DETAILED ACTION
This action is in response to claims filed 07 January 2024 for application 18406198 filed 07 January 2024. Currently claims 1-20 are pending.
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 § 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.
Claims 3-5 and 18 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 3-5 and 18 recite the limitations "the posterior probability distribution", “the marginalized loss function”, “the marginalized loss function” and “the marginalized loss function” in lines 1-2, lines 1-2, lines 1-2, and line 3 respectively. There is insufficient antecedent basis for these limitation in the respective claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims can be interpreted as directed to software per se. The system of claim 16 comprising an input and output port does not have any associated hardware in the claim. When looking at [0118] of the specification, the system can be interpreted as completely software. Claims 17-20 are likewise rejected based upon their dependence to claim 16 and none of the claims correct this deficiency.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In step 1, claims 1 and 10 are directed to a statutory category of a method. Claim 16 is not directed to the statutory category, however, will be fully analyzed for compact prosecution..
In step 2a prong 1, claim 1 recites, in part, providing a trained autoencoder, receiving an input and processing the input to produce an output. Claim 10 recites, in part, recite, in part, modelling natural language with a first classifier, defining a loss function to transfer information from the first classifier to a second classifier, and process input according to the second classifier. Claim 16 recites, in part, a port to receive input, an autoencoder that has been trained with a loss function, and an output for processed natural language. The limitations of providing a trained autoencoder, receiving an input, processing the input to produce an output, modelling natural language with a first classifier, defining a loss function to transfer information from the first classifier to a second classifier, process input according to the second classifier,
a port to receive input, an autoencoder that has been trained with a loss function, and an output for processed natural language are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “autoencoder”, “input port”, “output port” in the context of the claims, the limitations encompass a person applying a neural network model to data to produce a result in the mind or with aid. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
In step 2a prong 2, this judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “autoencoder”, “input port”, “output port”. The computer components in the claim are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Please see MPEP §2106.04.(a)(2).III.C.
In step 2b, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “autoencoder”, “input port”, “output port” to perform the steps of the claims amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-9, 11-15 and 17-20 recite further limitations of the classifier has weights and bias and has a marginalized loss function, using a Markov chain monte Carlo, the marginalized loss function uses Laplace approximation or Bregman divergence, a denoising autoencoder, trained using backpropagation, using stochastic gradient descent, training with bag-of-words and a classifier is a SVM or logistic regression classifier. These limitations recite the same abstract idea as identified above in step 2a prong 1. No further additional elements are present to amount to a practical application in step 2a prong 2 or significantly more in step 2b.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 2, 6-11, 14-17, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions) in view of Chen et al. (Marginalized Denoising Auto-encoders for Nonlinear Representations).
Regarding claim 1, Socher discloses: A natural language processing method, comprising:
providing an autoencoder comprising a neural network having at least one hidden layer (Fig 3 Autoencoder, “To prevent such undesirable behavior, we modify the hidden layer such that the resulting parent representation always has length one” p154 §Length Normalization), the autoencoder having a [loss] function defined with the weights learned from a classifier trained on natural language (“The hyperparameter weighs reconstruction and cross-entropy error. When minimizing the cross entropy error of this softmax layer, the error will backpropagate and influence both the RAE parameters and the word representations.” P155 ¶2, Note: the autoencoder has a hidden layer, its weights are trained using error functions and words (natural language) learned from a bag-of-words equivalent in p152 §2.1);
receiving, by the autoencoder, a natural language input (Fig 1 input phrase is natural language); and
processing the natural language input with the autoencoder, to produce an output (Fig 1 predicted sentiment distribution).
Socher discloses an error function, however, does not explicitly disclose loss function. Chen teaches a loss function (p2 §2.2 Marginalized loss function).
Socher and Chen are in the same field of endeavor of autoencoders and are analogous. Socher discloses the use of an autoencoder for natural language inputs to predict sentiment. Chen discloses the use of the known marginalized loss function. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the error functions of the autoencoder used by Socher to utilize the known marginalized loss function as taught by Chen to yield the predictable results of a more efficient learning process by effectively using more corrupted samples through marginalization.
Regarding claim 2, Socher discloses: The natural language processing method according to claim 1, wherein the classifier has a plurality of weights and a bias, and the bias is reduced by defining a posterior probability distribution on the weights of the classifier (P153 §2.2 ¶3 EQ 2 shows the autoencoder uses weights and biases).
Socher does not explicitly disclose, however, Chen teaches: deriving the loss function as a marginalized loss function (p2 §2.2 Marginalized loss function eq 2).
Regarding claim 6, Socher does not explicitly disclose, however, Chen teaches: The natural language processing method according to claim 1, wherein the autoencoder is a denoising autoencoder (p2 §2.2 denoising autoencoder using marginalization).
Regarding claim 7, Socher discloses: The natural language processing method according to claim 1, wherein the autoencoder is trained using backpropagation (“The hyperparameter weighs reconstruction and cross-entropy error. When minimizing the cross entropy error of this softmax layer, the error will backpropagate and influence both the RAE parameters and the word representations.” P155 ¶2).
