FINAL REJECTION, SECOND DETAILED ACTION
Status of Prosecution
The present application 17/744,062, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The application was filed in the Office on May 13, 2022 and claims foreign priority to French application FR2105613, filed on May 28, 2021.
The Office mailed a first detailed action, non-final rejection on January 1, 2026.
Applicant filed amendments and arguments on April 13, 2026.
Claims 1-20 are pending and are all rejected in this rejection. Claims 1, 8 and 15 are independent.
Status of Claims
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 6-8, 13-15 and 19-20 are rejected under 35 USC § 103 as being unpatentable over Baluja et al. (“Baluja”), United States Patent Application Publication 2021/0209475, published on July 8, 2021.
Claims 2-3, 9-10 and 16-17 are rejected under 35 USC § 103 as being unpatentable over Baluja et al. (“Baluja”), United States Patent Application Publication 2021/0209475, published on July 8, 2021 in view of Choi et al. (“Choi”) United States Patent Application Publication 2018/0107925, published on Apr. 19, 2018.
Claims 4-5, 11-12 and 18 are rejected under 35 USC § 103 as being unpatentable over Baluja et al. (“Baluja”), United States Patent Application Publication 2021/0209475, published on July 8, 2021.
Response to Remarks and Arguments
Examiner thanks Applicant for the submitted remarks and arguments.
First regarding claims 7, 14 and 20 which were rejected for indefiniteness under 35 USC S § 112(b). Finding the amendments sufficient to traverse, Examiner withdraws this rejection.
Turning to the § 101 subject matter eligibility rejection, Examiner has reviewed the arguments made. First, Applicant contends that Applicant’s representative claim 1 is integrated into a practical application of neural network execution on a processor (Remarks: p. 10). Applicant points to the specification’s statements as to the purported practical application and states that the amended, “executing step is specifically configured to address the technical challenge of reducing computational load during neural network execution on a processor.” (Remarks: p. 11). But as the recited claim is one of computing, that is an identified judicial exception alone cannot provide the improvement. MPEP 2106.05(a), USPTO Memorandum. Aug. 4, 2025, as cited by Applicant, (“Reminder Memo”) at p. 4. Applicant’s additional element of providing the computing (the judicial exception) on a processor, is merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. MPEP 2106.05(f).
Examiner is not persuaded on this point.
Applicant is reminder that,” [i]f the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology.” MPEP 2106.05(a).
Next Applicant argues that Applicant’s claim cannot be practically performed in the human mind (Remarks: p. 12). As the Office Action does not utilize this analysis, Examiner declines to respond to these arguments as they were not initially presented by in the rejection.
As to the remaining arguments from the Reminder Memo, Examiner is not persuaded.
Finally, regarding the prior art rejections, Examiner has adjusted the rejections below. As to Applicant’s remarks that the use of the background section of the primary reference is detrimental to the rejection, Examiner notes that all parts of a reference may be used for its teachings. "The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain." In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
The Claims stand rejected.
Claim Rejections – 35 USC § 101, Subject Matter Eligibility
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding representative claim 1, at step 1, the claim recites a method, a process, which is a statutory category of invention. See MPEP § 2106.03.
At step 2A, prong one, the claim recites the apparatus capable of performing a method that determines aggregate data, calculates a value of a predetermine objective function using the aggregate data and a predetermined objective function with respect to a parameter and updating the parameter.
The limitations:
forming clusters of trained weights of the weight applied connection for each input of each layer of the trained artificial neural network;
computing a representative weight for each formed cluster; and
replacing the trained weights of the weight applied connection for each cluster with the representative weight to form the simplified artificial neural network.
computing weighted inputs by multiplying the input by the representative weights of connections connected to that input.
are each processes or steps, that under a broadest reasonable interpretation, are the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Therefore, the claim recites at least an abstract idea per this part of the analysis.
At step 2A prong two, the claim language is analyzed to determine whether it recites additional elements that integrate the judicial exception into a practical application. See MPEP § 2106.04(d).
The claim element, “[a] method for generating a simplified artificial neural network from a trained artificial neural network comprising layers of neurons, each layer having at least one input, and each input coupled to at least one neuron of the layer by a weight applied connection,” are additional elements that generally links the use of the judicial exception to a particular technological environment or field of use, specifically neural networks. See MPEP §§ 2106.04(d), 2106.05(h).
The claim element, “executing the simplified artificial neural network on a processor by, for each input of each layer,” for the computing step above.
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. The claim is therefore directed to an abstract idea.
