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, 3-4, 6, 11, 13-14, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Luong et al. (US20220083840A1, hereinafter referred to as Luong) in view of Forest et al. (Interpretable Prognostics with Concept Bottleneck Models, hereinafter referred to as Forest).
Regarding claim 1, Luong a concept bottleneck model training apparatus comprising: a memory in which a training program for identifying concepts that degrade the performance of a concept bottleneck model and training the concept bottleneck model is stored; and a processor configured to execute the training program, wherein the training program: receives a training image set, trains a first concept bottleneck model to infer at least one class using the training image set (met by teacher model is not noised during the generation of the pseudo labels), trains a second concept bottleneck model by applying a predetermined ratio of noise to the training image set (met by student model is noised during its training/learning process), evaluates the first concept bottleneck model and the second concept bottleneck model using a test image set, and selects concepts that degrade the prediction accuracy of the concept bottleneck model for a specific class using weight information for each concept included in the evaluation results (met by using a noisy student to emphasize the role that noise plays in providing robust methods for training models that can achieve more accurate results), and wherein the training image set includes. This is read in (Paragraph [0032]-[0033]).
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Luong fails to teach a target label representing a specific class and a concept label corresponding to the target label. However, Forest amends this deficiency.
Forest teaches a concept bottleneck model as well as a label representing a specific class (image of an apple) and a concept label corresponding to the target label (concepts such as shape (”round”) and color (”red”)). This is read in (Page 4, Paragraph 2).
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Therefore, it would have been prima facia obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Luong to incorporate the teachings of Forest in order to provide the application of Concept Bottleneck Models (CBMs) (Abstract).
Regarding claim 3, Forest as read in the rejection of claim 1, incorporated herein, meets wherein the target label includes target information for any one of a plurality of classes (met by image of an apple), and the concept label includes a plurality of concept information corresponding to a specific class (met by concepts such as shape (”round”) and color (”red”)).
Regarding claim 4, Luong as read in the rejection of claim 1, incorporated herein meets wherein the training program: inputs the test image set into the first concept bottleneck model, calculates the prediction accuracy of the first concept bottleneck model for each class included in the test image set, and generates a first weight list for each class, and wherein the first weight list includes: the plurality of concepts inferred by the first concept bottleneck model for the test images and the weights assigned to each concept when predicting the test images as a specific class (met by injection of the noise component forces the noised student model to learn attributes of data items in a manner that is harder or more difficult).
Further, see (Paragraph [0086]).
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Regarding claim 6, Luong teaches wherein the training program generates a noisy image set by applying a predetermined ratio of noise to the concept labels defined in the training image set and trains the second concept bottleneck model using the noisy image set (met by system (100) is configured to add noise to the second machine-learning model during the training of the second machine-learning model (208); further met by modify attributes of data items (e.g., images)). This is read in (Paragraph [0054]).
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Regarding claim 11, the claim is substantially identical to claim 1, the analysis of which is incorporated herein.
Regarding claim 13, the claim is substantially identical to claim 3, the analysis of which is incorporated herein.
Regarding claim 14, the claim is substantially identical to claim 4, the analysis of which is incorporated herein.
Regarding claim 16, the claim is substantially identical to claim 6, the analysis of which is incorporated herein.
Allowable Subject Matter
Claims 2, 5, 7-10, 12, 15, 17-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 2 recites wherein the training program trains the concept bottleneck model using the concepts that degrade the prediction accuracy for each class and the Sharpness-Aware Minimization (SAM) optimization algorithm. The prior art of reference fails to meet this feature.
Claim 5 recites wherein the training program extracts the top n concepts with the highest weights from the first weight list for each class to generate a first key concept list for each class. The prior art of reference fails to meet this feature.
Claim 7 recites wherein the training program: inputs the test image set into the second concept bottleneck model, calculates the concept accuracy and prediction accuracy of the second concept bottleneck model for each class included in the test image set, and generates a second weight list for each class, and wherein the second weight list includes: the plurality of concepts inferred by the second concept bottleneck model for the test images and the weights assigned to each concept when predicting the test images as a specific class. The prior art of reference fails to meet these features.
Claim 8 recites wherein the training program extracts the top n concepts with the highest weights from the second weight list for each class to generate a second key concept list for each class. The prior art of reference fails to meet this feature.
Claim 9 recites wherein the training program analyzes the impact caused by the concepts corresponding to the noise based on changes in at least one of the prediction accuracy of the second concept bottleneck model, the second weight list, or the second key concept list, relative to the prediction accuracy, the first weight list, and the first key concept list of the first concept bottleneck model. The prior art of reference fails to meet these features.
Claim 10 recites wherein the training program analyzes concept accuracy change information for each concept, weight change information for each concept, and prediction accuracy change information for each class caused by the concepts corresponding to the noise, based on changes in the key concepts between the first key concept list and the second key concept list, and identifies the concepts that degrade the prediction accuracy for each class using at least one of the analyzed information. The prior art of reference fails to meet these features.
Claim 12 is substantially identical to claim 2, the analysis of which is incorporated herein.
Claim 15 is substantially identical to claim 5, the analysis of which is incorporated herein.
Claim 17 is substantially identical to claim 7, the analysis of which is incorporated herein.
Claim 18 is substantially identical to claim 8, the analysis of which is incorporated herein.
Claim 19 is substantially identical to claim 9, the analysis of which is incorporated herein.
Claim 20 is substantially identical to claim 10, the analysis of which is incorporated herein.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW JAMES BODNARK whose telephone number is (703)756-5378. The examiner can normally be reached 8a-5p.
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/MATTHEW JAMES BODNARK/Examiner, Art Unit 2668
/UTPAL D SHAH/Primary Examiner, Art Unit 2668
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