Regarding claim 8, Socher discloses: The natural language processing method according to claim 1, wherein the autoencoder is a … comprising the neural network trained according to … gradient descent training using randomly selected data samples, wherein a gradient is calculated using back propagation of errors (p155 §3 Learning discloses training via gradient descent using back-propagation).
Socher does not explicitly disclose, however, Chen teaches: denoising autoencoder (p2 §2.2 denoising autoencoder using marginalization) … stochastic (p5 §4.1.Methods ¶2) gradient descent.
Regarding claim 9, Socher discloses: The natural language processing method according to claim 8, wherein the training comprises training an objective function of the classifier with a bag of words (p152 §2.1 Neural word representations, sampling is performed on a Gaussian distribution and label distributions are used by the model, the matrix of word vectors is interpreted as a bag-of-words), wherein the classifier comprises at least one of (a) a support vector machine classifier with squared hinge loss and l2 regularization, and (b) a Logistic Regression classifier (“In order to predict the sentiment distribution of a sentence with this model, we use the learned vector representation of the top tree node and train a simple logistic regression classifier.” P155 left column last ¶).
Regarding claim 10, Socher discloses: A method of processing natural language, comprising:
modelling natural language with a first classifier, dependent on a set of classifier weights derived from on a set of labeled natural language data (p152 §2.1 Neural word representations, sampling is performed on a Gaussian distribution and label distributions are used by the model);
defining a [loss] function, based on a posterior probability distribution learned from the first classifier, to transfer information from the first classifier to a second classifier (p152 §2.1 Neural word representations, sampling is performed on a Gaussian distribution and label distributions are used by the model); and
automatically processing input data according to the second classifier (Fig 1 input phrase is natural language).
Socher discloses an error function, however, does not explicitly disclose loss function. Chen teaches a loss function (p2 §2.2 Marginalized loss function).
Regarding claim 11, Socher discloses: The method according to claim 10, wherein the first classifier comprises a neural network having a plurality of weights and a bias, the method further comprising reducing the bias by defining the posterior probability distribution on the weights of the classifier, (P153 §2.2 ¶3 EQ 2 shows the autoencoder uses weights and biases).
Socher does not explicitly disclose, however, Chen teaches: deriving the loss function as a marginalized loss function (p2 §2.2 Marginalized loss function eq 2).
Regarding claim 14, Socher discloses: The method according to claim 10, wherein the first second and second classifier comprise a denoising autoencoder comprising the neural network trained according to stochastic gradient descent training using randomly selected data samples (p2 §2.2 denoising autoencoder using marginalization, p5 §4.1.Methods ¶2).
Chen teaches: wherein a gradient is calculated using back propagation of errors (p155 §3 Learning discloses training via gradient descent using back-propagation).
Regarding claim 15, Socher discloses: The natural language processing method according to claim 14, wherein the training comprises training an objective function of the classifier with a bag of words, wherein the classifier comprises at least one of (a) a support vector machine classifier with squared hinge loss and l2 regularization, and (b) a Logistic Regression classifier (p152 §2.1 Neural word representations, sampling is performed on a Gaussian distribution and label distributions are used by the model, the matrix of word vectors is interpreted as a bag-of-words, “In order to predict the sentiment distribution of a sentence with this model, we use the learned vector representation of the top tree node and train a simple logistic regression classifier.” P155 left column last ¶).
Regarding claim 16, Socher discloses: A natural language processing system, comprising:
an input port configured to receive a natural language input (Fig 1 input phrase is natural language);
an autoencoder comprising a neural network having at least one hidden layer, the autoencoder having a … function defined with the weights learned from a classifier trained on natural language (Fig 3 Autoencoder, “To prevent such undesirable behavior, we modify the hidden layer such that the resulting parent representation always has length one” p154 §Length Normalization, “The hyperparameter weighs reconstruction and cross-entropy error. When minimizing the cross entropy error of this softmax layer, the error will backpropagate and influence both the RAE parameters and the word representations.” P155 ¶2, Note: the autoencoder has a hidden layer, its weights are trained using error functions and words (natural language);
an output port configured to provide processed natural language (Fig 1 predicted sentiment distribution).
Socher discloses an error function, however, does not explicitly disclose loss function. Chen teaches a loss function (p2 §2.2 Marginalized loss function).
Regarding claim 17, Socher discloses: The natural language processing system according to claim 16, wherein the classifier has a plurality of weights, and the classifier has a posterior probability distribution based on the weights of the classifier (P153 §2.2 ¶3 EQ 2 shows the autoencoder uses weights and biases).
Socher does not explicitly disclose, however, Chen teaches: deriving the loss function as a marginalized loss function (p2 §2.2 Marginalized loss function eq 2).
Regarding claim 19, Socher does not explicitly disclose, however, Chen teaches: The natural language processing system according to claim 16, wherein the autoencoder is a denoising autoencoder p2 §2.2 denoising autoencoder using marginalization, p5 §4.1.Methods ¶2).
Regarding claim 20, Socher discloses: The natural language processing system according to claim 16, and the neural network is trained according to … gradient descent training using back propagation of errors (p155 §3 Learning discloses training via gradient descent using back-propagation).
