DETAILED ACTION
This Office Action is sent in response to the Applicant’s Communication received on 08/06/2025 for application number 17/572,740. The Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration, IDS, and Claims.
Claims 1, 9, 14, and 15 are amended.
Claims 16-19 are new.
Claims 1-19 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
35 USC 101
The Applicant argues that amended claims require to analyze runtime data sources to identify different parts of a computation graph that are dependent and independent of input data, which is a step that cannot be practically performed in the human mind and should not be directed to a mental process. Additionally, Applicant further states that the human mind cannot analyze runtime data sources, split a computation graph, nor generate and apply a wrapper that performs a transparent mapping of a neural network.
The Examiner respectfully disagrees. Analyzing runtime data sources and splitting the computation graph are limitations that are recited in a high level of generality, without further defining the steps of how analyzing and splitting are explicitly performed. Therefore, analyzing runtime data source and splitting the computation graph are actions that can be reasonably performed mentally with the aid of pen and paper, and are therefore mental processes. Furthermore, the claimed limitation “generating and applying a wrapper that performs a transparent mapping of a neural network” is not an abstract idea, but rather an additional element analyzed under 2A prong 2 and 2B, mere instructions to implement an abstract idea on a computer recited at a high level of generality (MPEP 2106.05(f)).
Applicant further argues that the wrapper is fundamentally rooted in computer technology to perform particular computer functions, and that these features cannot be considered mere instructions to implement an abstract idea on a computer.
The Examiner respectfully disagrees. The claimed limitation “generating and applying a wrapper that performs a transparent mapping of a neural network,” although rooted in computer technology, is an additional element analyzed under 2A prong 2 and 2B, mere instructions to implement an abstract idea on a computer recited at a high level of generality (MPEP 2106.05(f)). The alleged argument that the wrapper being “particular computer functions” are recited in a high level of generality and therefore are considered mere instructions to implement an abstract idea on a computer.
The Applicant further argues that claims 1-15 recite features which, when considered as a whole, provide for improvements over existing technology by “enabling the pre-evaluable layers to be executed only once and not within every iteration along with saving computational resources, costs, and accuracy, while providing for additional computations to be performed” in order to reduce execution time, as well as “enabling the transparent reconfiguration of the number, shape, padding, data type and data layout of the parameters within the neural network” in order to improve computational runtime without risking a loss of accuracy. Additionally, Applicant cites paragraphs 0014, 0049, and 0050 to be the support for the alleged improvement steps.
The Examiner respectfully finds the Applicant’s argument unpersuasive. The Applicant’s alleged “enabling the pre-evaluable layers to be executed only once and not within every iteration” and “enabling the transparent reconfiguration of the number, shape, padding, data type and data layout of the parameters within the neural network” appear to be disclosed invention. However, such disclosed invention as improvements to the computers of Al frameworks are not explicitly reflected in the claimed invention. Although, instant application paragraphs 0014, 0049, and 0050 have detailed descriptions of implementing steps that provide technological improvement, these details are not explicitly present in the current claim. Specifically, epochs * len(dataset) iterations of the model, moving all layers that are not dependent on runtime input data into the partial evaluation graph, and hiding changes to the neural network from the user, are not explicitly stated in the claim. Therefore, the claimed elements or combination of elements do not reflect a technical improvement as alleged.
Finally, Applicant argues that embodiments of the present invention provide an unconventional solution to overcome the technical problem of AI frameworks only being able to support generic data layouts and require data conversion to a different layout, before the execution of each layer, by identifying and splitting a computation graph into a pre- evaluation part independent of the input data, and into a computation part dependent on the input data, and then generating and applying a wrapper that performs a transparent mapping of data layouts of the pre-evaluation part, as recited in the claims. This results in the technical improvements of reducing execution time and saving computational resources (without any negative impact on accuracy or peak memory consumption) and enabling transparent reconfiguration of neural network parameters, among other technical improvements discussed above. See paragraphs [0014] and [0049] of the published application.