Next, at step 2B of the analysis, the claim is considered if it recites additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05. The element of using a processor to execute the simplified artificial neural network for the computing step is also simply including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to nothing more than linking the use of the judicial exception to a particular technological environment or field of use, neural networks. See MPEP § 2106.05(h).
Therefore, claim 1 is ineligible.
As to dependent claims 2-4, the analysis of the parent claim is incorporated.
In the step 2A, prong one analysis, the additional limitations are under a broadest reasonable interpretation, also the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
The claim is therefore also ineligible.
As to dependent claim 5, the analysis of the parent claim is incorporated.
In the step 2A, prong two analysis, the additional limitation of, “wherein the simplified artificial neural network is executed by an embedded system,” is a step or process that, under its broadest reasonable interpretation, is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, neural network environments. See MPEP §§ 2106.04(d), 2106.05(h). Correspondingly, in the step 2B analysis, the limitation amount to nothing more than linking the use of the judicial exception to a particular technological environment or field of use, specifically neural network environments. See MPEP § 2106.05(h).
The claim is therefore also ineligible.
As to dependent claims 6-7, the analysis of the parent claim is incorporated.
In the step 2A, prong one analysis, the additional limitations of, “wherein the trained weights are determined through a training phase of the trained artificial neural network” and “wherein the simplified artificial neural network includes less trained weights than the trained artificial neural network,” merely further limits the abstract ideas that remain, under a broadest reasonable interpretation, are the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
The claim is therefore also ineligible.
Claim 8 is substantively similar in analysis as above. The only differences, such as step 1 analysis of the statutory class are statutory (a material or manufacture), but still fail in the rest of the analysis. This claim is also therefore ineligible.
Claims 9-14 are mere recitations of dependent claims 2-7 and the similar analysis is incorporated. These claims are also therefore ineligible.
Claim 15 is substantively similar in analysis as above. The only differences, such as step 1 analysis of the statutory class are statutory (a material or manufacture), but still fail in the rest of the analysis. This claim is also therefore ineligible.
Claims 16-20 are mere recitations of dependent claims 2-7 and the similar analysis is incorporated. These claims are also therefore ineligible.
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 of this title, 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.
A.
Claims 1, 6-8, 13-15 and 19-20 are rejected under 35 USC § 103 as being unpatentable over Baluja et al. (“Baluja”), United States Patent Application Publication 2021/0209475, published on July 8, 2021.
As to Claim 1, Baluja teaches: A method for generating a simplified artificial neural network from a trained artificial neural network comprising layers of neurons, each layer having at least one input, and each input coupled to at least one neuron of the layer by a weight applied connection (Baluja: par. 0003, a neural network can include a group of connected nodes arranged in one or more layers and can be connected with weights associated with each connection (an edge)), the method comprising:
forming clusters of trained weights of the weight applied connection for each input of each layer of the trained artificial neural network (Baluja: Fig. 3, [306] par. 0096, clusters are formed);
computing a representative weight for each formed cluster (Baluja: par. 0096, [308], a representative weight, such as the mean or median of the weights assigned to a cluster may be used as the representative weight); and
replacing the trained weights of the weight applied connection for each cluster with the representative weight to form the simplified artificial neural network (Baluja: par. 0097, at [310] the weights are replaced accordingly).
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Baluja may not explicitly teach: executing the simplified artificial neural network on a processor by, for each input of each layer, computing weighted inputs by multiplying the input by the representative weights of connections connected to that input.
Baluja however does disclose that the typical process is to multiply the input by the weight corresponding to the edge (Baluja: par. 0022).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified Baluja by utilizing the multiplication means as taught by the prior art as mentioned by Baluja. Such a person would have been motivated to do so with a reasonable expectation of success as a design choice or if a lookup table is not practicable.
As to Claim 6, Baluja teaches or suggests the elements of claim 1.
Baluja further teaches: wherein the trained weights are determined through a training phase of the trained artificial neural network (Baluja: Fig. 3, [302], performa number of training iterations on a neural network).
As to Claim 7, Baluja teaches or suggests the elements of claim 1.
Baluja further teaches: wherein the simplified artificial neural network includes fewer trained weights than the trained artificial neural network (Examiner asserts that a compressed network will have fewer nodes and thus fewer trained weights).
As to Claim 8, it is rejected for similar reasons as claim 1. Baluja further teaches a computer readable medium with instructions (Baluja: par. 0067).
As to Claim 13, it is rejected for similar reasons as claim 6.
As to Claim 14, it is rejected for similar reasons as claim 7.
As to Claim 15, it is rejected for similar reasons as claims 1 and 8. Baluja further teaches a processor(Baluja: par. 0067).
As to Claim 19, it is rejected for similar reasons as claim 6.
