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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/02/2026 has been entered.
DETAILED ACTION
Claims 1, 3, 5-6, 8, 10, 12-13, 15, 18-19, and 21-29 are presented for examination in this application, 18/178,223, filed 06/02/2026, having an effective filing date of 2022-10-06 via provisional application 63/413,725.
The Examiner cites particular sections in the references as applied to the claims below for the convenience of the applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant(s) fully consider the references in their 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.
Response to Arguments
Applicant’s arguments and remarks filed 06/02/2026 have been fully considered. The arguments and remarks regarding the 35 U.S.C 101 rejections were found to be persuasive. Therefore, the 35 U.S.C 101 rejections have been overcome. The arguments and remarks regarding the 35 U.S.C 102(a)(1) and 35 U.S.C 103 rejections were found to be persuasive however the amendments have necessitated a change in the references applied resulting in a new grounds of rejection. The 35 U.S.C 102(a)(1) rejections have been overturned. The 35 U.S.C 103 rejections have been maintained.
35 U.S.C 103
Applicant’s response:
Applicant asserts “Accordingly, the combination of Wangenheim and Sharpe fails to teach or suggest every element of amended claim 1.Similar language is also included in amended claims 8 and 15. Thus, the combination of Wangenheim and Sharpe does not teach or suggest all the features of independent claims 1, 8, and 15 or their corresponding dependent claims 3 and 10. The cancelations of claims 2, 4, 7, 9, 11, 16, 17, and 20 render the rejections of those claims moot. Accordingly, Applicant respectfully requests that the rejections of claims 1-4, 7, 9- 11, 15-17, and 20 under 35 U.S.C. § 103 be withdrawn.
As discussed above, the combination of Wangenheim and Sharpe fails to teach or suggest all of the features of independent claims 1, 8, and 15. Jin fails to cure at least these deficiencies of independent claims 1, 8, and 15. Therefore, Applicant respectfully submits that claims 5, 6, 12, 13, 18, and 19 are patentable over the cited references at least by virtue of their respective dependencies from independent claims 1, 8, and 15. Accordingly, Applicant respectfully requests that the rejections of claims 5, 6, 12, 13, 18, and 19 under 35 U.S.C. § 103 be withdrawn.”.
Examiner’s response:
Arguments regarding the amended limitations have been fully considered but are moot in view of the new grounds of rejection.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 5, 8, 10, 12, 15, 18, and 21-29 are rejected under 35 U.S.C 103 as being unpatentable over Cataltepe (US20190279043A1 hereinafter, Cataltepe) in view of Guo et al. (US12045585B2 hereinafter, Guo) in further view of Daly et al. (US20220215285A1 hereinafter, Daly).
Regarding claim 1:
Cataltepe teaches a system comprising: a memory; and a processing device, operatively coupled to the memory, configured to (see para [0031]: “FIG. 2 illustrates a block diagram of an electronic device 200 that can implement one or more aspects of systems and methods for interactive video generation and rendering according to one embodiment of the invention. Instances of the electronic device 200 may include servers, e.g., servers 107-109, and client devices, e.g., client devices 102-106. In general, the electronic device 200 can include a processor/CPU 202, memory 230” ):
receive, from a client device via a user interface, input data comprising an initial version of a machine learning model trained to predict an activity class for activity recognition (see para [0144]: “Let F be the set of all features. Assume that a set of features Fm is or will be totally or mostly missing. Let Fr=F−Fm be the remaining features. First of all, for each model in the OMLE that uses features from Fm, we create a new fresh machine learning model initialized and trained with the features from Fr. In order to be able to get most use of the already trained models, we use an artificial instance generation mechanism and use the already existing models in OMLE as teachers to the new corresponding models (FIG. 5). The artificially generated input (F) is fed to the old model and the label is recorded (x′,r′). The Fr part of the features, together with the predicted label is fed as a training instance to each new corresponding model.”);
…
send, to the client device via the user interface, an explanation indicative of feature importance (see para [0062]: “Particularly, in some embodiments, the OMLS may communicate with, or may include an Online Explanation System (OES). The OES may be updated continuously, and may be used in providing instance level explanations and model level explanations to a user”. Also see para [0192]: “FIG. 8 shows the user being able to search for specific features or paths (patterns) in the explanation model.”);
…
and generate, based on the initial version of the machine learning mode and the user input relating to the explanation, an enhanced version of the machine learning model in accordance with operating mode (see para [0060]: “In some embodiments, users, such as experts or other decision-makers, are provided with a displayed interactive staging area. The staging area may be used to allow the decision-makers to interactively manipulate, filter, review and update models, for example, to increase model trustworthiness and accountability, or to compare models or model performances. In some embodiments, the OMLS or particular models may include alert modules designed to alert decision-makers to a need for their feedback and may prompt them with staged models.”).
