DETAILED ACTION
The present application, filed on 6/23/2023 is being examined under the AIA first inventor to file provisions.
The following is a FINAL Office Action in response to Applicant’s amendments filed on 6/3/2026.
a. Claims 1-5, 8-9, 11, 15-18 are amended
b. Claims 13-14, 20 are cancelled
c. Claim 21 is new
Overall, claims 1-12, 15-19, 21 are pending and have been considered below.
Claim Rejections - 35 USC § 101
35 USC 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 1-12, 15-17, 20-21 are rejected under 35 USC 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception, i.e. an abstract idea, not integrated into a practical application, and without significantly more.
Per Step 1 of the multi-step eligibility analysis, claims 1-7 are directed to a computer implemented method, claims 8-12, 21 are directed to computer implemented method, and claims 15-17, 20 are directed to a system.
Thus, on its face, each independent claim and the associated dependent claims are directed to a statutory category of invention.
[INDEPENDENT CLAIMS]
Per Step 2A.1. Independent claim 1, is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 1 recite an abstract idea, shown in bold below:
[A] A method comprising:
[B] receiving user input identifying a deep equilibrium model and identifying a training dataset; and
[C] training the deep equilibrium model on the training dataset,
[D] wherein the training includes performing a normalization method according to: W = W o min (t, f) = W o min (t, g/N(W)),
[x] wherein the training further includes generating a computational graph associated with forward and backward passes through the deep equilibrium model to reduce memory usage during training.
[E] where f is the deep equilibrium model, W is a weight matrix, g is a learnable scaling factor, o is a row-wise multiplication, t is a threshold for clipping the scaling factor g, and N is a computation of a norm for the weight matrix W.
Independent claim 1 recites: receiving user input ([B]); training a deep equilibrium model ([C], [D]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: training a deep equilibrium model, by employing a normalized method along with a training dataset.
This is a combination that, under its broadest reasonable interpretation, covers performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
Accordingly, it is concluded that independent claim 1 recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the qualifiers “where f is the deep equilibrium model, W is a weight matrix, g is a learnable scaling factor, o is a row-wise multiplication, t is a threshold for clipping the scaling factor g, and N is a computation of a norm for the weight matrix W”, as well as “wherein the training further includes generating a computational graph associated with forward and backward passes through the deep equilibrium model to reduce memory usage during training.”, as applied to the training equation, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These additional elements of the independent claims do not preclude from carrying out the identified abstract idea training a deep equilibrium model, by employing a normalized method along with a training dataset, and do not serve to integrate the identified abstract idea into a practical application.
Therefore, the additional claim elements of independent claim 1 do not integrate the identified abstract idea into a practical application and the claims remain a judicial exception.
Per Step 2B. Independent claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claim 1 is deemed ineligible.
Per Step 2A.1. Independent claim 8, is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 8 recite an abstract idea, shown in bold below:
[A] A method:
[B] receiving input identifying a deep equilibrium model, a training dataset and an injection module;
[C] training the deep equilibrium model on the training dataset,
[D] wherein the training includes performing a normalized method according to:
W = W o min (t, f) = W o min (t, g/N(W)),
[E] where f is the deep equilibrium model, W is a weight matrix, g is a learnable scaling factor, is a row-wise multiplication, t is a threshold for clipping the scaling factor g, and N is a computation of a norm for the weight matrix W
[F] wherein the training further includes generating a computational graph associated with forward and backward passes through the deep equilibrium model to reduce memory usage during the training.
Independent claim 8 recites: training a deep equilibrium model ([C]); performing a normalized method ([D]), and generating a computational graph (([F]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: training a deep equilibrium model, by employing a normalized method along with a training dataset.
This is a combination that, under its broadest reasonable interpretation, covers performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
Accordingly, it is concluded that independent claim 8 recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the qualifiers “where f is the deep equilibrium model, W is a weight matrix, g is a learnable scaling factor, is a row-wise multiplication, t is a threshold for clipping the scaling factor g, and N is a computation of a norm for the weight matrix W” as applied to the backward solvers, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These additional elements of the independent claims do not preclude from carrying out the identified abstract idea training a deep equilibrium model, by employing a normalized method along with a training dataset, and do not serve to integrate the identified abstract idea into a practical application.
