DETAILED ACTION
This action is responsive to the amendment filed on 12/05/2025. Claims 1-5, 7-18, and 20 are pending in the case. Claims 1, 11, and 14 are independent claims. Claims 1, 11, and 14 are currently amended.
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 .
Priority
Acknowledgement is made of applicant’s claim for domestic priority based on PCT/RU2019/000313.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/05/2025 has been entered.
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 1-5, 7-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1 Statutory Category: Claim 1 is directed to a machine, which falls within one of the four statutory categories.
Step 2A Prong 1 Judicial exception: Claim 1 recites, in part, “change the binary weights of the BNN using a backpropagation method…wherein changing the binary weights increases or minimizes decrease of an information entropy of a weight distribution of the binary weights of the BNN”, “determining a weight distribution for each of a plurality of layers of the BNN”, and “determining an information entropy based on the determined weight distribution for each of the plurality of layers of the BNN”. These limitations are processes that, under the broadest reasonable interpretation covers the recitation of mathematical calculations, and mathematical relationships which fall within the “Mathematical concepts” grouping of abstract ideas, See MPEP § 2106.04(a)(2)(I)(C). Further, the claim recites: “increasing a backpropagation gradient for each of the plurality of layers, for which the information entropy is determined below a certain threshold”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP §2106.04(a)(2)(I)(A). Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Integration into a practical application: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “a device”, “a processor”, and “a memory storing instructions”. These elements are additional elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). The claim also recites the additional element “obtain binary weights of a binary neural network (BNN) to be trained”, which is discussed in paragraph 0052 of applicant’s specification, “The device 100 is configured to obtain binary weights 102 of the BNN 101, e.g. to receive them from a training unit, or to determine them based on analyzing the BNN 101”. This element is recited at a high level of generality such that it amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception. Alternatively, determining them based on analyzing the BNN, covers performance of the limitation in the mind or pen and paper which falls under the “Mental Processes” grouping of abstract ideas, see MPEP § 2106.04(a)(2)(III). Further, the claim recites: “such that information capacity is increased, accuracy is improved, and overfitting is reduced”. This limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “wherein the trained BNN is used to classify data objects, including image recognition, natural language processing, or trajectory prediction of autonomous driving vehicles”. This limitation does not positively recite the action of “classification” and thus is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Alternatively, if the limitation were considered to positively recite “classification”, the limitation would be considered an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g).
Step 2B Significantly more: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements “a device”, “a processor”, and “a memory storing instructions” amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional element “obtain binary weights of a binary neural network (BNN) to be trained” is insignificant extra-solution activity to the judicial exception and is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the additional elements “such that information capacity is increased, accuracy is improved, and overfitting is reduced” and “wherein the trained BNN is used to classify data objects, including image recognition, natural language processing, or trajectory prediction of autonomous driving vehicles” generally link the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Alternatively, “wherein the trained BNN is used to classify data objects, including image recognition, natural language processing, or trajectory prediction of autonomous driving vehicles” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. Furthermore, the additional element is well‐understood, routine, and conventional as taught by activity is supported under Berkheimer Option 2, Keller et al., U.S. Patent Application Publication No. 20190294972, Paragraph 0003, Lines 1-5, “Artificial neural networks (ANNs) are commonly used computing systems that address a wide variety of tasks, such as classification, image recognition, regression, function approximation, samples of data according to a learned distribution, etc.”. See MPEP § 2106.05(d). The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: “wherein the backpropagation method includes a backpropagation of error gradients obtained during training of the BNN”. This limitation recites additional mathematical concepts in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: “change the binary weights of the BNN separately for at least one filter or layer of the BNN”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Backpropagation is a method to change binary weights that operates layer-by-layer, or effectively “separately” for at least one layer. The additional element is well‐understood, routine, and conventional as taught by activity is supported under Berkheimer Option 2, Thawakar, Backpropagation: Learning Factors, 12/11/2018, https://medium.com/@omee0805/backpropagation-learning-factors-aca9c6e3bc1 , Paragraph 1, Lines 8-9, “Backpropagation is the widely used and most successful algorithm for the training of a neural network of all time”. Accordingly, the claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: “change the binary weights of the BNN in real-time during training of the BNN”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Backpropagation is a method that changes binary weights in real-time during training. The additional element is well‐understood, routine, and conventional as taught by activity is supported under Berkheimer Option 2, Thawakar, Backpropagation: Learning Factors, 12/11/2018, https://medium.com/@omee0805/backpropagation-learning-factors-aca9c6e3bc1 , Paragraph 1, Lines 8-9, “Backpropagation is the widely used and most successful algorithm for the training of a neural network of all time”. Accordingly, the claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: “randomly replacing at least one prevalent weight by a minority weight for one or more layers of the BNN”. This limitation is a continuation of the mathematical concept abstract idea of the parent claim.