DETAILED ACTION
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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shim et al. (US 2024/0303436 A1), hereinafter “Shim”, and in view of Gupta et al. (US 2023/0196011 A1), hereinafter “Gupta”.
As per claim 1, Shim teaches a method for extracting relations from text data comprising:
“collecting labeled text data from diverse sources, including digital archives and online repositories, each source including sentences annotated with detailed grammatical structures” at [0028]-[0035];
(Shim teaches receiving human annotated data and distantly supervised data. The data includes sentences annotated with grammatical structure such as subject, predicative, object, complement, noun)
“systematically generating initial relational data from the grammatical structures by applying advanced parsing and machine learning techniques using sophisticated rule-based algorithm” at [0036]-[0044], [0056]-[0057];
(Shim teaches the relation extraction apparatus 100 receives the input text from a plurality of terminals or other servers and infers the relation information included in the input text by executing the relation extraction model. The relation extraction apparatus 100 outputs a relation extraction result in a formal form of triple <eh, r, et> including an entity pair including a head entity <eh> and a tail entity <et> extracted from the input text IN_TXT, and relation information <r> included in the entity pair. For example, the head entity <eh> corresponds to the subject of the sentence and the tail entity <et> corresponds to the object of the sentence. For example, the head entity <eh> corresponds to a first noun of the sentence and the tail entity <et> corresponds to a last noun of the sentence. For example, the relation information <r> may be associated with the predicative of the sentence)
“training a neural network model using a comprehensive array of semantically equivalent but syntactically varied prompt templates designed to test and refined linguistic capabilities of a model” at [0079][0080];
(Shim teaches the relation extraction model separately trains the first relation network HA-Net using the human annotated training data for training and the second relation prediction network DS-Net using distantly supervised training data for training)
“determining a final relation extraction output by implement a vote-based decision system integrating statistical analysis and utilizing a weighted voting mechanism to optimize extraction accuracy and reliability” at [0081].
(Shim teaches after the training is completed, obtaining the outputs of the first relation prediction network HA-Net and the second relation prediction network DS-Net. Determining the difference between prediction results of the first and second relation prediction network HA-Net and DS-Net to adjust a degree (i.e., weight) of using different types of training data for training the relation extraction model to optimize extraction accuracy and reliability of the relation extraction model)
Shim does not explicitly teach “generating training sets for enhancing the diversity and complexity of a relation dataset by applying any of a plurality of data augmentation techniques to the initial relational data” as claimed. However, Gupta teaches a method for data augmentation for sentence level relation extraction including the step of “generating training sets for enhancing the diversity and complexity of a relation dataset by applying any of a plurality of data augmentation techniques to the initial relational data” at [0006], [0017]-[0020] and Fig. In particular, Gupta teaches a method for training a relation extraction model using data augmentation of training data including receiving an original labeled sentence as input, the label sentence including entities and at least one relation. A dependency parsing process is used on the labeled sentence to generate first augment training data. A constituency parsing process is used on the labeled sentence to generate second augmented training data. A scoring function is used to order a training set based on difficulty. The training set includes the original leveled sentence, the first augmented training data, and the second augmented training data. A curriculum learning process is then used to train the relation extraction model by feeding the scored training set to the machine learnable model. Thus, it would have been obvious to an ordinary skill in the art to combine Gupta with Shim’s teaching in order to increase the amount of samples in a training data set using data augmentation methods as taught by Gupta , which “are intended to satisfy two constraint: (1) preserving the entity mentions and (2) preserving the relationship between them”, as suggested by Gupta at [0016].
As per claim 2, Shim and Gupta teach the method of claim 1 discussed above. Shim also teaches: “wherein the collecting of labeled text data includes preprocessing operations to remove noise and transform the data to standardized formats across different text sources” at [0035]-[0038], [0165]-[0168].
As per claim 3, Shim and Gupta teach the method of claim 1 discussed above. Shim also teaches: “integrating the extracted relations into enterprise data management systems to provide automated data retrieval and enhance functionalities including search accuracy and content recommendations within corporate databases and Enterprise Resource Planning systems for improved operational efficiency and data utilization based on the derived relational insights” at [0165]-[0168].
As per claim 4, Shim and Gupta teach the method of claim 1 discussed above. Shim also teaches: wherein “the generation of training sets includes using machine learning models to automatically determine an optimal mix of synthetic and adversarial examples to archive maximum model robustness against unseen data” at [0097]-[0127].
As per claim 5, Shim and Gupta teach the method of claim 1 discussed above. Shim also teaches: wherein “training the neural network model further includes performing multiple iterations of training cycles, each followed by an evaluation phase wherein model adjustment are made based on performance metrics such as accuracy and loss reduction” at [0129]-[0159].
As per claim 6, Shim and Gupta teach the method of claim 1 discussed above. Shim also teaches: wherein “determining the final relation extraction output includes applying ensemble learning techniques in which multiple model predictions are combined to reduce variance and improve decision accuracy” at [0013]-[0014].
As per claim 7, Shim and Gupta teach the method of claim 1 discussed above. Shim also teaches: “integrating and utilizing the extracted relations to automatically tag and categorize new incoming text data to enhance data accessibility and retrievability in comparatively large-scale information systems” at [0165]-[0168].
Claims 8-20 recite similar limitations as claims 1-7 and are therefore rejected by the same reasons.
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
July 16, 2026
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