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 .
Response to Amendment
Applicant's submission filed on 18728340 has been entered. Claim(s) 1, 3-7 is/are pending in the application.
Claim Rejections - 35 USC § 101
1. 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, 3-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1 is/are drawn to method (i.e., a process), claim(s) 7 is/are drawn to a system (i.e., a machine/manufacture). As such, claims 1, 7 is/are drawn to one of the statutory categories of invention.
Claims 1, 3-7 are directed to determining a gradient. Specifically, the claims recite receiving, from a transmitting communication device, transmission neural network information including a configuration of a transmission neural network for end-to-end communications; generating a replica for the transmission neural network based on the transmission neural network information; receiving, from the transmitting communication device, a batch that includes P training symbols, where P is a positive integer larger than 1; determining, via a reception neural network for the end-to-end communications at the receiving communication device and the replica for the transmission neural network, P gradient values for the transmission neural network, based on the P training symbols, which is grouped within the Mathematical Concepts and is similar to the concept of (mathematical relationships OR mathematical formulas or equations OR mathematical calculations) OR Mental Processes and is similar to the concept of (concepts performed in the human mind (including an observation, evaluation, judgement, opinion) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as at least one transceiver; at least one processor; and at least one computer memory operably connected to the at least one processor merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the at least one transceiver; at least one processor; and at least one computer memory operably connected to the at least one processor perform(s) the steps or functions of feeding a gradient value for the batch back to the transmitting communication device, wherein the gradient value for the batch is an average of the P gradient values.. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a at least one transceiver; at least one processor; and at least one computer memory operably connected to the at least one processor to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of determining a gradient. As discussed above, taking the claim elements separately, the at least one transceiver; at least one processor; and at least one computer memory operably connected to the at least one processor perform(s) the steps or functions of feeding a gradient value for the batch back to the transmitting communication device, wherein the gradient value for the batch is an average of the P gradient values. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of determining a gradient. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Dependent claims 3-6 further describe the abstract idea of determining a gradient. The dependent claims do not include additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, the dependent claims are also not patent eligible.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Learchner (U.S. Patent App Pub 20200234468) in view of Wang (U.S. Patent App Pub 20210049451) in view of Yoo (U.S. Patent Pub 20210264255).
Regarding claim 1,
Lerchner teaches a method performed by a receiving communication device, the method comprising: receiving, from a transmitting communication device, transmission neural network information including a configuration of a transmission neural network for end-to-end communications (See figures 5-6 and paragraphs 47-49, Lerchner teaches receiving information)
Lerchner does not explicitly teach but Wang teaches generating a replica for the transmission neural network based on the transmission neural network information; (See fig 9-10, abstract, paragraphs 127-128, 177 Wang teaches generates the replica base don’t neural network formation configuration)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have known to combine the teachings of Wang with Lerchner because both deal with training neural networks. The advantage of incorporating the above limitation(s) of Wang into Lerchner is that Wang enables reducing data transmissions between the network entity and the user equipment, so that responsiveness of the user equipment can be improved, thus improving information recovery in the wireless communication system, therefore making the overall system more robust and efficient. (See paragraphs [1-4], Wang)
Lerchner and Wang do not explicitly but Yoo teaches receiving, from the transmitting communication device, a batch that includes P training symbols, where P is a positive integer larger than 1; (See figures 2-3, paragraphs 53-55, Yoo teaches processing multiple receives symbols/signals for training gradient computation)
determining, via a reception neural network for the end-to-end communications at the receiving communication device and the replica for the transmission neural network, P gradient values for the transmission neural network, based on the P training symbols; and (See abstract, figures 8-9, paragraphs 130, 11-13, Yoo teaches determining gradients associated with the transmission neural network)
feeding a gradient value for the batch back to the transmitting communication device, (See abstract, figures 8-9, paragraphs 130, 11-13, Yoo teaches feeding the gradient with the ref point gradient to the device) wherein the gradient value for the batch is an average of the P gradient values. (See abstract, figures 8-9, paragraphs 130, 7-8, 11, 126, Yoo teaches gradient batch processes)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have known to combine the teachings of Yoo with Lerchner and Wang because both deal with training neural networks. The advantage of incorporating the above limitation(s) of Yoo into Lerchner and Wang is that Yoo enables improving spectral efficiency, lowering costs, improving services, making use of new spectrum, and better integrating with other open standards using orthogonal frequency division multiplexing (OFDM) with a cyclic prefix on a downlink (DL), using CP-OFDM and/or SC-FDM on the uplink (UL), as well as supporting beamforming, multiple-input multiple-output (MIMO) antenna technology, and carrier aggregation, therefore making the overall system more robust and efficient. (See paragraphs [4-7], Yoo)
