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) 1-7.10-15 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 1-7, 10-15 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, 10 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, 10 is/are drawn to one of the statutory categories of invention.
Claims 1-7, 10-15 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 copy of the transmission neural network based on the transmission neural network information; receiving, from the transmitting communication device, a batch that includes P training, where P is a positive integer larger than 1;estimating, via a reception neural network for the end-to-end communications, P communication channels Hi to HP based on the P training symbols in the batch, respectively, where a communication channel Hp is a communication channel that a p-the training symbol in the batch experiences, where p = 1, 2, ..., P; determining P gradient values for the transmission neural network via the copy of the transmission neural network based on the P estimated communication channels, respectively; and ; and updating a weight of the transmission neural network based on the gradient, 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 the gradient back to the transmission device. 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 the gradient back to the transmission device. 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 2-6, 11-15 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) 1-7, 10-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lerchner (U.S. Patent App Pub 20200234468) in view of Hoydis (U.S. Patent Pub 20210192320).
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)
generating a copy of the transmission neural network based on the transmission neural network information; (See figures 5-6 and paragraphs 47-48, 51, Lerchner teaches copy of transmission generated)
receiving, from the transmitting communication device, a batch that includes P training, where P is a positive integer larger than 1; (See figures 5-6 and paragraphs 51, 54-55, Lerchner teaches parameterizing the batch)
estimating, via a reception neural network for the end-to-end communications, P communication channels Hi to HP based on the P training symbols in the batch, respectively, where a communication channel Hp is a communication channel that a p-the training symbol in the batch experiences, where p = 1, 2, ..., P; (See figures 5-6 and paragraphs 49, 53, Lerchner teaches multiple stages in the process)
Lerchner does not explicitly teach but Hoydis teaches determining P gradient values for the transmission neural network via the copy of the transmission neural network based on the P estimated communication channels, respectively; and (See figures 5-6 and paragraphs 49-51, Hoydis feeding into the neural network)
feeding gradient information related to the P gradient values back to the transmitting communication device. (See figures 5-6 and paragraphs 48, 56, Hoydis teaches iterative training loop feeding gradient info)
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 Hoydis with Lerchner because both deal with training neural networks. The advantage of incorporating the above limitation(s) of Hoydis into Lerchner is that Hoydis teaches the neural networks is jointly trained in order to optimize the end-to-end performance of the system, therefore making the overall system more robust and efficient. (See paragraphs [0003], [0012]-[0013], Hoydis)
Regarding claim 2,
Lerchner and Hoydis teach the method of claim 1.
Hoydis further teaches wherein the gradient information includes a gradient for the batch by averaging the P gradient values in the batch.(See paragraphs 59, 78, Hoydis) See motivation to combine for claim 1.
Regarding claim 3,
Lerchner and Hoydis 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 and Hoydis 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 and Hoydis 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 and Hoydis 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.
Regarding claim 10,
Learchner teaches a method performed by a transmitting communication device, the method comprising: transmitting, to a receiving communication device including a reception neural network for end-to-end communications, transmission neural network information including a configuration of a transmission neural network for the end-to-end; (See figures 5-6 and paragraphs 47-49, Lerchner teaches transmitting information)
transmitting, to the receiving communication device, a batch that P training symbols, where P is a positive integer larger than 1; (See figures 5-6 and paragraphs 51, 54-55, Lerchner teaches parameterizing the batch)
receiving, from the receiving communication device, a gradient for the batch that is an average of gradient values respectively related to the P training symbols in the batch; and (See figures 5-6 and paragraphs 49-51, Hoydis feeding into the neural network)
updating a weight of the transmission neural network based on the gradient. (See figures 5-6 and paragraphs 48, 56, 58 Hoydis teaches iterative training update weights)
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 Hoydis with Lerchner because both deal with training neural networks. The advantage of incorporating the above limitation(s) of Hoydis into Lerchner is that Hoydis teaches the neural networks is jointly trained in order to optimize the end-to-end performance of the system, therefore making the overall system more robust and efficient. (See paragraphs [0003], [0012]-[0013], Hoydis)
Regarding claim 11,
Lerchner and Hoydis teach the method of claim 10. Hoydis further teaches wherein updating the weight of the transmission neural network based on the gradient comprises updating a weight of a training part of the transmission neural network based on the gradient. (See figures 5-6 and paragraphs 48, 56, 58 Hoydis teaches iterative training update weights)See motivation to combine for claim 1.
Regarding claim 12,
Lerchner and Hoydis teach the method of claim 11, comprising: determining the training part of the transmission neural network; and transmitting information regarding the training part to the receiving device. (See figures 5-6 and paragraphs 129, 126-127, Lerchner teaches front end)
Regarding claim 13,
Lerchner and Hoydis teach the method of claim 12, wherein determining the training part of the transmission neural network comprises determining a front end of the transmission neural network as the training part. (See figures 5-6 and paragraphs 19-20, 138, Lerchner teaches front end)
Regarding claim 14,
Lerchner and Hoydis teach the method of claim 10, 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 15,
Lerchner and Hoydis teach the method of claim 10, 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)
Response to Arguments
Applicant's arguments filed 12/31/2025 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 gradient information related to the P gradient values back to the transmitting communication device. 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)). 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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NINOS DONABED whose telephone number is (571)272-8757. The examiner can normally be reached Monday - Friday 8:00pm - 4:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Follansbee can be reached on (571) 272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NINOS DONABED/Primary Examiner, Art Unit 2444