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is further incorporated, and further, the claim recites: “increase the backpropagation gradient for a given layer by a value that is proportional to loss of information entropy in a following layer of the BNN”. This limitation recites additional mathematical concepts in addition to the judicial exception identified in the rejection of claim 1 and claim 6, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is further incorporated, and further, the claim recites: “determining one or more weight distributions for one or more layers and/or filters of the BNN, or determining the weight distribution for the entire BNN”. This limitation recites additional mathematical concepts, namely mathematical calculations, in addition to the judicial exception identified in the rejection of claim 1. Further, the claim recites: “determining the information entropy based on each determined weight distribution”. This limitation recites additional mathematical concepts, namely mathematical calculations, in addition to the judicial exception identified in the rejection of claim 1. Further, the claim recites: “appending a cost function, used for training the BNN, with a penalty term based on the one or more determined information entropies”. This limitation recites additional mathematical concepts, namely mathematical formulas/relationships, in addition to the judicial exception identified in the rejection of claim 1. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 8 is further incorporated, and further, the claim recites: “determine an information loss based on the one or more determined information entropies”. This limitation recites additional mathematical concepts, namely mathematical calculations, in addition to the judicial exception identified in the rejection of claim 1, claim 6, claim 8. Further, the claim recites: “append the information loss as the penalty term to the cost function”. This limitation recites additional mathematical concepts, namely mathematical formulas, in addition to the judicial exception identified in the rejection of claim 1, claim 6, and claim 8.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 9 is further incorporated, and further, the claim recites: “determine the information loss with respect to a maximum information entropy of the one or more weight distributions, or with respect to a constant value”. This limitation recites additional mathematical concepts, namely mathematical calculations/relationships, in addition to the judicial exception identified in the rejection of claim 1, claim 6, claim 8, and claim 9.
The claim does not include any additional element that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 11:
Step 1 Statutory Category: Claim 11 is directed to a system, which falls within one of the four statutory categories.
Step 2A Prong 1 Judicial exception: Claim 11 recites, in part, “changing the binary weights of the BNN using a backpropagation method…wherein changing the binary weights increases or minimizes decrease of an information entropy of a weight distribution of the binary weights of the BNN”, “determining a weight distribution for each of a plurality of layers of the BNN”, and “determining an information entropy based on the determined weight distribution for each of the plurality of layers of the BNN”. These limitations are processes that, under the broadest reasonable interpretation covers the recitation of mathematical calculations, and mathematical relationships which fall within the “Mathematical concepts” grouping of abstract ideas, See MPEP § 2106.04(a)(2)(I)(C). Further, the claim recites: “increasing a backpropagation gradient for each of the plurality of layers, for which the information entropy is determined below a threshold value”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP §2106.04(a)(2)(I)(A). Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Integration into a practical application: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “obtaining binary weights of a binary neural network (BNN) to be trained”, which is discussed in paragraph 0052 of applicant’s specification, “The device 100 is configured to obtain binary weights 102 of the BNN 101, e.g. to receive them from a training unit, or to determine them based on analyzing the BNN 101”. This element is recited at a high level of generality such that it amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception. Alternatively, determining them based on analyzing the BNN, covers performance of the limitation in the mind or pen and paper which falls under the “Mental Processes” grouping of abstract ideas, see MPEP § 2106.04(a)(2)(III). Further, the claim recites: “a non-transitory machine-readable storage medium having instructions stored therein” and “a processor”. These elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Further, the claim recites: “such that information capacity is increased, accuracy is improved, and overfitting is reduced”. This limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “wherein the trained BNN is used to classify data objects, including image recognition, natural language processing, or trajectory prediction of autonomous driving vehicles”. This limitation does not positively recite the action of “classification” and thus is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Alternatively, if the limitation were considered to positively recite “classification”, the limitation would be considered an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g).
Step 2B Significantly more: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements “a non-transitory machine-readable storage medium having instructions stored therein” and “a processor” amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional element “obtaining binary weights of a binary neural network (BNN) to be trained” is insignificant extra-solution activity to the judicial exception and is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the additional elements “such that information capacity is increased, accuracy is improved, and overfitting is reduced” and “wherein the trained BNN is used to classify data objects, including image recognition, natural language processing, or trajectory prediction of autonomous driving vehicles” generally link the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Alternatively, “wherein the trained BNN is used to classify data objects, including image recognition, natural language processing, or trajectory prediction of autonomous driving vehicles” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. Furthermore, the additional element is well‐understood, routine, and conventional as taught by activity is supported under Berkheimer Option 2, Keller et al., U.S. Patent Application Publication No. 20190294972, Paragraph 0003, Lines 1-5, “Artificial neural networks (ANNs) are commonly used computing systems that address a wide variety of tasks, such as classification, image recognition, regression, function approximation, samples of data according to a learned distribution, etc.”. See MPEP § 2106.05(d). The claim is not patent eligible. The claim is not patent eligible.
