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 communications: RCE filed on 12/03/2025.
Claims 1-5, 9-13, and 16-19 are pending. Claims 1, 10 and 16 are independent. Claim 7 is newly canceled.
The previous rejection of claims 1-5, 9-13, and 16-19 under 35 USC § 103 have been withdrawn in view of the amendment.
Claim Rejections - 35 USC § 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.
Claim(s) 1-5, 9-13, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US2022/0237520) in view of McGrath et al. (US2022/0129794) and Chen et al. (US2021/0056127).
In regards to claim 1, Wang et al. discloses a computer program product for facilitating processing within a computing environment, the computer program product comprising:
one or more computer-readable storage media and program instructions embodied therewith (Wang et al. para[0025]), the program instructions being readable by a processing circuit to cause the processing circuit to perform a method comprising:
obtaining training data for use in training a machine learning model, the training data comprising factual data related to a particular application (Wang et al. fig. 1B para[0054], obtains training data (158) for training machine learning model (160)); and
training the machine learning model using the training data and the imputed counterfactual data to facilitate generating machine learning model predictions for the particular application in accordance with the one or more application domain properties (Wang et al. fig. 2 para[0069], trains machine learning model to generate updated prediction model).
Wang et al. does not explicitly disclose obtaining, with reference to the training data, unlabeled counterfactual data for the particular application;
imputing one or more labels to the unlabeled counterfactual data using domain knowledge for the particular application, to obtain imputed counterfactual data, the domain knowledge including one or more application domain properties;
wherein the imputing comprises iteratively re-imputing one or more labels to the counterfactual data and updating the machine learning model using the re-imputed labels until convergence to simulate a randomized control trial in agreement with one or more monotonic-type properties.
However McGrath et al. discloses obtaining, with reference to the training data, unlabeled counterfactual data for the particular application (McGrath et al. para[0031], classification model identifies a cluster of unlabeled counter factual explanations);
imputing one or more labels to the unlabeled data using annotators domain knowledge for the particular application, to obtain imputed counterfactual data(McGrath para[0036], generates and stores counterfactual explanation with a label determined from feedback, as a labeled counterfactual explanation);
wherein the imputing comprises iteratively re-imputing one or more labels to the counterfactual data and updating the machine learning model using the re-imputed labels until convergence to simulate a randomized control trial in agreement with one or more monotonic-type properties (McGrath et al. para[0040], For example the automated analysis system , during a training period, may iteratively receive pairs of user information and a prediction output of the qualification model, iteratively select one or more relevant counter factual explanations, iteratively provide the selected counterfactual explanations for feedback, iteratively update labels based on iteratively received feedback data, and iteratively retrain the clustering model and/or the classification model according to the feedback data).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the data augmentation of Wang et al. with the counterfactual generation method of McGrath et al. in order to reduce the quantity of feedback need to improve predictions (McGrath et al. para[0014]).
Wang et al. does not explicitly disclose imputing one or more labels to the unlabeled data using domain knowledge for the particular application, to obtain imputed data, the domain knowledge including one or more application domain properties;
wherein the updating of the machine learning model using the re-imputed labels facilitates reducing prediction error of the machine learning model contrary to the one or more monotonic-type properties.
However Chen et al. discloses imputing one or more labels to the unlabeled data using domain knowledge for the particular application, to obtain imputed data, the domain knowledge including one or more application domain properties (Chen et al. para[0041], relation labels assigned based on domain knowledge, labels define relation between examples as well as counterparts in the opposite modality);
wherein the updating of the machine learning model using the re-imputed labels facilitates reducing prediction error of the machine learning model contrary to the one or more monotonic-type properties (Chen et al. fig. 3 para[0060], updates parameters before applying labels to a new selected pairs to improve encoders).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the data augmentation method of Wang et al. with the classification method of Chen et al. in order to make use of multi-modal data to predict future states (Chen et al. para[0004]).
In regards to claim 2, Wang et al. as modified by McGrath and Chen et al. discloses the computer program product of claim 1, wherein imputing one or more labels to the unlabeled counterfactual data using the domain knowledge, and training the machine learning model using, in part, the imputed counterfactual data, facilitates integrating the one or more monotonic-type properties into the machine learning model (Wang et al. para[0146], integrates monotonic constraints on model inputs).
In regards to claim 3, Wang et al. as modified by McGrath and Chen et al. discloses the computer program product of claim 2, further comprising:
providing the trained machine learning model to an artificial intelligence explainability model to generate explanations for the imputed one or more labels to the unlabeled counterfactual data (McGrath et al. para[0079], system may determine based on generator model a plurality of explanations associated with the prediction); and
receiving an evaluation of the imputing obtained by checking the generated explanations for the imputing of the one or more labels to confirm presence of the one or more application domain properties (McGrath et al. para[0081], selects counterfactual explanation based on relevance score).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the data augmentation of Wang et al. with the counterfactual generation method of McGrath et al. in order to reduce the quantity of feedback need to improve predictions (McGrath et al. para[0014]).
In regards to claim 4, Wang et al. as modified by McGrath and Chen et al. discloses the computer program product of claim 2, further comprising using the trained machine learning model to provide a prediction for the particular application in accordance with the one or more monotonic-type properties (Wang et al. para[0079], generate a prediction model to provide labels for unknown data).
In regards to claim 5, Wang et al. as modified by McGrath and Chen et al. discloses the computer program product of claim 2, wherein training the machine learning model using the training data and the imputed counterfactual data comprises:
training the machine learning model using the training data (McGrath et al. para[0040], training model during a training period); and
updating the machine learning model using the imputed counterfactual data (McGrath et al. para[0040], iteratively train model with labeled counter factual explanations).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the data augmentation of Wang et al. with the counterfactual generation method of McGrath et al. in order to reduce the quantity of feedback need to improve predictions (McGrath et al. para[0014]).
In regards to claim 9, Wang et al. as modified by McGrath and Chen et al. discloses the computer program product of claim 2, wherein the trained machine learning model for the particular application comprises discrete actions and discrete outcomes (Wang para[0117], The output of the credit determination application (618) is used to create a credit decision (620), which may include the terms of the loan, reasons for rejection of the credit application, etc.) .
Claims 10-13 recite substantially similar limitations to claims 1-3, and 5. Thus claims 10-13 are rejected along the same rationale as claims 1-3, and 5.
Claims 16-19 recite substantially similar limitations to claims 1-3, and 5. Thus claims 16-19 are rejected along the same rationale as claims 1-3, and 5.
Response to Arguments
Applicant’s arguments with respect to claims 1-5, 9-13, and 16-20 have been considered but are moot because the arguments do not apply the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tang et al. (US11,069,352) teaches iteratively imputing missing labels on training data.
Convolbo (US2021/0201177) teaches assigning labels based on domain knowledge.
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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