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 claims filed 10/27/2025. Claims 1-4, 7-14, 17-21 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/27/2025 has been entered.
Claim Rejections - 35 USC § 101
The 101 rejections of the claims are withdrawn in view of Applicant’s response.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 9, 11-13, 19, 21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lewis (US 20220237465 A1).
For claim 1, Lewis discloses: a method of generating a machine learning model for classifying sequential data (fig.1, 0027-29 contemplates various data including sequential data such as text, video, audio, sensor, etc.), the method comprising:
receiving the sequential data, wherein the sequential data comprises a plurality of records having a plurality of features in a plurality of environments (ibid: sequential data encoded into vector form, hence, plurality of features; the cited passages also disclose a plurality of environments for each mode (e.g., different vehicle environments for automatic vehicle navigation, field data for forms, topics, etc.) as well as the modes themselves being different environments; see also 0061: training data from different scene environments);
initializing a set of mask weights on the features (0044-46: parameter tensors corresponding to divided volumes or sparsity constraints constitute pruning masks on the features / intermediate layer weights; see also 0063-66);
initializing a set of classifier weights on the features, wherein each mask weight corresponds to one of the classifier weights and influences the corresponding classifier weight in the machine learning model (0063, 0067-68: weights for each layer are generated for forward propagation);
processing, iteratively, a plurality of frames of the sequential data using the machine learning model comprising the mask weights and the classifier weights, wherein at each iteration the processing comprises (fig.4, 0067: iterative forward and back propagations steps):
generating a current one of the frames of the sequential data (ibid: an input vector generated for the current sequential data frame);
determining a variance of the classifier weights on the features of the current frame (0065: determining current variance parameters);
computing a penalty term over a data space of the current frame, wherein computing the penalty term over the data space of the current frame further comprises:
computing a penalty loss term over the data space of the current frame of the classifier weights across the current frame (0076-77: SNR function pushing for lower SNR constitutes a penalty loss term); and
computing a total loss as a function of a prediction error for the mask weights on the features of the current frame and the penalty loss (0074-75: combination with reconstruction loss as prediction error based on features); and
updating the mask weights using the classifier weights on the features and the penalty term (0073-74: mask weights are updated based on the penalty term / SNR loss during training), wherein the updating of the mask weights decreases the mask weights of spurious ones of the features according to the variance (0076-77: spurious features with greater variance are decreased, such as by reducing SNR by reducing mean parameter, hence, decreasing mask wight; see also 0080, 0089: reduction of mask weights via pruning); and
outputting the machine learning model including updated ones of the mask weights to a service for performing a classification task based on a detection of at least one of the features in test data (fig.1, 0029 contemplates various applications of pruned inference system featuring the updated mask weights for performing classification tasks, these tasks requiring transforming inputs to intermediate features).
For claim 2, Lewis discloses the method of claim 1, as described above. Lewis further discloses: the data space of the current frame is an entire data space of the current frame (0027, 0061 contemplates training over entire space of training data), the method includes no assumption on a degree of invariance of the mask weights (§fig.4, 0067: no assumption on invariance is made, relying instead on loss function 0076-77), and the machine learning model is one of an existing model and a newly instantiated model (0034: initializing parameters for training a new model).
For claim 3, Lewis discloses the method of claim 1, as described above. Lewis further discloses: wherein the method includes no assumption on whether each instance of a given one of the features is invariant or spurious and no labeled data about the environments (0067, 0076-77: no assumption about invariance or spuriousness is made as method relies on penalty loss for adjustment).
For claim 9, Lewis discloses the method of claim 1, as described above. Lewis further discloses: receiving the test data from at least one sensor (fig.1, 0028: sensor data) and the classification task comprises processing the test data received from the at least one sensor using the machine learning model (fig.1: sensor data is passed through inference model), wherein the machine learning model detects at least one event in the data (0028-29: contemplates various event or event type predictions from sensor signals).
For claim 11, Lewis discloses: a computer readable medium comprising computer executable instructions which when executed by a computer system cause the computer (fig.8, 0091-92) to perform a method for generating a machine learning model for classifying sequential data (fig.1, 0027-29 contemplates various data including sequential data such as text, video, audio, sensor, etc.), the method comprising:
accessing the sequential data, wherein the sequential data comprises a plurality of records having a plurality of features in a plurality of environments (ibid: sequential data encoded into vector form, hence, plurality of features; the cited passages also disclose a plurality of environments for each mode (e.g., different vehicle environments for automatic vehicle navigation, field data for forms, topics, etc.) as well as the modes themselves being different environments; see also 0061: training data from different scene environments);
initializing a set of mask weights on the features (0044-46: parameter tensors corresponding to divided volumes or sparsity constraints constitute pruning masks on the features / intermediate layer weights; see also 0063-66);
initializing a set of classifier weights on the features, wherein each mask weight corresponds to one of the classifier weights and influences the corresponding classifier weight in the machine learning model (0063, 0067-68: weights for each layer are generated for forward propagation);
processing, iteratively, a plurality of frames of the sequential data using the machine learning model comprising the mask weights and the classifier weights, wherein at each iteration the processing comprises (fig.4, 0067: iterative forward and back propagations steps):
generating a current one of the frames of the sequential data (ibid: an input vector generated for the current sequential data frame);
determining a mean and a variance of the classifier weights on the features of the current frame (0065: determining current mean and variance parameters);
computing a penalty term over a data space of the current frame, wherein computing the penalty term over the data space of the current frame further comprises:
computing a penalty loss term over the data space of the current frame of the classifier weights across the current frame (0076-77: SNR function pushing for lower SNR constitutes a penalty loss term); and
computing a total loss as a function of a prediction error for the mask weights on the features of the current frame and the penalty loss (0074-75: combination with reconstruction loss as prediction error based on features); and
updating the mask weights using the classifier weights on the features and the penalty term (0073-74: mask weights are updated based on the penalty term / SNR loss during training), wherein the updating of the mask weights increases the mask weights of invariant ones of the features according to the variance (0076-77: non-spurious weights have their mean and variance and scalar terms adjusted during the course of backpropagation, hence, portions of invariant ones will have increase mean and scalar weighting during backpropagation based on total loss function); and
outputting the machine learning model including updated ones of the mask weights to a service for performing a classification task based on a detection of at least one of the features in test data (fig.1, 0029 contemplates various applications of pruned inference system featuring the updated mask weights for performing classification tasks, these tasks requiring transforming inputs to intermediate features).