Examiner respectfully disagrees. The specific steps for reducing execution time and saving computational resources without any negative impact on accuracy or peak memory consumption, and their improvement to computers of Al frameworks, are not explicitly reflected in the claimed invention. Although, instant application paragraphs 0014, 0049, and 0050 have detailed descriptions of implementing steps that provide technological improvement, these details are not explicitly present in the current claim. Specifically, epochs * len(dataset) iterations of the model, moving all layers that are not dependent on runtime input data into the partial evaluation graph, and hiding changes to the neural network from the user, are not explicitly stated in the claim. Therefore, the claimed elements or combination of elements do not reflect a technical improvement as alleged.
For at least the above reasons, it is respectfully submitted that claims 1-15 are not directed to patent-eligible subject matter pursuant to the Subject Matter Eligibility Test.
35 USC 103
Applicant argues that Yang and Zishan, alone or in combination, fail to disclose or suggest identifying parts of a computation graph of a neural network that depend on input data and parts that are independent of the input data, much less splitting the computation graph into these parts as claimed.
Examiner respectfully disagrees, Yang and Zishan do indeed teach the cited limitation. Specifically, under the Broadest Reasonable Interpretation (BRI), Yang teaches identifying parts of the neural network that depend on input data, when Yang teaches in Col 13, lines 47-50: adaptor module…may determine kernel size information tailored or, alternatively, optimized for the NPU based on static and/or dynamic information regarding the NPU. Yang also teaches parts that are independent of the input data in Col 13, lines 41-43: an input neural network model performs an operation involving a kernel having a size of 5*5. Additionally, Yang teaches in Col 13, lines 44-47: A neural network adaptor module may parse information regarding the N*N kernel structure from the input neural network model. The initial kernel size 5*5, prior to optimization, is independent of the parsed information. Furthermore, in regards to splitting the computation graph, Zishan teaches, splitting the computation graph into the pre-evaluation part and the computation part in Para 0144: the preset operators in the first computational graph are traversed, and each preset operator is… converted into a target operator subgraph.
Applicant further argues that merely changing the kernel size of the neural network model does not involve differentiating between parts of a computation graph dependent on input data and parts independent of input data, much less splitting a computation graph based thereon. Therefore, Yang fails to disclose any dependency of any part of the kernel structure on the input data of the neural network.
Examiner respectfully disagrees. “-differentiating between parts of a computation graph dependent on input data and parts independent of input data” is not part of the explicit claimed language. The amended claim states “analyzing runtime data sources to identify parts… of the neural network that depend on input data as a computation part,” which is met by Yang Col 12, lines 42-49: Referring to FIG. 9, a neural network model is received by HW which will execute a neural network operation in operation S31. The HW may perform an operation involving a kernel having a first size according to the neural network model in operation S32. The neural network adaptor module may receive dynamic information during runtime in operation S33 and may determine whether the HW executing the neural network model is changed in operation S34. Additionally, Yang Col 13, lines 47-50: adaptor module…may determine kernel size information tailored or, alternatively, optimized for the NPU based on static and/or dynamic information regarding the NPU. In other words, kernel size (parts of the neural network) is determined in the adaptor module (computation part) when dynamic information (input data based on runtime data sources) is received. Additionally, Zishan was brought in to teach identifying parts of a computation graph as a computation part. In this regard, Zishan teaches, in paragraph 0142: preset operators represented by a directed acyclic graph data structure according to the parsing results (identifying parts of a computation graph) provided by the model parsing module. Additionally, paragraph 0144, Zishan teaches: the preset operators in the first computational graph are traversed, and each preset operator is… converted into a target operator subgraph (computation part).