The additional steps in the independent claims, shown not bolded above, recite: receiving user input identifying a deep equilibrium model, a training dataset and an injection module ([B]). When considered individually, they amount to nothing more than receiving data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset) into a practical application (see MPEP 2106.05(f)(2)).
Therefore, the additional claim elements of independent claim 8 do not integrate the identified abstract idea into a practical application and the claims remain a judicial exception.
Per Step 2B. Independent claim 8 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claim 8 is deemed ineligible.
Per Step 2A.1. Independent claim 15, is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 15 recite an abstract idea, shown in bold below:
[A] A system comprising: one or more processors; and non-transitory memory including processor-executable instructions:
[B] receiving input identifying a deep equilibrium model and
[C] identifying a training dataset; and
[D] training the deep equilibrium model on the training dataset,
wherein the training includes:
[E] performing forward and backward solvers to conduct forward and backward passes through the deep equilibrium model,
[F] wherein the forward and backward solvers are modified by parameters in the input; and
[G] generating a computational graph associated with the forward and backward through the deep equilibrium model to reduce memory usage during training;
[H] performing one or more of the following: automatic normalization of weight tensors; Jacobian regularization; and fixed-point correction.
Independent claim 15 recites: identifying a training data set ([C]); training a deep equilibrium model by performing forward and backward solvers([D], [E]); generating a computational graph ([G]); performing selected training operations ([H]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: training a deep equilibrium model, by employing a normalized method along with a training dataset.
This is a combination that, under its broadest reasonable interpretation, covers performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
Accordingly, it is concluded that independent claim 15 recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “processors,” “memory,” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further, the qualifiers “wherein the forward and backward solvers are identified in the user input” as applied to the forward and backward solvers, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These qualifiers of the independent claims do not preclude from carrying out the identified abstract idea training a deep equilibrium model, by employing a normalized method along with a training dataset, and do not serve to integrate the identified abstract idea into a practical application.
The additional steps in the independent claims, shown not bolded above, recite: receiving user input identifying a deep equilibrium model ([B]). When considered individually, they amount to nothing more than receiving data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset) into a practical application (see MPEP 2106.05(f)(2)).
Therefore, the additional claim elements of independent claim 15 do not integrate the identified abstract idea into a practical application and the claims remain a judicial exception.
Per Step 2B. Independent claim 15 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claim 15 is deemed ineligible.
[DEPENDENT CLAIMS]
Dependent claim 2 (which is representative of claims 11, 16) recites: computational graph is
generated using gradient checkpointing during backpropagation.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: training a deep equilibrium model, by employing a normalized method along with a training dataset. The elements in this dependent claim are comparable to “sorting information” i.e. comparing data, which has been recognized by a controlling court as "well-understood, routine and conventional computing functions" when claimed generically as they are in these dependent claims. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset) into a practical application (see MPEP 2106.05(d) II)).
The dependent claim elements have the same relationship to the underlying abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset).
Therefore, dependent claim 2 (which is representative of claims 11, 16) is deemed ineligible.
Dependent claim 3 (which is representative of claims 12, 17) recites:
identifies a decoder module.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: training a deep equilibrium model, by employing a normalized method along with a training dataset. The elements in this dependent claim are comparable to “sorting information” i.e. comparing data, which has been recognized by a controlling court as "well-understood, routine and conventional computing functions" when claimed generically as they are in these dependent claims. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset) into a practical application (see MPEP 2106.05(d) II)).
The dependent claim elements have the same relationship to the underlying abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset).
Therefore, dependent claim 3 (which is representative of claims 12, 17) is deemed ineligible.
Dependent claim 4 recites:
performing forward and backward solvers to conduct the forward and backward passes through the deep equilibrium model.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: training a deep equilibrium model, by employing a normalized method along with a training dataset. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
The dependent claim elements have the same relationship to the underlying abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset).
Therefore, dependent claim 4 is deemed ineligible.
Dependent claim 6 (which is representative of claims 7, 10) recites:
performing one or more of the following: automatic normalization of weight tensors; Jacobian regularization; and fixed point correction.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: training a deep equilibrium model, by employing a normalized method along with a training dataset. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
The dependent claim elements have the same relationship to the underlying abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset).
Therefore, dependent claim 6 (which is representative of claims 7, 10) is deemed ineligible.