Cataltepe does not explicitly teach the cause the user interface to operate in an operating mode of a plurality of operating modes for machine learning model building.
Guo, however, analogously teaches the cause the user interface to operate in an operating mode of a plurality of operating modes for machine learning model building (see col 1 lines 21-36: In general, one innovative aspect of the subject matter described in this specification can be implemented in a computer implemented method that includes providing a graphical user interface (GUI) for generating machine learning models; receiving, through the GUI, user selection of mode button displayed in the GUI, wherein the mode button, when selected, causes the GUI to display a first set of user selectable buttons that correspond to respective machine learning routines, and when not selected, causes the GUI to display a second set of user selectable buttons that correspond to respective machine learning sub-routines, wherein a machine learning routine comprises a respective plurality of connected machine learning sub-routines; in response to receiving the user selection of the mode button, displaying, in the GUI, the first set of user selectable buttons”.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Cataltepe and Guo before him or her, to modify the system of claim 1 to include attributes of wherein to the cause the user interface to operate in an operating mode of a plurality of operating modes for machine learning model building in order to improve the efficiency at which personalized models can be generated (see col 3 lines 38-43: “Therefore, the efficiency at which personalized models can be generated is improved. Furthermore, the computational efficiency of running a machine learning model can be improved, since users with experience of machine learning concepts have the possibility to streamline a provided machine learning routine.”).
Cataltepe does not explicitly teach to receive, from the client device via the user interface, user input relating to the explanation, wherein the user input relating to the explanation comprises a set of ground truth features, and wherein each ground truth feature of the set of ground truth features has a corresponding feature weight.
Daly, however, analogously teaches to receive, from the client device via the user interface, user input relating to the explanation, wherein the user input relating to the explanation comprises a set of ground truth features, and wherein each ground truth feature of the set of ground truth features has a corresponding feature weight (see para [0044]: “This framework produces a disjunctive normal form (DNF) representation of a logical formula to predict a class label for an instance, where the class label can be either the ground truth label or the label provided by an ML model. BRCG can be used for binary classification models, and it can also be generalized to multi-class problems using a one-vs.-rest configuration. The system and method disclosed herein in one or more embodiments can use other explainer models, and in one or more embodiments, the explanations can be mapped to boolean rules.” … “By way of example, these two rule sets together form the Explainer Rule Set (ERS), which is used by the ML Layer 202. The feedback given by the users are stored as a feedback rule (FR). A feedback rule set (FRS) is the set of feedback rules that are stored in Feedback Rules LookUp Table 218.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Cataltepe, Guo, and Daly before him or her, to modify the system of claim 1 to include attributes of receiving, from the client device via the user interface, user input relating to the explanation, wherein the user input relating to the explanation comprises a set of ground truth features, and wherein each ground truth feature of the set of ground truth features has a corresponding feature weight in order to produce explanations to support the prediction from an ML model (see para [0044]: “To produce explanations to support the prediction from an ML model, the system and/or method in one or more embodiments can leverage a Boolean Rule Column Generation (BRCG) framework.”).
Regarding claim 8:
Claim 8 recites analogous limitations to claim 1 and therefore is rejected on the same grounds.
Regarding claim 15:
Claim 15 analogous limitations to claim 1 and therefore is rejected on the same grounds.
Claim 15 additionally includes a non-transitory computer-readable storage medium. Catalpe further teaches a non-transitory computer-readable storage medium (see para [0046]: “The computer readable media may be non-transitory”).
Regarding claim 3:
Cataltepe in view of Guo in further view of Daly teaches the system of claim 1.
Cataltepe further teaches wherein the explanation is a local interpretable model-agnostic explanation (LIME)-based explanation (see para [0175]: “In order to explain machine learning models, approaches such as LIME”).
Regarding claim 10:
Claim 10 recites analogous limitations to claim 3 and therefore is rejected on the same grounds as claim 3.
Regarding claim 5:
Cataltepe in view of Guo in further view of Daly teaches the system of claim 1.
Cataltepe further teaches wherein, to generate the enhanced version of the machine learning model, the processing device is further configured to implement incremental learning (see para [0091]: “The OMLS can be incrementally updated and hence can be up-to-date at any time t”).
Regarding claims 12 and 18:
Claims 12 and 18 recites analogous limitations to claim 5 and therefore is rejected on the same grounds as claim 5.
Regarding claim 21:
Cataltepe in view of Guo in further view of Daly teaches the system of claim 1.
Cataltepe further teaches wherein the explanation comprises a visual representation of one or more features considered by the machine learning model in making a prediction (see para [0190]: “The model level feedback screen (FIGS. 16-20) allows the domain expert to teach the machine learning models by updating the OEM. Through the visual display of the explanation model, such as for example sunburst as in FIGS. 16-20, the user is able to understand how the machine learning has decided on its predictions in different regions of the input space and also provide model level feedback.”).