Applicant further argues that Zishan discloses single target operators and preset operators. Zishan does not disclose or suggest that the single target operators depend on the input data of the model as asserted by the Office. Nor does Zishan disclose or suggest that the preset operators are independent of the input data. Rather, Zishan teaches that each preset operator is converted into a single target operator according to the architecture of the processor. The resulting target operators are also not indicated as having dependence on the input data or not.
Examiner respectfully disagrees with the argument. “Depend on the input data” and “independent of the input data” are not taught by Zishan, but rather taught by Yang. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant further argues that the 'parameters' asserted to be taught by Yang are in reference to hyper parameters that impact how a computation is executed and how weights are shaped (e.g., options that influence the behavior of a layer, such as what kind of activation function is applied, or the kernel size, stride or dilation of a pooling or convolutions) in contrast to model parameters (e.g., the 'knowledge of the model, the numerical values that actually used in the computation). Therefore, Applicant respectfully further submits that Yang fails to disclose or suggest the features of independent claims 1, 14, and 15 recited above.
Examiner respectfully disagrees. First, 'parameters' are not part of the amended claims 1, 14, and 15. Second, model parameters being the knowledge of the model and the numerical values that actually used in the computation are not part of the claimed language. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant further argues that Zishan fails to disclose splitting a computation graph into a pre- evaluation part and a computation part. Rather, Zishan merely discloses to convert the preset operators into the single target operators, such that the preset operators (the asserted pre- evaluation part) and the single target operators (the asserted computation part) are not both split from a same computation graph. Merely converting the preset operators into target operators does not involve any splitting of a computation graph. Therefore, Applicant respectfully submits that Zishan fails to disclose the features of independent claims 1, 14, and 15 recited above.
Examiner respectfully disagrees. Zishan does indeed teach the claimed limitations. Specifically, Zishan teaches in paragraph 0144: the preset operators (pre- evaluation part) in the first computational graph (computation graph) are traversed, and each preset operator is… converted into (split) a target operator subgraph (computation part).
Applicant further argues that the performance of the dedicated processor as the "parameter ... depend[ent] on input data" such that the kernel size is the input data. See the Detailed Action, page 28. However, the Office's later assertion that the kernel size is a parameter independent of input data (see the Detailed Action, page 29) results in the Office asserting on the hand that the kernel size is the input data and on the other hand the kernel size is the parameters that are independent of the input data. Because the kernel size cannot be both the input data and parameters that are independent of the input data, Applicant respectfully submits that this is an improper reading of Yang.
Examiner respectfully disagrees. In regards to the claimed limitation “parts of the neural network that depend on input data,” Yang teaches in Col 12, lines 42-49: The neural network adaptor module may receive dynamic information (input data) during runtime in operation S33 and may determine whether the HW executing the neural network model is changed in operation S34. Furthermore, in Col 13, lines 35-37, Yang teaches: when N has a particular value in an N*N kernel structure, the NPU may operate at improved or, alternatively, optimal performance. In detail, when N is a multiple of 4 or a value a little less than a multiple of 4 in the N*N kernel structure, the NPU may operate at improved or, alternatively, optimal performance. Additionally, in Col 13, lines 47-50, Yang teaches adaptor module…may determine kernel size (parts of the neural network) information tailored or, alternatively, optimized for the NPU based on (depend on) static and/or dynamic information regarding the NPU. In regards to the claimed limitation “parts that are independent of the input data,” Yang teaches in Col 13, lines 41-43: an input neural network model performs an operation involving a kernel having a size of 5*5. Furthermore, in Col 13, lines 44-47, Yang teaches: A neural network adaptor module may parse information regarding the N*N kernel structure from the input neural network model. Therefore, the initial kernel size 5*5, prior to optimization, is independent of the parsed information.