Dependent claims 5, 9, 21 recite:
wherein one or more of the forward and backward solvers are modified by parameters included in the user input.
wherein one or more of the forward and backward solvers are modified by parameters included in the user input.
wherein one or more of the forward and backward solvers are modified by parameters included in the user input.
wherein one or more of the forward and backward solvers are modified by parameters in the input.
These further elements in the dependent claims do not perform any claimed method steps. They describe the nature, structure and/or content of other claim elements – forward and backward solvers – and as such, cannot change the nature of the identified abstract idea (training a deep equilibrium model, by employing a normalized method along with a training dataset), from a judicial exception into eligible subject matter, because they do not represent significantly more (see MPEP 2106.07). The nature, form or structure of the other claim elements themselves do not practically or significantly alter how the identified abstract idea would be performed and do not provide more than a general link to a technological environment.
Therefore, dependent claims 5, 9, 21 are deemed ineligible.
When the dependent claims are considered as a whole, as an ordered combination, the claim elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements, which form the abstract concept, are set forth in the independent claims. The fact that the computing devices and the dependent claims are facilitating the abstract concept is not enough to confer statutory subject matter eligibility, since their individual and combined significance do not transform the identified abstract concept at the core of the claimed invention into eligible subject matter. Therefore, it is concluded that the dependent claims of the instant application, considered individually, or as a as a whole, as an ordered combination, do not amount to significantly more (see MPEP 2106.07(a)II).
In sum, claims 1-12, 15-19, 21 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
US 20220027130 A1 KASHMIRI; Sayyed Mahdi et al. TIME DOMAIN RATIOMETRIC READOUT INTERFACES FOR ANALOG MIXED-SIGNAL IN MEMORY COMPUTE CROSSBAR NETWORKS A circuit configured to compute matrix multiply-and-add calculations that includes a digital-to-time converter configured to receive a digital input and output a signal proportional to the digital input and modulated in time-domain associated with a reference time, a memory including a crossbar network, wherein the memory is configured to receive the time modulated signal from the digital-to-time converter and output a weighted signal scaled in response to network weights of the crossbar network and the time modulated input signal, and an output interface in communication with the crossbar network and configured to receive its weighted output signal and output a digital value proportional to at least the reference time using a time-to-digital converter.
US 20210326663 A1 WINSTON; Ezra et al. SYSTEM AND METHOD OF A MONOTONE OPERATOR NEURAL NETWORK A system for training a neural work that includes an input interface for accessing input data for the neural network and a processor in communication with the input interface. The processor is programmed to receive input at the neural network and output a trained neural networking utilizing a forward prorogation and a backward propagation, wherein the forward propagation includes utilizing a root-finding procedure to identify a fixed point associated with one or more parameters of the neural network, wherein the backward propagation includes identifying a derivative of a loss associated with the parameters of the network.
US 20210383234 A1 BAI; Shaojie et al. SYSTEM AND METHOD FOR MULTISCALE DEEP EQUILIBRIUM MODELS A computer-implemented method for a classification and training a neural network includes receiving input at the neural network, wherein the input includes a plurality of resolution inputs of varying resolutions, outputting a plurality of feature tensors for each corresponding resolution of the plurality of resolution inputs, fusing the plurality of feature tensors utilizing upsampling or down sampling for the vary resolutions, utilizing an equilibrium solver to identify one or more prediction vectors from the plurality of feature tensors, and outputting a loss in response to the one or more prediction vectors.
US 20230101812 A1 FENG; Zhili et al. MONOTONE MEAN-FIELD INFERENCE IN DEEP MARKOV RANDOM FIELDS Methods and systems for inferring data to supplement an input utilizing a neural network, and training such a system, are disclosed. In embodiments, an input is received from a sensor at the neural network. Iterations of approximate probabilities can be determined based on hidden-to-hidden Markov random field (MRF) potentials, observed-to-hidden MRF potentials, and unary MRF potentials. A constant can be identified using a root-finding algorithm. The iterations can continue until convergence. The final iteration of the approximate probability can be used to supplement the input to produce an output.
