated and Costabello/Kim/Song further teaches: wherein the generating operation includes at least: calculating, by the machine learning model, a calibrated score for each unknown fact of a set of unknown facts, wherein the calibrated score reflects a predicted truth value of the respective unknown fact; and A level of truthfulness is determined for the candidate facts (Costabello, ¶ 49). determining, by a query strategy module, the candidate triples by processing the set of unknown facts, parameters of the machine learning model and the calibrated scores, and the known facts. The BPL model generates new beliefs/hypotheses using a knowledge graph including existing facts [known facts], a set of parameters, and training data from verification of previously generated beliefs/hypotheses (Song, ¶ 7, 17, 24–28, fig. 2).
Regarding dependent claim 4, the rejection of claim 3 is incorporated and Costabello/Kim/Song further teaches: wherein a calibration of the calibrated scores is implemented with: a probabilistic treatment with Bayesian neural networks, or a post processing step. The truth scores are calculated after the implication checking using the rules (Kim, p. 7, fig. 2).
Regarding dependent claim 5, the rejection of claim 1 is incorporated and Costabello/Kim/Song further teaches: wherein the logical rules comprise a set of logical rules used by the inference engine to infer the novel facts. The logical rules are applied to the fact triples to determine the truth of the facts (Kim, p. 7, fig. 2).
Regarding dependent claim 6, the rejection of claim 1 is incorporated and Costabello/Kim/Song further teaches: wherein the training and retraining operations include optimizing, by the training module, parameters of the machine learning model with respect to a loss function, with the loss function describing an accuracy of the calibrated scores computed by the machine learning model. A loss function is used to optimize, e.g., the surprise score, which is used when determining which candidate triples to add to the knowledge graph (Costabello, ¶¶ 23–24).
Regarding dependent claim 7, the rejection of claim 1 is incorporated and Costabello/Kim/Song further teaches: wherein the machine learning model is implemented as a graph neural network, or as a knowledge graph embedding algorithm capable of producing the calibrated scores. The knowledge graph is input to a knowledge graph embedding circuitry [embedding algorithm] (Costabello, ¶¶ 10–12, 19–22).
Regarding dependent claim 8, the rejection of claim 1 is incorporated and Costabello/Kim/Song further teaches: wherein the steps of the method are iterated in an active learning loop. The validation results are used to further train the BPL models (Song, ¶ 28). The auditing/feedback/model updating steps form a loop (Song, ¶ 40, fig. 2).
Regarding independent claim 9, Costabello teaches [a] system for training a machine learning model, comprising: a memory storing known facts; and A computer device including data sources [memory] storing the knowledge graph (Costabello, ¶ 57). a training module, configured for training a machine learning model based on the known facts; A knowledge graph is used as a training dataset for an embeddings generator [machine learning model] (Costabello, ¶¶ 17–20, 48). an active learning module, configured for generating candidate triples using the machine learning model; The embeddings generator selects a set of candidate facts (Costabello, ¶¶ 25–27). The facts in the knowledge graph and the candidate facts are triples (Costabello, ¶¶ 18, 33). […] and outputting novel facts, with the novel facts representing the result of the verification, […]; and The candidate facts are analyzed to detect their level of truthfulness and surprise, and a new surprising fact is selected and added [output] to the knowledge graph (Costabello, ¶¶ 49–51). […]
Costabello teaches determining a truthfulness of the candidate facts, but does not expressly teach using an expert system (Costabello, ¶ 49). However, Kim teaches: an expert system containing an inference engine and logical rules, configured for verifying the candidate triples by processing the candidate triples and the logical rules using reasoning, […] wherein each candidate triple is output together with a true, false, or unknown value, wherein the expert system acts as an oracle for the machine learning model A system for fact checking [verifying] comprising logical rules that are generated and subsequently applied to fact triples (Kim, pp. 10–11, § 3.6, p. 7, fig. 2). [Kim does not explicitly use the terms “inference engine” or “oracle”, however, the plain and ordinary meaning of the term “inference engine” is a software component that applies logical rules to a knowledge base; furthermore, the meaning of an “oracle” is a source of correct information, i.e., the fact checking system acts as an “oracle”.] A truth score is output ranging from 0.0 (false) to 1.0 (true) [therefore scores between 0.0 and 1.0 are “unknown”] (Kim, p. 5, § 3.1).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Costabello with those of Kim. One would have been motivated to do so in order to more accurately assess the truth of the candidate triples (Kim, p. 1, § 1).
Costabello/Kim teaches generating novel facts using a ML model, but does not expressly teach retraining the model based on the truth of the generated facts. However, Song teaches: wherein the training module is also configured for retraining the machine learning model by processing the novel facts output by the expert system. A Bayesian Program Learning (BPL) model generates beliefs or hypotheses [novel facts] which are verified using an auditor module; the results of the auditor module are used to retrain the BPL model (Song, ¶¶ 39, 41).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Costabello/Kim with those of Song. One would have been motivated to do so in order to improve the accuracy and reliability of the system (Song, ¶ 39).
Regarding dependent claim 10, Costabello/Kim/Song teaches [a] computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method method according to claim 1. A storage medium that stores instructions [for performing the invention] (Costabello, ¶ 12).
Regarding dependent claim 11, Costabello/Kim/Song teaches [a] provision device for the computer program product according to claim 1, wherein the provision device stores and/or provides the computer program product. A medium storing code representing instructions to be executed by a processor (Song, ¶ 17).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
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/Tyler Schallhorn/Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