Chen teaches: stochastic (p5 §4.1.Methods ¶2)
Claim(s) 3-5 ,12-13, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Socher in view of Chen and further in view of Azarafrooz et al. (US 20180129807 A1).
Regarding claim 3, Socher does not explicitly disclose, however, Azarafrooz teaches: he natural language processing method according to claim 1, wherein the posterior probability distribution on the weights of the classifier is estimated using a Markov chain Monte Carlo method ([0083-90] Monte Carlo simulation of weights of a classifier using Bregman Divergence and Laplace estimator).
Socher, Chen and Azarafrooz are in the same field of endeavor of classifiers and are analogous. Socher discloses the use of an autoencoder for natural language inputs to predict sentiment. Chen discloses the use of the known marginalized loss function. Azarafrooz teaches the use of Monte Carlo methods, Bregman Divergence and Laplace estimators for use with classification of source code. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the estimators and parameters of the autoencoder used by Socher and Chen to utilize the known MCMC, Bregman Divergence and Laplace estimator as taught by Azarafrooz to yield the predictable results of increased prediction rate (Azarafrooz [0091]).
Regarding claim 4, Socher does not explicitly disclose, however, Azarafrooz teaches: The natural language processing method according to claim 3, wherein the marginalized loss function is derived with a Laplace approximation([0083-90] Monte Carlo simulation of weights of a classifier using Bregman Divergence and Laplace estimator).
Regarding claim 5, Socher does not explicitly disclose, however, Azarafrooz teaches: The natural language processing method according to claim 3, wherein the marginalized loss function is a Bregman Divergence ([0083-90] Monte Carlo simulation of weights of a classifier using Bregman Divergence and Laplace estimator).
Regarding claim 12, Socher does not explicitly disclose, however, Azarafrooz teaches: The method according to claim 11, wherein the marginalized loss function is derived with a Laplace approximation or a Bregman Divergence ([0083-90] Monte Carlo simulation of weights of a classifier using Bregman Divergence and Laplace estimator).
Regarding claim 13, Socher does not explicitly disclose, however, Azarafrooz teaches: The method according to claim 10, wherein the posterior probability distribution is estimated using a Markov chain Monte Carlo method ([0083-90] Monte Carlo simulation of weights of a classifier using Bregman Divergence and Laplace estimator).
Regarding claim 18, Socher does not explicitly disclose, however, Azarafrooz teaches: The natural language processing system according to claim 16, wherein the posterior probability distribution on the weights of the classifier is estimated using a Markov chain Monte Carlo method, and wherein the marginalized loss function is derived with a Laplace approximation or a Bregman Divergence ([0083-90] Monte Carlo simulation of weights of a classifier using Bregman Divergence and Laplace estimator).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 3, 5-8, 10, 12-16, 19 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-6 and 10 of U.S. Patent No. 11205103 B2 in view of Socher et al. (Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions).
Instant Application
US 11205103
Claim 1
Claim 1
A natural language processing method, comprising:
providing an autoencoder comprising a neural network having at least one hidden layer,
the autoencoder having a loss function defined with the weights learned from a classifier trained on natural language;
receiving, by the autoencoder, a natural language input; and
processing the natural language input with the autoencoder, to produce an output.
A method of modelling data, comprising:
training an objective function of a linear classifier, based on a set of labeled data, to derive a set of classifier weights;
defining a posterior probability distribution on the set of classifier weights of the linear classifier;
approximating a marginalized loss function for an autoencoder as a Bregman divergence, based on the posterior probability distribution on the set of classifier weights learned from the linear classifier; and
automatically classifying unlabeled data using a compact classifier according to the marginalized loss function.
Patent ‘103 fails to teach the limitation in bold above. However, Socher teaches that limitation in Fig 1, natural language input.
It would have been obvious at the time of the applicant’s invention to modify the teachings of patent ‘103 by incorporating: trained on natural language as taught by Socher for the purpose of applying the autoencoder to natural language tasks.
Claims 10 and 16 are largely the same subject matter as claim 1 and are likewise rejected.
Claim 3 of the instant application is fully anticipated by claim 10 of the ‘103 patent.
Claim 5 of the instant application is fully anticipated by claim 1 of the ‘103 patent.
Claim 6 of the instant application is fully anticipated by claim 4 of the ‘103 patent.
Claim 7 of the instant application is fully anticipated by claim 5 of the ‘103 patent.
Claim 8 of the instant application is fully anticipated by claim 5 of the ‘103 patent.
Claim 12 of the instant application is fully anticipated by claim 1 of the ‘103 patent.
Claim 13 of the instant application is fully anticipated by claim 10 of the ‘103 patent.
Claim 14 of the instant application is fully anticipated by claim 5 of the ‘103 patent.
Claim 15 of the instant application is fully anticipated by claim 6 of the ‘103 patent.
Claim 19 of the instant application is fully anticipated by claim 4 of the ‘103 patent.
Claim 20 of the instant application is fully anticipated by claim 5 of the ‘103 patent.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trujillo can be reached at (571)-272-3677. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ERIC NILSSON/ Primary Examiner, Art Unit 2151
Prosecution Insights