US 20220027723 A1 KOLTER; Jeremy et al. HARDWARE COMPUTE FABRICS FOR DEEP EQUILIBRIUM MODELS A dynamic equilibrium (DEQ) model circuit includes a first multiplier configured to receive an input, scale the input by a first weight, and output the scaled input, second multiplier configured to receive a root, scale the root by a second weight, and output the scaled root, a summation block configured to combine the scaled input, a bias input, and the scaled root and output a non-linear input, and a first non-linear function configured to receive the non-linear input and output the root, wherein the first weight and second weight are based on a trained DEQ model of a neural network.
US 20220277859 A1 Alesiani; Francesco METHOD AND SYSTEM TO DIFFERENTIATE THROUGH BILEVEL OPTIMIZATION PROBLEMS USING MACHINE LEARNING The present invention provides a method for bilevel optimization using machine learning. The method comprises: obtaining input data associated with the bilevel optimization; determining a solution for the bilevel problem; updating, based on the solution for the bilevel problem, a neural network using one or more intermediate parameters associated with the neural network and the bilevel optimization, wherein the one or more intermediate parameters are based on first output from the neural network and second output from a loss function associated with the neural network, wherein the first output is generated based on inputting the input data into the neural network; and outputting one or more finalized parameters for the bilevel optimization based on a change of the one or more intermediate parameters reaching a pre-determined threshold.
US 20220028444 A1 PAPAGEORGIOU; Efthymios et al. READ ONLY MEMORY ARCHITECTURE FOR ANALOG MATRIX OPERATIONS A read-only memory (ROM) computing unit utilized in matrix operations of a neural network comprising a unit element including one or more connections, wherein a weight associated with the computing unit is responsive to either a connection or lack of connection internal to the unit cell or between the unit element and a wordline and a bitline utilized to form an array of rows and columns in the ROM computing unit, and one or more passive or active electrical elements located in the unit element, wherein the passive or active electrical elements are configured to adjust the weight associated with the compute unit, wherein the ROM computing unit is configured to receive an input and output a value associated with the matrix operation, wherein the value is responsive to the input and weight.
US 11558620 B2 Besenbruch; Chri et al. Image encoding and decoding, video encoding and decoding: methods, systems and training methods Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it using an encoder trained neural network, to produce a y latent representation; (iii) encoding the y latent representation using a hyperencoder trained neural network, to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation using a predetermined entropy parameter to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, using predetermined entropy parameters; (vi) processing the quantized z hyperlatent representation using a hyperdecoder trained neural network to obtain a location entropy parameter μ.sub.y, an entropy scale parameter σ.sub.y, and a context matrix A.sub.y of the y latent representation; (vii) processing the y latent representation, the location entropy parameter μ.sub.y and the context matrix A.sub.y, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream, using the entropy scale parameter σ.sub.y; and (ix) transmitting the bitstreams.
US 11763545 B2 Shelhamer; Evan et al. Generating confidence-adaptive pixel-level predictions utilizing a multi-exit pixel-level prediction neural network The present disclosure relates to systems, methods, and non-transitory computer readable media for efficiently, quickly, and flexibly generating and providing pixel-wise classification predictions utilizing early exit heads of a multi-exit pixel-level prediction neural network. For example, the disclosed systems utilize a multi-exit pixel-level prediction neural network to generate classification predictions for a digital image on the pixel level. The multi-exit pixel-level prediction neural network includes a specialized architecture with early exit heads having unique encoder-decoder architectures for generating pixel-wise classification predictions at different early exit stages. In some embodiments, the disclosed systems implement a spatial confidence-adaptive scheme to mask certain predicted pixels to prevent further processing of the masked pixels and thereby reduce computation.
US 11755890 B2 Kumar; Suhas et al. Local training of neural networks A method for performing learning is described. A free inference is performed on a learning network for input signals. The input signals correspond to target output signals. The learning network includes inputs that receive the input signals, neurons, weights interconnecting the neurons, and outputs. The learning network is described by an energy for the free inference. The energy includes an interaction term corresponding to interactions consisting of neuron pair interactions. The free inference results in output signals. A first portion of the plurality of weights corresponding to data flow for the free inference. A biased inference is performed on the learning network by providing the input signals to the inputs and bias signals to the outputs. The bias signals are based on the target output signals and the output signals. The bias signals are feedback to the learning network through a second portion of the weights corresponding to a transpose of the first portion of the weights. At locations in the learning network, learning network equilibrium states are determined for the biased inference. The weights are updated based on the learning network equilibrium states.
