DETAILED ACTION
This action is responsive to the following communication: Amendment filed Aug. 10, 2026. This Action is made Non-Final.
Claims 1-20 are pending in the case. Claims 1, 15 and 19 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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “encoding an in-distribution dataset having a plurality of in-distribution components, and an out-of-distribution dataset having a plurality of in-distribution components, wherein said encoding is done using a foundation model; pairing an in-distribution component with an out-of-distribution component in a same class and using a contrastive model to provide a first set of paired components” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “pairing another in-distribution component with another out-of-distribution component in a different class using said contrastive learning model to provide a second set of paired components; augmenting said first and second set of paired components to generate an augmented training dataset; and adjusting said foundation model by using said augmented training dataset to train said Al engine” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “an artificial intelligence (AI) engine” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “an artificial intelligence (AI) engine “ is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 15:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “encoding an in-distribution dataset having a plurality of in-distribution components, and an out-of-distribution dataset having a plurality of in-distribution components, wherein said encoding is done using a foundation model; pairing an in-distribution component with an out-of-distribution component in a same class and using a contrastive model to provide a first set of paired components” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “pairing another in-distribution component with another out-of-distribution component in a different class using said contrastive learning model to provide a second set of paired components; augmenting said first and second set of paired components to generate an augmented training dataset; and adjusting said foundation model by using said augmented training dataset to train said Al engine” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “A computer system for data augmentation for training an artificial intelligence (AI) engine, the computer system comprising: one or more processors, one or more computer-readable memories and one or more computer-readable storage media; program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(1), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “A computer system …“ is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 19:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “encoding an in-distribution dataset having a plurality of in-distribution components, and an out-of-distribution dataset having a plurality of in-distribution components, wherein said encoding is done using a foundation model; pairing an in-distribution component with an out-of-distribution component in a same class and using a contrastive model to provide a first set of paired components” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “pairing another in-distribution component with another out-of-distribution component in a different class using said contrastive learning model to provide a second set of paired components; augmenting said first and second set of paired components to generate an augmented training dataset; and adjusting said foundation model by using said augmented training dataset to train said Al engine” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “A computer program product for data augmentation used for training an artificial intelligence (AI) engine, the computer program product comprising: one or more computer readable storage media;program instructions, stored on at least one of the one or more storage media” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(1), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “A computer program … “ is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
Claims 2-14, 16-18 and 20 are dependent claims. These claims recite additional limitations, but do not otherwise add any meaningful limits beyond the abstract idea.
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 Kung et al. (hereinafter Kung) “Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity” 2021 in view of Krishnan et al. (hereinafter Krishnan) U.S. Patent Pub. No. 2021/0326660.
With respect to independent claim 1, Kung teaches a method for data augmentation for training an artificial intelligence (AI) engine, comprising:
encoding an in-distribution dataset (see e.g., Page 419-420 – The primary task data corresponds to the “in-distribution” dataset.) having a plurality of in-distribution components, and an out-of-distribution dataset (see e.g., Page 419-420 – The auxiliary dataset corresponds to the “out-of-distribution” dataset.) having a plurality of in-distribution components, wherein said encoding is done using a foundation model (see e.g., Page 420 Sect. 4.2– “For all experiments, we use BERT-Base as the back bone structure and add a linear layer for each task oriented predictor or a task-discriminator.”);
augmenting said first and second set of paired components to generate an augmented training dataset (see e.g., Page 419 – “As illustrated in Figure2,we can easily rank all auxiliary data samples by their similarity to the primary task, and the top-ranked data samples are selected as DA best, the subset of auxiliary data that can benefit the primary task most.”); and adjusting said foundation model by using said augmented training dataset to train said AI engine (see e.g., Page 420 Sect. 3.3 – “After training TO-MTDNN, we further fine-tune the model on Dp to boost the performance of the primary task.”).
Kung does not expressly show the feature discussed below. However, Krishnan teaches pairing an in-distribution component with an out-of-distribution component in a same class and using a contrastive model to provide a first set of paired components (see e.g., Para [7] and Claim 1 – “The operations include obtaining an anchor image associated with a first class of a plurality of classes, a plurality of positive images associated with the first class, and one or more negative images associated with one or more other classes of the plurality of classes, the one or more other classes being different from the first class … The operations include evaluating a loss function that evaluates a similarity metric between the anchor projected representation and each of the plurality of positive projected representations and each of the one or more negative projected representations.”); pairing another in-distribution component with another out-of-distribution component in a different class using said contrastive model to provide a second set of paired components (see e.g., Para [20] – “By enabling contrastive learning to occur simultaneously across both multiple positive training examples from the same class and multiple negative training examples from other classes, whole clusters of points belonging to the same class can be pulled together in embedding space, while clusters of samples from different classes are simultaneously pushed apart. ”). Both Kung and Krishnan are directed to machine learning training methods. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Kung and Krishnan in front of them to modify the system of Kung to include the above feature. The motivation to combine Kung and Krishnan comes from Krishnan. Krishnan discloses the motivation to perform supervised contrastive learning to improve training performance (see e.g. para [2]-[9]). This motivation for combination also applies to the remaining claims which depend on this combination.
With respect to dependent claim 2, the modified Kung teaches said adjusting comprises tuning and reiteratively fine-tuning said foundation model (see e.g., Page 419-420 Sect. 3.3 – “After training TO-MTDNN, we further fine-tune the model on Dp to boost the performance of the primary task.” Tuning is performed repeatedly.).