As to Claim 20, it is rejected for similar reasons as claim 7.
B.
Claims 2-3, 9-10 and 16-17 are rejected under 35 USC § 103 as being unpatentable over Baluja et al. (“Baluja”), United States Patent Application Publication 2021/0209475, published on July 8, 2021 in view of Choi et al. (“Choi”) United States Patent Application Publication 2018/0107925, published on Apr. 19, 2018.
As to Claim 2, Baluja teaches the limitations of claim 1.
Baluja may not explicitly teach: forming sets of clusters of trained weights of the weight applied connection for each input of each layer of the trained artificial neural network;
computing a representative weight for each formed cluster of each set of clusters of trained weights;
selecting a subset of the set of clusters of trained weights such that a minimum cost function is achieved by replacing the trained weights of the weight applied connection by the representative weight; and
replacing the trained weights of the weight applied connection for each cluster of the selected subset with the representative weight to form the simplified artificial neural network.
Baluja does however teach that the clustering may be repeated again iteratively (Baluja: par. 0098). Choi teaches in general concepts related to performing network parameter quantization in deep neural networks (Choi: Abstract). Specifically, Choi teaches that an objective function may be used to minimize the loss for selecting how weights are to be adjusted for simplifying the network (Choi: pars. 0054-56). Equations 5(a) and 5(b) are resulting k-means clustering minimization of quantization error (Choi: par. 0059).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Baluja disclosures and teachings by performing the selection of the clustered weights with a minimized cost approach as taught and suggested by Choi. Such a person would have been motivated to do so with a reasonable expectation of success to allow for quantization techniques that allows recovery of original performance (Choi: par. 0043).
As to Claim 3, Baluja and Choi teach the limitations of claim 2.
Choi further teaches: wherein the subset of the clusters of trained weights is selected in accordance with the formula
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, wherein Si are the sets of clusters, Sik are the subset of the set of clusters, K is the number of the subset of the set of clusters Si,k of the sets of clusters Si, wi,j are the trained weights of the layer, the clusters of a set comprising L trained weights in total, w-bar i, k is the representative weight for the trained weights of the subset of the set of clusters Si,k, and
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for j
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{1,...,L} are the partial gradients of the cost function with respect to the trained weights wi,j of the layer (Choi: Eq. 8(a), par. 0084, in essence is the cost function, utilizing a Hess0an-weighted k-means clustering minimization quantization loss function).
As to Claim 9, it is rejected for similar reasons as claim 2.
As to Claim 10, it is rejected for similar reasons as claim 3.
As to Claim 16, it is rejected for similar reasons as claim 2.
As to Claim 17, it is rejected for similar reasons as claim 3.
C.
Claims 4-5, 11-12 and 18 are rejected under 35 USC § 103 as being unpatentable over Baluja et al. (“Baluja”), United States Patent Application Publication 2021/0209475, published on July 8, 2021.
As to Claim 4, Baluja teaches the elements of claim 1.
Baluja further teaches: computing weighted inputs for each input of the layer, (Baluja: Fig. 4, [404], par. 0104, using a table lookup, the result value for the weight input is determined);
computing an accumulated value corresponding to the sum of the weighted inputs and bias connections of a neuron connected to the input (Baluja: Fig. 4, [408], par. 0105, the sum is result values identified in [406]); and
computing an output value of the neuron by passing the accumulated value in an activation function of the neuron (Baluja: Fig. 4, [406], par. 0106).
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Baluja may not explicitly teach: the computing comprising multiplying the input by representative weights corresponding to connections connected to the input.
Baluja however does disclose that the typical process is to multiply the input by the weight corresponding to the edge (Baluja: par. 0022).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified Baluja by utilizing the multiplication means as taught by the prior art as mentioned by Baluja. Such a person would have been motivated to do so with a reasonable expectation of success as a design choice or if a lookup table is not practicable.
As to Claim 5, Baluja teaches the elements of claim 4.
Baluja further teaches: wherein the simplified artificial neural network is executed by an embedded system (Baluja: par. 0066, computing device [102] may be an embedded device).
As to Claim 11, it is rejected for similar reasons as claim 2.
As to Claim 12, it is rejected for similar reasons as claim 3.
As to Claim 18, it is rejected for similar reasons as claim 2.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Aravamudan et al., US Patent Application Publication 2018/0082197 (Mar. 22, 2018) (describing knowledge bases identified by generating semantic associations);
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES T TSAI whose telephone number is (571)270-3916. The examiner can normally be reached M-F 8-5 Eastern.
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, Viker Lamardo can be reached on 571-270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES T TSAI/ Primary Examiner, Art Unit 2147
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