Response to Amendments/Arguments
Applicant’s submitted remarks and arguments have been fully considered.
Applicant disagrees with the Office Action conclusions and asserts that the presented claims fully comply with the requirements of 35 U.S.C. § 101 regrading judicial exceptions. Further, Applicant is of the opinion that the prior art fails to teach Applicant’s invention.
Examiner respectfully disagrees in both regards.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 101.
Applicant submits:
a. The pending claims are not directed to an abstract idea.
b. The identified abstract idea is integrated into a practical application.
c. The pending claims amount to significantly more.
Furthermore, Applicant asserts that the Office has failed to meet its burden to identify the abstract idea and to establish that the identified abstract idea is not integrated into a practical application and that the pending claims do not amount to significantly more.
Examiner responds – The arguments have been considered in light of Applicants’ amendments to the claims. The arguments ARE NOT PERSUASIVE. Therefore, the rejection is maintained.
The pending claims, as a whole, are directed to an abstract idea not integrated into a practical application. This is because (1) they do not effect improvements to the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05 (a)); (2) they do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or a medical condition (see the Vanda memo); (3) they do not apply the abstract idea with, or by use of, a particular machine (see MPEP 2106.05 (b)); (4) they do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05 (c)); (5) they do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the identified abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designated to monopolize the exception (see MPEP 2106.05 (e) and the Vanda memo).
In addition, the pending claims do not amount to significantly more than the abstract idea itself.
As such, the pending claims, when considered as a whole, are directed to an abstract idea not integrated into a practical application and not amounting to significantly more.
More specific:
Applicant submits “Pending claims 1-12 and 15-19 and 21 recite a technological process for generating a computational graph associated with forward and backward passes through the deep equilibrium model by reducing memory during training.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
“generating a computational map” is not an eligibility criterion (see MPEP 2106.05.04-07)
Thus, the rejection is proper and has been maintained.
Applicant submits “These recitations improve the functioning of a computer by reducing memory overhead during training.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
It appears that applicant refers top the provisions of MPEP 2106.05(a).
MPEP 2106.04(d)(1) discloses:
An important consideration to evaluate when determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology .... In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art .... Second, if the specification sets forth an improvement in technology. the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. (Emphasis added)
That is, the claimed invention may integrate the judicial exception into a practical application by demonstrating that it improves the relevant existing technology although it may not be an improvement over well-understood, routine, conventional activity. (Emphasis added)
Thus, the rejection is proper and has been maintained.
Applicant submits “These additional elements impose meaningful limits on any alleged judicial exception and apply any mathematical concepts in a particular technological environment to improve the operation of a computer system.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
Based on MPEP 2106.05(e), the test is not if the limitations are meaningful, but if the identified judicial exception is applied or used “in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designated to monopolize the exception” (see MPEP 2106.05(e) and the Vanda Memo). In the instant situation, the steps of: receiving user input identifying a deep equilibrium model and identifying a training dataset; and training the deep equilibrium model on the training dataset, Examiner argues that while the steps in themselves surely are meaningful, however, the activity they describe does not help applying the judicial exception in a meaningful way.
Thus, the rejection is proper and has been maintained.
Applicant submits “These features provide a specific technological improvement in the operation of machine learning systems by reducing memory requirements during training and enabling efficient training of deep equilibrium models. The claimed combination therefore amounts to significantly more than any alleged abstract idea and supplies an inventive concept sufficient to satisfy Step 2B.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
The eligibility analysis in the instant office Action concludes at Step 2B:
Per Step 2B. Independent claims 1, 8, 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claim 1, 8, 15 is deemed ineligible.
Thus, the rejection is proper and has been maintained.
It follows from the above that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. Therefore, the rejection under 35 U.S.C. § 101 is maintained.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 103.
The rejection is withdrawn, as a result of the amendments.
The identified prior art doe not disclose:
W= W o min (t, f) = W o min (t, g/N(W))
wherein the training further includes generating a computational graph associated with
forward and backward passes through the deep equilibrium model
Examiner has reviewed and considered all of Applicant’s remarks. The rejection is maintained, necessitated by the fact that the rejection of the claims under 35 USC § 101 has not been overcome.
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Radu Andrei/
Primary Examiner, AU 3697
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