DETAILED ACTION
This action is in response to the claims filed 01/23/2024 for Application number 18/420,751. Claims 1-20 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/23/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
to indicate gradient information corresponding to one or more layers of one or more neural networks based, at least in part, on one or more indications of the one or more layers can be considered to be an evaluation in the human mind,
This limitation as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “a processor” and “one or more circuits.”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Additionally, the claim recites “one or more neural networks”. This additional element is merely generally linked to the judicial exception. Please see MPEP 2106.05(h). 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 further recites: to perform an application programming interface (API). This limitation is an insignificant extra-solution activity. 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 as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing a processor and one or more circuits to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional element of “one or more neural networks” is merely generally linked to the judicial exception. Furthermore, the limitation of to perform an application programming interface (API) is well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components, generally linking the additional element to the judicial exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the indications of the one or more layers include information identifying the one or more neural networks or information identifying the one or more layers. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein input to the API comprises the one or more indication of the one or more layers. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein performing the API is to cause one or more gradients of the one or more neural networks to be calculated. This claim recites additional mathematical steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the gradient information comprises one or more gradient values of one or more layers of the one or more neural networks. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein performing the API is to cause one or more back-propagation operations to be performed using the one or more layers. This claim recites additional mathematical steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the API is to be performed using one or more graphics processing units (GPUs). This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claims 8-14, they recite features similar to claims 1-7 and are rejected for at least the same reasons therein.
Regarding Claims 15-20, they recite features similar to claims 1-6 and are rejected for at least the same reasons therein.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yin et al. ("US 20220284232 A1", hereinafter "Yin").
Regarding claim 1, Yin teaches A processor (¶0113 “one or more processors”) comprising:
one or more circuits (¶0113, “one or more circuits”) to perform an application programming interface (API) (See ¶0676, “call to an application programming interface”) to indicate gradient information corresponding to one or more layers of one or more neural networks based, at least in part, on one or more indications of the one or more layers (“In at least one embodiment, inference and/or training logic 815 may include, without limitation, hardware logic in which computational resources are dedicated or otherwise exclusively used in conjunction with weight values or other information corresponding to one or more layers of neurons within a neural network… In at least one embodiment, inference and/or training logic 815 includes, without limitation, code and/or data storage 801 and code and/or data storage 805, which may be used to store code (e.g., graph code), weight values and/or other information, including bias values, gradient information, momentum values, and/or other parameter or hyperparameter information.” [¶0131]).
Regarding claim 2, Yin teaches The processor of claim 1, wherein the indications of the one or more layers include information identifying the one or more neural networks or information identifying the one or more layers. (“In at least one embodiment, inference and/or training logic 815 may include, without limitation, hardware logic in which computational resources are dedicated or otherwise exclusively used in conjunction with weight values or other information corresponding to one or more layers of neurons within a neural network.” [¶0131])
Regarding claim 3, Yin teaches The processor of claim 1, wherein input to the API comprises the one or more indication of the one or more layers. (“In at least one embodiment, machine learning models within model registry 3724 may uploaded, listed, modified, or deleted by developers or partners of a system interacting with an API.” [¶0533; models being listed, modified, or deleted implies an indication of the one or more layers of a neural network.])
Regarding claim 4, Yin teaches The processor of claim 1, wherein performing the API is to cause one or more gradients of the one or more neural networks to be calculated. (“In at least one embodiment, a client calculates a gradient, based at least in part on a model, for each image of one or more training images. In at least one embodiment, a client averages calculated gradients for images of training images to determine averaged gradients. In at least one embodiment, a system obtains averaged model gradients and a model from one or more clients and/or server systems.” [¶0115])
Regarding claim 5, Yin teaches The processor of claim 1, wherein the gradient information comprises one or more gradient values of one or more layers of the one or more neural networks. (“In at least one embodiment, averaged model gradients comprise one or more average gradient values, in which each value corresponds to a batch or set of training images.” [¶0115])
Regarding claim 6, Yin teaches The processor of claim 1, wherein performing the API is to cause one or more back-propagation operations to be performed using the one or more layers. (“In at least one embodiment, code and/or data storage 805 stores weight parameters and/or input/output data of each layer of a neural network trained or used in conjunction with one or more embodiments during backward propagation of input/output data and/or weight parameters during training and/or inferencing using aspects of one or more embodiments.” [¶0124])
Regarding claim 7, Yin teaches The processor of claim 1, wherein the API is to be performed using one or more graphics processing units (GPUs). (“In at least one embodiment, inference and/or training logic 815 illustrated in FIG. 8A may be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware or other hardware, such as field programmable gate arrays (“FPGAs”).” [¶0130])
Regarding claims 8-14, they are substantially similar to claims 1-7 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Regarding claims 15-20, they are substantially similar to claims 1-6 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kalamkar et al. ("US 20240070799 A1") discloses a method for transmitting data between multiple compute nodes and performing ML framework workflow by using API calls to enable automatic exchange activation data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM.
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/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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