DETAILED ACTION
This office action is in response to the Application No. 16926407 filed on
05/11/2026. Claims 2-4, 6, 11, 13-15 and 17 has been cancelled, claims 1, 5, 7-10, 12, 16 and 18-21 are presented for examination and are currently pending. Applicant’s arguments have been carefully and respectfully considered.
Response to Arguments
2. Upon further review, the arguments of the Applicant has been considered and are persuasive, as a result, the prior art rejection has been withdrawn.
The claim amendment of 05/11/2026 has overcome the 112 (b) rejection of the
previous Office Action. As a result the 112(b) rejection has been withdrawn.
However, new 112(b) and 112(d) rejections has been issued. The 112s needs to be resolved before the claim can be allowed.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3. Claims 1, 5, 7-10, 12, 16 and 18-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, lines 17-18 recites “with said at least one hardware processor, iteratively repeating said neuron alignment, training, and selecting steps to obtain a further refined new model”.
However, the “evaluating ...” in line 9 is not included in the iteration steps in lines 17-18, that is “iteratively repeating said neuron alignment, training, and selecting steps”. The iteration step should include the evaluating step before the selecting steps to be in accordance with order of the limitations of claim 1.
Independent claims 10 and 12 which is similar to claim 1, as the same issues as claim 1.
Claims 5, 7-9, 16 and 18-21 that are not specifically mentioned are rejected due to dependency.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
4. Claim 7 and 8 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
The limitations recited in claims 7 and 8 have similarly been recited in independent claim 1. As a result, claims 7 and 8 fails to further limit the limitations in independent claim 1.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Allowable Subject Matter
5. 1, 5, 7-10, 12, 16 and 18-21 would be allowable if rewritten to overcome the 35 USC 112 rejections.
The closest prior art record is Uriot modified by Nair and Ryan.
Uriot discloses In order to find a mapping between the hidden layers of two feedforward neural networks (i.e. a correspondence between the neurons of the two layers) trained on the same dataset, we first have to define how to represent a layer, pg. 3, left col., section 3.1. The Examiner notes the training data (i.e dataset) is included in the alignment data; In order to find a mapping between the hidden layers of two feedforward neural networks (i.e. a correspondence between the neurons of the two layers) trained on the same dataset, we first have to define how to represent a layer, pg. 3, left col., section 3.1. The Examiner notes the dataset is the alignment data; Following Algorithm 2, we can then functionally align θa and θb by permuting the neurons of the layers {Lda, Ldb }Dd=1 (and thus the weights) according to the pairings {lda , ldb }Dd=1. Finally, once the weights of the two neural networks are permuted, we can safely crossover the two networks by directly matching the weights at the same location in both networks … where the networks are now functionally aligned according to a uniquely defined mapping obtained by applying Algorithm 2, pg. 5, right col., section 4.3.
Nair discloses For a given hyperparameter configuration, the models (e.g. models ml, m2 and m3 in the example above) may be responsible for predicting the minimum value of the loss curve [0046]; In some embodiments, since the models used have been trained or developed using training losses of multiple NNs [0039]; The predicted loss may be a mean of loss for all NNs within a certain category of instances, possibly normalized when displayed in FIG. 5 to the range of loss values of the NN whose actual loss over epochs is shown in orange lines 602 [0093]; The example operations of FIG. 4B may be used for early stopping when searching for a set of hyperparameters for a NN [0082]; In global mode, the model may predict the probability that the current loss curve will be able to improve beyond the best performing model (e.g. NN with certain hyperparameters) seen so far [0015]; ... In another embodiment one best loss value over all different NN-hyperparameter combinations seen thus far may be recorded (e.g. as best_metric) [0086]); In another embodiment one best loss value over all different NN-hyperparameter combinations seen thus far may be recorded (e.g. as best_metric); when a new best loss occurs, it may replace the best loss value [0086]. The Examiner notes that the new best loss is a new model.
Ryan discloses Noise may be introduced into the inputs to the black boxes [0139]; an intrusion activity or cyber-attack in network traffic data [0129]. The Examiner notes that noise and cyber-attack in network traffic data are adversarial attacks on data; Meta Learning 224 selects the best performing models which is sent as input according to the arrow into machine learning 222 (which is considered as artificial intelligence application) Fig. 15, diagram on the right of last shaded section; meta learning 224 processes for providing models and selecting the best performing models [0139]; One way that this can be done is by creating images from time-series data, as described above, and then passing the image data to a Generalized Adversarial Network (GAN), which is a Deep Neural Network that enables learning of a distribution of the data from the time-series [0182]; forecasting traffic congestion on streets by detecting patterns in a time-series from video cameras on streets, cars [0061]. The Examiner notes that video cameras as tools for perceiving scenes in computer vision.
However, neither Uriot modified by Nair and Ryan, disclose “with said at least one hardware processor, iteratively repeating said neuron alignment, training, and selecting steps to obtain a further refined new model”, in combination with other limitations of the claim.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MORIAM MOSUNMOLA GODO whose telephone number is (571)272-8670. The examiner can normally be reached Monday-Friday 8am-5pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T Bechtold can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.G./Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
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