Applicant further argues that Zishan’s preset operators disclose the claimed pre-evaluation part and Zishan's single target operators disclose the claimed computation part. However, Zishan's preset operators are converted into the single target operators, meaning that the parameters asserted in the Office Action as being dependent on the input data (single target operators) are converted from the parameters asserted in the Office Action as being independent of the input data (present operators), which Applicant respectfully submits is an improper reading of Zishan. Therefore, Applicant respectfully submits that because the Office's interpretations of both Yang and Zishan are improper, and because Yang and Zishan fail to disclose the features of claim 1 discussed above, both Yang and Zishan, alone or in combination, fail to disclose or suggest the features of the independent claims.
Examiner respectfully disagrees. The claimed limitation “parts of a computation graph of the neural network that depend on input data as a computation part, and parts of the computation graph that are independent of the input data as a pre- evaluation part” are taught by combination of Yang and Zishan, not Zishan alone. Specifically, Yang teaches parts of the neural network that depend on input data as a computation part and parts that are independent of the input data as a pre-evaluation part, while Zishan teaches parameters of a computation graph as a computation part and pre-evaluation part. Therefore, applicant’s alleged argument related to data being “dependent” or “independent” of input data is taught by Yang, not Zishan. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In page 9 of the Remarks section, the Applicant states that dependent claim 6 recites features which further distinguish over the cited prior art. Claim 6 recites:
performing the transparent mapping of data layouts of the pre-evaluation part;
executing the neural network; and applying a gradient update to the transparently mapped data layout of the pre- evaluation part. For example, when training the model, the user expects the gradient update not in the precomputed data layouts, but in the original data layout, and reversing the pre-evaluation graph for the gradients satisfies this expectation. Accordingly, the wrapper allows for the abstraction of the automatic optimization and seamlessly integrates it into the code of the user, so the user does not need to manually modify their code to match. In contrast to the claimed features above, (1) Yang's disclosure of reshaping a neural network simply fails to disclose a gradient update, as Yang is silent as to updating any gradients and (2) Yang fails to disclose which part of the input neural network model is reshaped, much less that the reshaping is done to the pre-evaluation part of the input neural network model.
Examiner respectfully disagrees. First, it is to be noted that the claimed limitation states “applying a gradient update to the transparently mapped data layout of the pre- evaluation part,” which is not the same as the Applicant’s alleged argument “updating any gradients.” Yang discloses in Col 9-10, lines 66-1: information regarding an accuracy loss tolerance (a gradient) during execution of an application may be provided (update) to the reshaper module 330 (the transparently mapped data layout of the pre- evaluation part). Second, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., (I) when training the model, the user expects the gradient update not in the precomputed data layouts, but in the original data layout, and reversing the pre-evaluation graph for the gradients satisfies this expectation. Accordingly, the wrapper allows for the abstraction of the automatic optimization and seamlessly integrates it into the code of the user, so the user does not need to manually modify their code to match. (II) part of the input neural network model is reshaped, that the reshaping is done to the pre-evaluation part of the input neural network model) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In pages 9-10 of the remarks section, Applicant states that dependent claim 7 also recites further features which further distinguish over the cited prior art. Claim 7 recites: performing the transparent mapping of data layouts of the pre-evaluation part; receiving a request to export the neural network from a current data layout to a subsequent data layout; and executing the transparent mapping of data layouts of the pre-evaluation part backwards. The Office asserts that Dennison discloses the features of "executing the transparent mapping of data layouts of the pre-evaluation part backwards." However, Dennison performs batch normalization in a distributed environment. Batch normalization training requires computing the mean and variance across all samples, which when distributed, means that all parallel computers need to synchronize their results to compute the correct values, but when speculative, means the normalization does not exactly compute these values, but rather guesses at a "speculative" mean and variance. Applicant respectfully submits that this normalization does not include converting the gradients from optimal data layout to original data layout, as claimed according to above-recited features of claim 7.