With respect to dependent claim 3, the modified Kung teaches said in-distribution dataset is a labeled training dataset designated for a target task (see e.g., Page 419-420 Sect. 3.2 – “In multi-task auxiliary learning, Tp denotes a primary task with training dataDp and N auxiliary tasks TAi,i∈{1,2,...,N}with training data DAi, i∈ {1,2,...,N}.”).
With respect to dependent claim 4, the modified Kung teaches said out-of-distribution dataset is a quasi-labeled dataset designated for a similar target task (see e.g., Page 419 – “As illustrated in Figure2,we can easily rank all auxiliary data samples by their similarity to the primary task, and the top-ranked data samples are selected as DA best, the subset of auxiliary data that can benefit the primary task most.”).
With respect to dependent claim 5, the modified Kung teaches said in-distribution component and said out-of-distribution component are an in-distribution sentence and an out-of-distribution sentence respectively (see e.g., Page 420 Sect. 4.1 – “we used eight datasets (MNLI, RTE, MRPC, STS-B, QQP, QNLI, SST-2, CoLA) from GLUE (General Language Understanding Evaluation Benchmark) (Wang et al., 2018) in our experiment.”).
With respect to dependent claim 6, the modified Kung teaches said adjusting is used for training said foundation model (see e.g., Page 420 Sect. 3.3 – “After training TO-MTDNN, we further fine-tune the model on Dp to boost the performance of the primary task.”).
With respect to dependent claim 7, the modified Kung teaches training said AI engine is performed through a two-stage training process (see e.g., Page 419 Sect. 3 – “we extend the original MT-DNN training process to a two-stage multi-task auxiliary learning pipe line illustrated in Figure2”).
With respect to dependent claim 8, the modified Kung teaches said two stage training process comprises a first training stage that is performed using an augmented dataset and a second training stage that is performed using said in-distribution dataset (see e.g., Page 420 Sect. 3.3 – “After training TO-MTDNN, we further fine-tune the model on Dp to boost the performance of the primary task”).
With respect to dependent claim 9, the modified Kung teaches said contrastive learning model is used to maximize a cosine similarity between a plurality of embeddings of sentences of the two datasets from said same class (see e.g., Krishnan Para [7] and Claim 1 – “The operations include obtaining an anchor image associated with a first class of a plurality of classes, a plurality of positive images associated with the first class, and one or more negative images associated with one or more other classes of the plurality of classes, the one or more other classes being different from the first class … The operations include evaluating a loss function that evaluates a similarity metric between the anchor projected representation and each of the plurality of positive projected representations and each of the one or more negative projected representations.”).
With respect to dependent claim 10, the modified Kung teaches for each sentence in said out-of-distribution dataset, computing a cosine similarity with each sentence in said same class in said in-distribution dataset; and providing a score based on a level of similarity (see e.g., Page 419 Sect. 3.2 – “each element indicates how much similar to a task and can be viewed as the similarity to a task for the input data”).
With respect to dependent claim 11, the modified Kung teaches said scores indicating similarities based on similarity of each sentence in said out-of-distribution dataset (see e.g., Page 419 Sect. 3.2 – all auxiliary samples are ranked by similarity to the primary task).
With respect to dependent claim 12, the modified Kung teaches selecting a score with a highest similarity score amongst said out-of-distribution dataset (see e.g., Page 419 Sect. 3.2 – top ranked data samples are selected).
With respect to dependent claim 13, the modified Kung teaches selecting a sentence from said out-of-distribution dataset for classes with fewer than k sentences in said in-distribution dataset (see e.g., Page 419 Sect. 3.2 – Kung does not expressly show this feature. However, it would have been obvious because Kung teaches top ranked data samples are selected).
With respect to dependent claim 14, the modified Kung teaches appending selected sentences to a training dataset (see e.g., Kung Sect. 3.3 – selected auxiliary data can be added/appended to primary data for training).
Claim 15 is rejected for the similar reasons discussed above with respect to claim 1.
With respect to dependent claim 16, the modified Kung teaches said adjusting comprises tuning and reiteratively fine-tuning said foundation model (see e.g., Page 420 Sect. 3.3 – “After training TO-MTDNN, we further fine-tune the model on Dp to boost the performance of the primary task.”)..
With respect to dependent claim 17, the modified Kung teaches said in-distribution dataset is a labeled training dataset designated for a target task (see e.g., Sect. 3.2-3.3 ).
With respect to dependent claim 18, the modified Kung teaches said another in-distribution component or said another out-of-distribution used to provide said second set of paired component may be the same as said in distribution component or said out- of-distribution component used to provide said first set of paired components (see e.g., Para [7] and Claim 1 – contrastive learning across anchor, positives, and negatives in a batch; same anchor can be compared to positives and negatives via similarity metric).
Claim 19 is rejected for the similar reasons discussed above with respect to claim 1.
With respect to dependent claim 20, the modified Kung teaches said adjusting comprises tuning and fine turning said foundation model (see e.g., Page 420 Sect. 3.3 – “After training TO-MTDNN, we further fine-tune the model on Dp to boost the performance of the primary task.”).
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. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://portal.uspto.gov/external/portal. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/PEI YONG WENG/Primary Examiner, Art Unit 2141
Prosecution Insights