Regarding claim 12: The rejection of claim 11 is further incorporated, and further, the claim recites: “determining one or more weight distributions for one or more layers and/or filters of the BNN, or determining the weight distribution for the entire BNN”. This limitation recites additional mathematical concepts, namely mathematical calculations, in addition to the judicial exception identified in the rejection of the parent claim. Further, the claim recites: “determining the information entropy based on each determined weight distribution”. This limitation recites additional mathematical concepts, namely mathematical calculations, in addition to the judicial exception identified in the rejection of the parent claim. Further, the claim recites: “appending a cost function, used for training the BNN, with a penalty term based on the one or more determined information entropies”. This limitation recites additional mathematical concepts, namely mathematical formulas/relationships, in addition to the judicial exception identified in the rejection of the parent claim. Further, the claim recites: “change the binary weights of the BNN by at least one of: randomly replacing at least one prevalent weight by a minority weight, determining the weight distribution of weights for each of the plurality of layers of the BNN, determining, per layer of the plurality of layers, the information entropy based on the determined weight distribution, or increasing a backpropagation gradient for each layer, for which the information entropy is determined below a certain threshold value”. This limitation recites mathematical concepts in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 13: The rejection of claim 12 is further incorporated, and further, the claim recites: “providing a prediction result based on the training data produced by the BNN”. This limitation recites mathematical concepts in addition to the judicial exception identified in the rejection of claims 11 and 12, thus recites a judicial exception.
Further, the claim recites the additional element of “a training device”. This element in the claim is recited at a high level of generality such that it amounts 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 further recites: providing the BNN to a training device, receiving from the training device, and storing at least one of the BNN, training data, or the trained data. These limitations are claimed at a high level of generality and amount to mere data gathering and as such are additional elements that amount to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the elements that amount to mere data gathering are directed to receiving or transmitting data over a network which the courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, See MPEP § 2106.05(d)(II). The claim is not patent eligible.
Regarding claim 14:
Step 1 Statutory Category: Claim 1 is directed to a method, which falls within one of the four statutory categories.
Step 2A Prong 1 Judicial exception: Claim 14 recites, in part, “changing… the binary weights of the BNN using a backpropagation method…wherein changing the binary weights increasing or minimizing decrease of an information entropy of a weight distribution of the binary weights of the BNN”, “determining…a weight distribution for each of a plurality of layers of the BNN”, and “determining…an information entropy based on the determined weight distribution for each of the plurality of layers of the BNN”. These limitations are processes that, under the broadest reasonable interpretation covers the recitation of mathematical calculations, and mathematical relationships which fall within the “Mathematical concepts” grouping of abstract ideas, See MPEP § 2106.04(a)(2)(I)(C). Further, the claim recites: “increasing…a backpropagation gradient for each of the plurality of layers, for which the information entropy is determined below a threshold value”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP §2106.04(a)(2)(I)(A). Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Integration into a practical application: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element: “by a processor”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites the additional element: “obtaining…binary weights of a binary neural network (BNN) to be trained”, which is discussed in paragraph 0052 of applicant’s specification, “The device 100 is configured to obtain binary weights 102 of the BNN 101, e.g. to receive them from a training unit, or to determine them based on analyzing the BNN 101”. This element is recited at a high level of generality such that it amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception. Alternatively, determining them based on analyzing the BNN, covers performance of the limitation in the mind or pen and paper which falls under the “Mental Processes” grouping of abstract ideas, see MPEP § 2106.04(a)(2)(III). Further, the claim recites: “such that information capacity is increased, accuracy is improved, and overfitting is reduced”. This limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “wherein the trained BNN is used to classify data objects, including image recognition, natural language processing, or trajectory prediction of autonomous driving vehicles”. This limitation does not positively recite the action of “classification” and thus is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Alternatively, if the limitation were considered to positively recite “classification”, the limitation would be considered an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g).
Step 2B Significantly more: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “by a processor” amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional element “obtaining…binary weights of a binary neural network (BNN) to be trained” is insignificant extra-solution activity to the judicial exception and is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the additional elements “such that information capacity is increased, accuracy is improved, and overfitting is reduced” and “wherein the trained BNN is used to classify data objects, including image recognition, natural language processing, or trajectory prediction of autonomous driving vehicles” generally link the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Alternatively, “wherein the trained BNN is used to classify data objects, including image recognition, natural language processing, or trajectory prediction of autonomous driving vehicles” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. Furthermore, the additional element is well‐understood, routine, and conventional as taught by activity is supported under Berkheimer Option 2, Keller et al., U.S. Patent Application Publication No. 20190294972, Paragraph 0003, Lines 1-5, “Artificial neural networks (ANNs) are commonly used computing systems that address a wide variety of tasks, such as classification, image recognition, regression, function approximation, samples of data according to a learned distribution, etc.”. See MPEP § 2106.05(d). The claim is not patent eligible. The claim is not patent eligible.