Examiner respectfully disagrees. First, Dennison was brought in only to teach the limitation executing data layouts backwards. In contrast, the limitation “the transparent mapping of data layouts of the pre-evaluation part” is taught by Yang in Col 13, lines 56-67 (see claim 1). In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Furthermore, Applicant’s alleged “converting the gradients from optimal data layout to original data layout” is not recited in claim 7. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-13 are directed towards a method. Claim 14 is directed towards a system. Claim 15 is directed towards a tangible, non-transitory computer-readable medium. Therefore, all claims are directed towards one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1:
Claim 1 recites:
“analyzing runtime data sources to identify parts of a computation graph of the neural network that depend on input data as a computation part, and parts of the computation graph that are independent of the input data as a pre-evaluation part;” Analyzing runtime data sources to identify parts graph of the neural network that depend on input data as a computation part, and parts of the computation graph that are independent of the input data as a pre-evaluation part is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“splitting the computation graph into the pre-evaluation part and the computation part;” Splitting the computation graph into the pre-evaluation part and the computation part is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“generating and applying a wrapper that performs a transparent mapping of data layouts of the pre-evaluation part;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“generating and applying a wrapper that performs a transparent mapping of data layouts of the pre-evaluation part;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 2
Step 2A Prong 1:
Claim 2 recites:
“computes the transparent mapping between a default artificial intelligence (AI) framework layout and a compute library layout of the neural network;” Computing the transparent mapping between a default artificial intelligence (AI) framework layout and a compute library layout of the neural network is a claim that merely uses textual replacements for particular equations, and is therefore a mathematical concept.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“generates code implementing the transparent mapping between the default Al framework layout and the compute library layout;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
“generates a new neural network from the neural network by injecting the code into an execution of the neural network;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“generates code implementing the transparent mapping between the default Al framework layout and the compute library layout;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
“generates a new neural network from the neural network by injecting the code into an execution of the neural network;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 3
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“executing the new neural network;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“executing the new neural network;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 4
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“Exporting and Deploying the neural network;” Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)).
“Storing the neural network;” This limitation is merely a post-solution step of storing the data—a nominal addition to the claim that does not meaningfully limit the claim. The method storing is recited at a high level of generality. Simply implementing the abstract idea in a generic method is not a practical application of the abstract idea. Therefore, storing step is an insignificant extra-solution activity. See MPEP 2106.05(g).
“reversing, by the wrapper, the transparent mapping back to the default Al framework layout;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“Exporting and Deploying the neural network;” Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)). This falls under Well-Understood, Routine, Conventional activity -see MPEP 2106.05(d)(II)(vi).
“Storing the neural network;” These elements amount to storing… information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; See MPEP 2106.05(d) (II)(iv). The courts have recognized the computer functions of storing as well‐understood, routine, and conventional function when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
“reversing, by the wrapper, the transparent mapping back to the default Al framework layout;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 5
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“the transparent mapping of data layouts of the pre- evaluation part includes a parameter update;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“the transparent mapping of data layouts of the pre- evaluation part includes a parameter update;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 6
Step 2A Prong 1:
Claim 1 recites:
“performing the transparent mapping of data layouts of the pre-evaluation part;” Performing the transparent mapping of data layouts of the pre-evaluation part is a claim that merely uses textual replacements for particular equations, and is therefore a mathematical concept.
“applying a gradient update to the transparently mapped data layout of the pre- evaluation part;” Applying a gradient update to the transparently mapped data layout of the pre- evaluation part is a claim that merely uses textual replacements for particular equations, and is therefore a mathematical concept.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“executing the neural network;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“executing the neural network;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 7
Step 2A Prong One
Claim 7 recites:
“performing the transparent mapping of data layouts of the pre-evaluation part;” Performing the transparent mapping of data layouts of the pre-evaluation part is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“executing the transparent mapping of data layouts of the pre-evaluation part backwards;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a request to export the neural network from a current data layout to a subsequent data layout;” Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“executing the transparent mapping of data layouts of the pre-evaluation part backwards;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
“receiving a request to export the neural network from a current data layout to a subsequent data layout;” Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amount to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 8
Step 2A Prong One
Claim 8 recites:
“performing the transparent mapping of data layouts of the pre-evaluation part;” Performing the transparent mapping of data layouts of the pre-evaluation part is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“storing an output of the pre-evaluation part in the neural network;” This limitation is merely a post-solution step of storing the data—a nominal addition to the claim that does not meaningfully limit the claim. The method storing is recited at a high level of generality. Simply implementing the abstract idea in a generic method is not a practical application of the abstract idea. Therefore, storing step is an insignificant extra-solution activity. See MPEP 2106.05(g).