Claims 12-13, 19, 21 recite analogous subject matter to the above claims and hence are rejected under the same rationale.
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) 4, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis (US 20220237465 A1) in view of Parascandolo ("Learning explanations that are hard to vary", published 2020).
For claim 4, Lewis discloses the method of claim 1, as described above. Lewis further does not disclose the limitations of claim 4.
Parascandolo discloses: wherein a number of iterations, s, is pre-defined for the processing of the frames (§3.3: §B.6 ¶3 discloses training for a preset number of epoch iterations, see also §3.3 disclosing the number of levels per epoch, sampling of a batch of data).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify method of Lewis by incorporating the epoch training technique of Parascandolo. Both concern the art of neural network optimization, and the incorporation would have, according to Parascandolo, allow comparisons of learning rate for different hyperparameter sets (§3.3: §B.6 ¶3).
Claim(s) 14 recite analogous subject matter to the above claims and hence are rejected under the same rationale.
Claim(s) 7-8, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis (US 20220237465 A1) in view of Cantarero (US 20160062967 A1).
For claim 7, Lewis discloses the method of claim 1, as described above. Lewis does not disclose the limitations of claim 7.
Cantarero discloses: wherein the test data includes a text corpus of documents (fig.1:102, 0033), and wherein the classification task comprises:
processing each of the documents using the machine learning model to identify instances of the features in each of the documents (fig.1:108-110, 0043-45: processing documents to identify features, such as genre, category, etc.; combination with Lewis yielding processing via the machine learning technique of Lewis);
classifying each of the documents according to respective ones of the identified instances of the features (ibid: associating features with classifications such as sentiment); and
adding an indication of the respective classifications to each of the documents (fig.1:112, 0046).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify method of Lewis by incorporating the sentiment analysis platform of Cantarero. Both concern the art of data science and machine analysis of human creations, and the incorporation would have, according to Cantarero, allow for fulfilment of the need for deeper analysis of textual data (0001-3).
For claim 8, Lewis discloses the method of claim 1, as described above. Lewis does not disclose the limitations of claim 8.
Cantarero discloses: wherein the test data includes a document (fig.1:102, 0033), and the classification task comprises:
processing the document using the machine learning model to identify instances of the features in the document (fig.1:108-110, 0043-45: processing documents to identify features, such as genre, category, etc.; combination with Lewis yielding processing via the machine learning technique of Lewis);
identifying a sentiment for each of a plurality of portions of the document according to corresponding ones of the identified instances of the features (ibid: identifying sentiment according to identified features); and
adding an indication of the sentiments to the document (fig.1:112, 0046).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify method of Lewis by incorporating the sentiment analysis platform of Cantarero. Both concern the art of data science and machine analysis of human creations, and the incorporation would have, according to Cantarero, allow for fulfilment of the need for deeper analysis of textual data (0001-3).
Claim(s) 17-18 recite analogous subject matter to the above claims and hence are rejected under the same rationale.
Claim(s) 10, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis (US 20220237465 A1) in view of Ma (US 20200192970 A1).
For claim 10, Lewis discloses the method of claim 9, as described above. Lewis does not disclose the limitations of claim 10.
Ma discloses: wherein the at least one sensor includes an accelerometer and a gyroscope (35), and the at least one event is a selected from at least one of a walking event, a walking upstairs event, a walking downstairs event, a sitting event, a standing event, and a laying down event (0037).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify method of Lewis by sensor-based detection technique of Ma. Both concern the art of machine pattern recognition, and the incorporation would have, according to Ma, allowed application to various human-worn devices to allow broad applicability such as human activity recognition (0033-34).
Claim(s) 20 recite analogous subject matter to the above claims and hence are rejected under the same rationale.
Response to Arguments
Applicant’s arguments have been fully considered. In the remarks, Applicant argues:
1) The recited claims are not directed to an abstract idea without significantly more.
Examiner agrees and the rejections are withdrawn.
2) Parascandolo, Kingma, and Haemel do not teach the newly amended limitations as they do not disclose masking for input features, only for weights or parameters.
Applicant’s arguments are moot in view of newly cited art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Das 102 (US 20190013102 A1) discloses feature rejection for time series signals, see fig.6a.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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/LIANG LI/
Primary examiner AU 2143