Regarding claims 22 and 23:
Claims 22 and 23 recite analogous limitations to claim 21 and therefore is rejected on the same grounds as claim 21.
Regarding claim 24:
Cataltepe in view of Guo in further view of Daly teaches the system of claim 1.
Cataltepe further teaches wherein the explanation is generated by assigning, to one or more regions of an input signal, one or more respective feature weights (see para [0190]: “The model level feedback screen (FIGS. 16-20) allows the domain expert to teach the machine learning models by updating the OEM. Through the visual display of the explanation model, such as for example sunburst as in FIGS. 16-20, the user is able to understand how the machine learning has decided on its predictions in different regions of the input space and also provide model level feedback.”).
Regarding claims 25 and 26:
Claims 25 and 26 recite analogous limitations to claim 24 and therefore is rejected on the same grounds as claim 24.
Claims 6, 13, and 19 are rejected under 35 U.S.C 103 as being unpatentable over Cataltepe (US20190279043A1 hereinafter, Cataltepe) in view of Guo et al. (US12045585B2 hereinafter, Guo) in further view of Daly et al. (US20220215285A1 hereinafter, Daly) and further in view of Jin et al. (“Artificial intelligence in glioma imaging: challenges and advances” hereinafter, Jin).
Regarding claim 6:
Cataltepe in view of Guo in further view of Daly teaches the system of claim 1.
Cataltepe does not explicitly teach wherein the incremental learning is regularization-based elastic weight consolidation (EWC) incremental learning.
Jin, however, analogously teaches wherein the incremental learning is regularization-based elastic weight consolidation (EWC) incremental learning (see pg. 7 section 2.2.1. ‘Choosing and training models’: “Transfer learning, however, can suffer from catastrophic forgetting issues, where the knowledge about the old task may not be maintained when adapting parameters to a new dataset or task. To avoid this pitfall and enable continual learning [92], several approaches were proposed in the realm of brain segmentation. Garderen et al applied a regularization called elastic weight consolidation (EWC) during transfer learning [93].”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Cataltepe, Guo, Daly, and Jin before him or her, to modify the system of claim 6 to include attributes of wherein the incremental learning is regularization-based elastic weight consolidation (EWC) incremental learning in order to improve performance (see Jin at pg. 7 section 2.2.1 ‘Choosing and training models’: “Research on segmenting low- and high-grade gliomas showed that EWC improved performance on the old domain after transfer learning on the new domain. Conversely, it also restricted the adaptation capacity to the new domain.”)
Regarding claims 13 and 19:
Claims 13 and 19 recite analogous limitations to claim 6 and therefore are rejected on the same grounds.
Claims 27, 28, and 29 are rejected under 35 U.S.C 103 as being unpatentable over Cataltepe (US20190279043A1 hereinafter, Cataltepe) in view of Guo et al. (US12045585B2 hereinafter, Guo) in further view of Daly et al. (US20220215285A1 hereinafter, Daly) and further in view of Ribeiro et al. (“‘Why Should I Trust You?’ Explaining the Predictions of Any Classifier” hereinafter, Ribeiro).
Regarding claim 27:
Cataltepe in view of Guo in further view of Daly teaches the system of claim 1.
Cataltepe does not explicitly teach wherein to generate the enhanced version of the machine learning model, the processing device is further configured to retrain the machine learning model using weighted loss based on a training set comprising incorrectly predicted samples.
Ribeiro, however, analogously teaches wherein to generate the enhanced version of the machine learning model, the processing device is further configured to retrain the machine learning model using weighted loss based on a training set comprising incorrectly predicted samples (see pg. 1142 section 6.3: “If one notes that a classifier is untrustworthy, a common task in machine learning is feature engineering, i.e. modifying the set of features and retraining in order to improve generalization. Explanations can aid in this process by presenting the important features, particularly for removing features that the users feel do not generalize.”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Cataltepe, Guo, Daly, and Jin before him or her, to modify the system of claim 27 to include attributes of wherein to generate the enhanced version of the machine learning model, the processing device is further configured to retrain the machine learning model using weighted loss based on a training set comprising incorrectly predicted samples in order to modify the set of feature and retraining in order to improve generalization (see pg. 1142 section 6.3: “modifying the set of features and retraining in order to improve generalization.”)
Regarding claims 28 and 29:
Claims 28 and 29 recite analogous limitations to claim 27 and therefore are rejected on the same grounds.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew A Bracero whose telephone number is ((571)270-0592. The examiner can normally be reached Monday - Friday 9:00 a.m. - 5:00 p.m. ET.
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/ANDREW BRACERO/Examiner, Art Unit 2126 /DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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