“wherein the pre-evaluation part comprises a generative layer;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“storing an output of the pre-evaluation part in the neural network;”These elements amount to storing… information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; See MPEP 2106.05(d) (II)(iv). The courts have recognized the computer functions of storing as well‐understood, routine, and conventional function when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
“wherein the pre-evaluation part comprises a generative layer;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 9
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein handling the transparent mapping of the data layouts by the wrapper comprises: generating a new neural network with a new parameter;” “performing the transparent mapping of data layouts of the pre-evaluation part using the parameter of the neural network as an input and the new parameter of the new neural network as an output;” “and replacing the neural network with the new neural network;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein handling the transparent mapping of the data layouts by the wrapper comprises: generating a new neural network with a new parameter;” “performing the transparent mapping of data layouts of the pre-evaluation part using the parameter of the neural network as an input and the new parameter of the new neural network as an output;” “and replacing the neural network with the new neural network;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive step.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 10
Step 2A Prong One
Claim 10 recites:
“detecting a data layout of the neural network;” Detecting a data layout of the neural network is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“detecting a data layout of a target device that will deploy the neural network;” Detecting a data layout of a target device that will deploy the neural network is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein handling the transparent mapping of the data layouts by the wrapper comprises: creating a new neural network with the data layout of the target device;” “and replacing the neural network with the new neural network;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein handling the transparent mapping of the data layouts by the wrapper comprises: creating a new neural network with the data layout of the target device;” “and replacing the neural network with the new neural network;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 11
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the wrapper detects the data layout of the neural network and detects the data layout of the target device that will deploy the neural network in response to a user execution of the neural network;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein the wrapper detects the data layout of the neural network and detects the data layout of the target device that will deploy the neural network in response to a user execution of the neural network;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 12
Step 2A Prong One
Claim 12 recites:
“detecting a data layout of the neural network;” Detecting a data layout of the neural network is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“detecting a data layout of a target device that will deploy the neural network;” Detecting a data layout of a target device that will deploy the neural network is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“performing the transparent mapping of data layouts of the pre-evaluation part;” Performing the transparent mapping of data layouts of the pre-evaluation part is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“replacing the neural network with a neural network that utilizes a data layout of the target device;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“replacing the neural network with a neural network that utilizes a data layout of the target device;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 13
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“further comprising removing, by the wrapper, a parameter of the neural network in response to a user input;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“further comprising removing, by the wrapper, a parameter of the neural network in response to a user input;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 14
Step 2A Prong 1:
Claim 14 recites:
“analyzing runtime data sources to identify parts of a computation graph of the neural network that depend on input data as a computation part, and parts of the computation graph that are independent of the input data as a pre-evaluation part;” Analyzing runtime data sources to identify parts of a computation graph of the neural network that depend on input data as a computation part, and parts of the computation graph that are independent of the input data as a pre-evaluation part is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“splitting the computation graph into the pre-evaluation part and the computation part;” Splitting the computation graph into the pre-evaluation part and the computation part is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“A system for optimizing computation graphs of a neural network comprising one or more hardware processors which, alone or in combination, are configured to provide for execution of the following steps;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
“generating and applying a wrapper that performs a transparent mapping of data layouts of the pre-evaluation part;” 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 as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“A system for optimizing computation graphs of a neural network comprising one or more hardware processors which, alone or in combination, are configured to provide for execution of the following steps;” Adding the words “apply it”