Regarding claim 3,
Lerchner, Wang and Yoo teach the method of claim 1, wherein the copy of the transmission neural network is a training part of the transmission neural network. (See figures 5-6 and paragraphs 111-114, Lerchner teaches determining a gradient function)
Regarding claim 4,
Lerchner, Wang and Yoo teach the method of claim 3, comprising receiving information regarding the training part of the transmission neural network from the transmitting device. (See figures 5-6 and paragraphs 17, 124, Lerchner teaches training)
Regarding claim 5,
Lerchner, Wang and Yoo teach the method of claim 1, wherein the transmission neural network information includes information regarding an initial state of the transmission neural network. (See figures 5-6 and paragraphs 19-20, Lerchner teaches initial state of transmission)
Regarding claim 6,
Lerchner, Wang and Yoo teach the method of claim 1, wherein the transmission neural network information includes information regarding generation of training symbols in the transmission neural network. (See figures 5-6 and paragraphs 113-115, Lerchner generating of training symbols)
Claim 7 list all the same elements of claim 1, but in system form rather than method form. Therefore, the supporting rationale of the rejection to claim 1 applies equally as well to claim 7.
Response to Arguments
Applicant's arguments filed 4/23/2026 have been fully considered but they are not persuasive.
A. Applicant argues that the claims are not directed to a judicial exception under Step 2A Prong One. As for Step 2A Prong One, of the Abstract idea is directed towards the abstract idea of determining a gradient which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (fundamental economic principles or practices including hedging insurance, mitigating risk) OR (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) OR (managing personal behavior or relationships or interactions between people including social activities teaching, and following rules or instructions) OR Mental Processes and is similar to the concept of (concepts performed in the human mind (including an observation, evaluation, judgement, opinion) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
B. Applicant argues that the claims are not directed to a judicial exception under Step 2A Prong Two. As for Step 2A Prong Two, the claim limitations do not include additional elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, and the claim is not more than a drafting effort designed to monopolize the judicial exception and the claim limitation simply describe the abstract idea. The limitation directed to determining a gradient does not add technical improvement to the abstract idea. The recitations to “transceiver”, processor, memory perform(s) the steps or functions of feeding a gradient value for the batch back to the transmitting communication device, wherein the gradient value for the batch is an average of the P gradient values. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
C. Applicant argues that the claims are not directed to a judicial exception under Step 2B.
As for Step 2B, The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the limitation directed to determining a gradient does not add significantly more to the abstract idea. Furthermore, using well-known computer functions to execute an abstract idea does not constitute significantly more. The recitations to “transceiver”, processor, memory are generically recited computer structure. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of determining a gradient. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)).
Further this is not an unconventional arraignment because it applies standard neutral network training operations like gradient computation and batch averaging and model replication using generic transmitters and receivers. This is a routing machine learning workflow setup.Therefore, the claim is not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and located in the PTO-892 form.
1.Mesmakhorshahi, U.S. Patent App 20220366236, teaches embodiments of the present disclosure include systems and methods for reducing operations for training neural networks. A plurality of training data selected from a training data set is used as a plurality of inputs for training a neural network. The neural network includes a plurality of weights. A plurality of loss values are determined based on outputs generated by the neural network and expected output data of the plurality of training data. A subset of the plurality of loss values are determined. An average loss value is determined based on the subset of the plurality of loss values. A set of gradients is calculated based on the average loss value and the plurality of weights in the neural network. The plurality of weights in the neural network are adjusted based on the set of gradients.
2. Chandak, U.S. Patent App 20230253074, teaches a method and a system for decoding MPEG-G encoded data of genomic information, including: receiving MPEG-G encoded data; extracting encoding parameters; selecting an arithmetic decoding type based upon the extracted encoding parameters; selecting a predictor type specifying the method to obtain probabilities of symbols which were used for arithmetically encoding the data, based upon the extracted encoding parameters; selecting arithmetic coding contexts based upon the extracted encoding parameters; and decoding the encoded data using the selected predictor and the selected arithmetic coding contexts.
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/NINOS DONABED/Primary Examiner, Art Unit 2444
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