Regarding claim 15: The rejection of claim 14 is further incorporated, and further, claim 15 is substantially similar to claim 2 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 16: The rejection of claim 14 is further incorporated, and further, claim 16 is substantially similar to claim 3 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 17: The rejection of claim 14 is further incorporated, and further, claim 17 is substantially similar to claim 4 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 18: The rejection of claim 14 is further incorporated, and further, claim 18 is substantially similar to claim 5 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 20: The rejection of claim 14 is further incorporated, and further, claim 20 is substantially similar to claim 7 respectively, and is rejected in the same manner and reasoning applying.
Allowable Subject Matter
Claims 1-5, 7-18, and 20 have only been rejected under 35 U.S.C. 101.
A complete prior art search has been performed for these claims; however, the search did
not uncover any prior art that fairly teach or suggest the features in these claims. Specifically,
independent claims 1, 11, and 14 are considered allowable over the prior art since when reading
the claims in light of the specification, as per MPEP 2111.01, none of the references of record
either alone or in combination fairly disclose or suggest the combination of limitations specified
in the claims, including at least:
“determining an information entropy based on the determined weight distribution for each of the plurality of layers of the BNN, and increasing a backpropagation gradient for each of the plurality of layers, for which an information entropy is determined below a certain threshold value”
The closest prior art of record includes:
Pertinent art (BERETA, Michał. Entropy-based regularization of AdaBoost. Computer Assisted Methods in Engineering and Science, [S.l.], v. 24, n. 2, p. 89–100, dec. 2017. ISSN 2956-5839. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/206>) discloses training classifiers based on weight distributions, and using information entropy as the regularization term (Berta, Page 3, section 2, lines 4-5, “In general, the final strong classifier is constructed step by step, by learning a new weak classifier in each iteration based on the current weight distribution of the training examples” Berta, Page 5, section 3, Paragraph 3, line 1, “The motivation to use the entropy as the regularization term”) but does not specifically disclose increasing backpropagation gradients for layers which an information entropy is below a certain threshold.
When taken as a whole, the dependent claims have been found allowable over the prior
art for at least the above features recited in the independent claims upon which they depend.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejections of the claims have been fully considered but are unpersuasive.
Applicant first argues, on page 9, paragraph 3 of the response, that claim 1 does not recite any mathematical concepts. Examiner respectfully disagrees. Applicant references an “obfuscating” step; however, it is unclear which of the recited limitations of claim 1 is being referred to by the applicant. Further, simply performing the identified abstract ideas “on a neural network” or “using a neural network” would be considered generally linking the abstract idea to a particular technology or field of use. Further, simply performing the mathematical calculations on a computer does not render them eligible, see MPEP 2106.05(f). For an in depth analysis of each claim limitation, please refer to the updated 35 U.S.C. 101 rejection seen above.
Applicant next argues, on page 9, final paragraph – page 10, paragraph 1 of the response, that “the determining and increasing limitation in combination of additional elements in the claim integrates the exception into a practical application. Examiner respectfully disagrees. It is worth noting that applicant specifically refers to a “determining” step which has been identified as an abstract idea, and the judicial exception alone cannot provide the improvement, see MPEP 2106.05(a). Further, the remaining limitations pointed to by the applicant, “such that information capacity is increased, accuracy improved, and overfitting is reduced, wherein the trained BNN is used to classify data objects, including image recognition, natural language processing, or trajectory prediction of autonomous driving vehicles” are considered to be merely generally linking the use of the judicial exception with a particular technology or field of use, and as such cannot provide an inventive concept. For an in depth analysis of each claim limitation, please refer to the updated 35 U.S.C. 101 rejection seen above.
Finally, applicant argues, on page 10, final paragraph – page 11, paragraph 1 of the response, in regard to Step 2B of the eligibility analysis, that “claim 1 requires a DP accelerator, which is not conventional computer parts”. Examiner respectfully disagrees. It is noted that the features upon which applicant relies (i.e., “DP accelerator”) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims.
Applicant's arguments regarding the remainder of the claims rely upon the arguments asserted with respect to the independent claims, and are thus unpersuasive.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOLLY CLARKE SIPPEL whose telephone number is (571)272-3270. The examiner can normally be reached Monday - Friday, 7:30 a.m. - 4:30 p.m. ET..
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/M.C.S./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122